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Mesoscience-based virtual process engineering
Accepted Manuscript
Mesoscience-based virtual process engineering Wei Ge , Li Guo , Xinhua Liu , Fanyong Meng , Ji Xu , Wen Lai Huang , Jinghai Li PII: DOI: Reference:
S0098-1354(18)31216-X https://doi.org/10.1016/j.compchemeng.2019.03.042 CACE 6403
To appear in:
Computers and Chemical Engineering
Received date: Revised date: Accepted date:
20 November 2018 29 March 2019 31 March 2019
Please cite this article as: Wei Ge , Li Guo , Xinhua Liu , Fanyong Meng , Ji Xu , Wen Lai Huang , Jinghai Li , Mesoscience-based virtual process engineering, Computers and Chemical Engineering (2019), doi: https://doi.org/10.1016/j.compchemeng.2019.03.042
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Highlights
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Addressing the bottleneck for chemical engineering at different levels. Describing the development, extension, and application of the EMMS model. Describing the mesoscale modeling based on the EMMS principle. Describing the EMMS paradigm towards virtual process engineering. Proposing the new PSE paradigm based on mesoscience.
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Mesoscience-based virtual process engineering Wei Ge1,2,*, Li Guo1,2, Xinhua Liu1, Fanyong Meng1, Ji Xu1, Wen Lai Huang1, Jinghai Li1,2,* 1
State Key Laboratory of Multiphase Complex Systems (MPCS), Institute of Process Engineering (IPE), Chinese Academy of Sciences (CAS), Beijing 100190, China School of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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2
* Corresponding authors.
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Fax: 86-10-62558065
E-mail addresses: [email protected] (Wei Ge), [email protected] (Jinghai Li) Postal address:
State Key Laboratory of Multiphase Complex Systems
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Institute of Process Engineering
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Chinese Academy of Sciences Zhong Guan Cun, Haidian District
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P.O. Box 353, Beijing 100190
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People’s Republic of China
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Declarations of interest: None Abstract
Accounting for complex mesoscale structures was found to be the key to predicting system performance from elemental properties, and hence a bottleneck for process systems engineering. The development and generalization of the energy-minimization multiscale (EMMS) model may present a 2
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continuous attempt to provide this key link, where mesoscale structures are characterized by analyzing the compromise in competition of the dominant mechanisms in the systems studied, and then an accurate and efficient simulation paradigm is established. This paradigm enables the integration of high-fidelity realtime simulation with virtual reality technologies to create a physically
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realistic digital counterpart of the industrial processes, that is, virtual process engineering (VPE). VPE may present a new research and development platform for process systems engineering. In long term, the seamless integration of physical and virtual experiments, either in situ or remotely, is also possible
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with VPE.
Keywords: Coarse-graining; Mesoscale; Mesoscience; Stability condition; Virtual process engineering; Virtual reality
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1. Challenges to chemical engineering
The chemical industry can be described as a producer of materials and energies for other
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industries and society as a whole, using natural resources as feedstocks. In this sense, it is understandable that the principles of chemical engineering are generally applicable to a wider range
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of industries with similar missions, which are collectively called process industries (Li, 2000). In China,
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for example, these industries account for nearly 1/6 the GDP (Xiao et al., 2004), which include not only traditional “heavy” industries (such as power, mining, metallurgy, and material industries) but
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also fast-developing “light” industries (such as food, biological, cosmetic, and pharmaceutical industries) and are closely related to emerging fields such as nanotechnology, information technology, aerospace engineering, and ocean engineering. In addition, other major industries such as mechanical, construction, textile, electrical, electronic, and civil engineering industries consume large amounts of materials and energy generated by these process industries. The chemical and process industries are 3
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thus still of fundamental importance despite the fact that modern society is relying more and more on information industries and service sectors. At least 5 of the 17 Sustainable Development Goals (SDGs) of the United Nations (United Nations, 2018) are directly related to process industries, such as inclusive economic growth and combating climate changes. The call for reinforcing manufacturing in
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the United States (Energy.gov, 2012) and advocating for Industry 4.0 in Germany (Heng, 2014) also acknowledge the importance of greener and more intelligent process industries.
Nevertheless, this importance also implies many challenges which are largely related to the
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significant multilevel and multiscale nature of the systems faced in process engineering, as illustrated in Fig. 1. The studies on different levels may belong to different disciplines (Grossmann and Westerberg, 2000), such as chemistry and material science for the material level, chemical and process engineering for the reactor level, and process systems engineering (PSE) for the factory level.
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Each of them have to address multiscale problems involving the element scale, the system scale, and
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the mesoscale in between which is typically most complex and least understood (Li and Huang, 2014). Self-assembly, particle clustering, and process synthesis superstructure (Floudas et al., 1986; Yee et al.,
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1990) are good examples on the material, reactor, and factory levels, respectively. The lack of matured theories and research methods for the mesoscales (Coppens, 2005; Ottino, 2011; Li, 2015a;
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Li, 2016; Li et al., 2016; Li et al., 2018a) is a major cause of the long-lasting bottleneck of process and
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equipment development. So far, stepwise experimental scaling-up is still the mainstream approach. In general, it’s very costly, time-consuming and not effective enough yet (Duduković and Mills, 2015). (Here insert Fig. 1.)
Nowadays, with more and more strict requirements on sustainable development, more precise design and more extensive and comprehensive optimization become even more critical for process 4
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industries (Floudas at al., 2016). On the other hand, to survive this more competitive market, the research and development (R&D) activities must respond to its changes swiftly and the demand for mesoscale theories and methods is hence more urgent. In this article, a possible avenue forward to meet this demand is discussed, which features the analysis of the dominant mechanisms in complex
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systems and their compromise that determines the stability conditions of the systems. The multiscale models thus established are effective for the manipulation and optimization of the system behaviors for engineering purpose and, in particular, they can provide much higher simulation capabilities featuring virtual process engineering (VPE) (Ge et al., 2011; Li, 2015a; Ge et al., 2015), that is,
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interactive (quasi-) realtime simulation with virtual-reality (VR) style user interface and cloud-based services, which will revolutionize the traditional R&D mode in chemical engineering. 2. Current status of simulations in process engineering
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To understand the importance of mesoscale studies for computer simulation in chemical
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engineering, it must be understood that the simulation performance is determined by the interplay among models, numerical methods or algorithms, and computer software and hardware, which is
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difficult to be assessed by any single criterion. However, some general features of the overall status
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can still be described based on the vast amount of literatures: Reliable physical principles, well-posed numerical methods, and precisely defined simulation
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conditions can produce sufficiently accurate results, but only for very simple systems and at small scales from an engineering viewpoint. For example, the computational cost of direct numerical simulation (DNS) based rigorously on the Navier–Stokes (NS) equation scales with Re9/4, where Re is the Reynolds number (Re) (Moin and Mahesh, 1998) and the required DNS resolution scales with LRe-3/4, where L is the characteristic scale of the flow. Thus, even for the fastest 5
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supercomputer currently available (The Oak Ridge Leadership Computing Facility, 2018), it is not realistic to conduct a DNS for typical industrial reactors with Re in the range of 106, let alone that for most practical problems, reactions and heat & mass transfer should be considered as well. For multiphase systems, due to the discontinuities and/or complexities at the phase interfaces, the
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scales are even more limited (Luo et al., 2016; Schneiders et al., 2017; Bogner et al., 2018). Engineering simulation approaches at large scales are available but not satisfactory, mainly due to their physical models: Methods based on averaged descriptions, such as traditional
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continuum-based methods (Lun et al., 1984; Jop et al., 2006; Goldhirsch 2010; Zhu et al., 2011) or traditional coarse-grained discrete methods (Sakai and Koshizuka, 2009; Sakai et al., 2012; 2014; Mokhtar et al., 2012; Hilton and Cleary, 2014; Lu et al., 2016c) for multiphase flows, can be much faster than first-principles approaches and can be applied at engineering scales with reasonable
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computational cost. However, the traditional averaging laws are, in general, only feasible for
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homogeneous and linearly non-uniform systems. Because of the significant dynamic multiscale nature of the engineering systems, especially at mesoscales (Li, 2015a), they usually suffer from
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low accuracy or even poor validity. For example, the two-fluid model (TFM) for gas-solid flow with drag laws based on uniform suspension results in unreasonable prediction of the overall slip
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velocity between the two phases (Li and Kwauk, 1994; Yang et al., 2003a; Lu et al., 2013).
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Multiscale methods are promising, but their computational cost for engineering simulation is still very demanding. The essence of multiscale methods lies in explicitly taking into account the effect of mesoscale structures, which is different from simple averaging of microscale behaviors to macroscale properties directly. Multiscale methods become a must when no clear scale separation presents in the system, as in the case of gas-solid flow (Li and Kwauk, 1994; van der 6
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Hoef et al., 2008), or the first-principles approach is too expensive, as in the case of turbulence. However, developing reliable mesoscale models has been difficult, regressed correlations from experiments are usually not general enough (Li and Kwauk, 2003; Li et al., 2016) whereas theoretical models are even less developed due to limited understanding of the mechanisms
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involved in the mesoscale behavior (Li and Huang, 2018; Sundaresan et al., 2018). In fact, even when the mesoscale behaviors, rather than the macroscale behaviors in engineering systems, are of interest for the exploration of the mesoscale models, the first-principles simulation cost involved is typically beyond attainment yet (Ge et al., 2011). Another category of multiscale
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methods, namely concurrent multiscale modeling (Miller and Tadmor, 2009) with spatial and/or temporal coupling of simulation methods at different scales, may be easier to implement, but their computational cost are also very demanding. Meanwhile, the coupling schemes, such as
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those between molecular dynamics simulations and continuum methods (Connell and Thompson, 1995; Mohamed and Mohamad, 2009), are not theoretically settled yet (Wijesinghe and
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Hadjiconstantinou, 2004).
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Supercomputing is important for high speed, but software scalability is not favorable yet. As shown in Fig. 2, supercomputing performance increased from 280.6 Tflops in 2006 to 143.5 Pflops
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in November 2018, as measured by the Linpack benchmark for dense matrix operations,
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representing growth of 511.4 times. This increase is even much faster than Moore’s law, which predicts doubled performance every 18 months. However, the development for real applications is not encouraging, as indicated by the HPCG (High Performance Conjugate Gradients, a new metric for ranking HPC systems (Dongarra and Heroux, 2013)) performance in Fig. 2, which is more practical but significantly below that of Linpack and has shown much less impressive increase, and 7
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sometimes even decrease. The main reason behind this discouraging fact is that present supercomputing relies heavily on larger-scale parallelization for higher total performance, whereas the clock frequency and linewidth for single processing units are approaching their physical limits. However, most applications, such as the computational fluid dynamics (CFD) codes
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widely used in chemical engineering, are inherently difficult to be used in parallel computing. In 2015, the largest-scale CFD simulation with 6.8 x 1011 grid cells ran on Tianhe-2, the fastest supercomputer then, reached only 5% of its peak performance even after extensive optimization (Wang et al., 2015). As in many other cases, nonlocal data and instruction dependence, which
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leads to frequent and long blocking of the parallel computing processes (or threads) and interruption of streamlines, is a fundamental reason behind such poor performance. On the other hand, for methods with good scalability, such as particle methods (Ge et al., 2017), a major cause
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of low efficiency is the presence of pronounced multiscale heterogeneity with complex dynamic behaviors, which brings about very thorny dynamic load balance problems that has not been well
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solved yet (Plimpton and Hendrickson, 1996; Koumoutsakos, 2005; Watanabe et al., 2013; Jebahi
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et al., 2014; Páll et al., 2014). As the fastest computers in the world currently have more than 2.4 million cores (The Oak Ridge Leadership Computing Facility, 2018), traditional numerical methods
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and algorithms with these characteristics have faced great challenges in using such
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supercomputers.
(Here insert Fig. 2.)
It can be inferred from the discussions above that the ubiquitous presence of mesoscale
structures is indeed a common and major origin of the challenges facing process simulation currently. In terms of modeling, it prevents the findings from first-principles simulations affordable at small 8
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scales to be applied directly at large scales of practical significance, results in the low accuracy of simple averaged models, and complicates the coupling of different models in multiscale modeling. In terms of computational methods and algorithms, mesoscale structures will typically introduce long-range correlations between the computational elements, hence more complex data dependence
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among them. On the other hand, heterogeneity is also most significant at mesoscales, which leads to serve load imbalance. Furthermore, these problems are, after all analysis, an expression of the inconsistency between the logic of the simulation models and the architectures of the software and computational hardware. In this sense, mesoscale structures should also be considered in the
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development of supercomputers. How to characterize, model and predict mesoscale structures is, therefore, of general importance to the development of process simulation. 3. Exploring mesoscale structures: From EMMS model to EMMS paradigm
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The importance of understanding mesoscale structures has come to notice for a long time (Li et
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al., 1988; Li and Kwauk, 1994; Li et al., 2013a). Some recent studies have attempted a systematic approach to improve the performance of process simulation in terms of accuracy, speed, and
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efficiency, and one of them originated from the energy-minimization multiscale (EMMS) model for
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gas-solid systems proposed some 30 years ago (Li et al., 1988).
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3.1. Mesoscale modeling with the EMMS model and principle Explicit quantification of mesoscale structures is challenging due to their dynamic nature and lack
of scale separation. For example, no generally applicable and agreed upon criterion has been proposed for the characteristic cluster size in gas-solid fluidization, and the partition of eddies of different regimes in turbulent flows is also semi-quantitative (Pope, 2001). This may explain why the 9
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traditional inductive approach of characterization-quantification-correlation (identifying a set of variables to describe the structures first, quantifying them in experimental measurements and/or computer simulations, and finally correlating the variables to establish a working model for engineering purpose), is not very successful for mesoscale structures.
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The EMMS model has followed a different approach. Rather than trying to elaborate the geometrical description of the mesoscale structures in gas-solid systems, that is, the gas bubbles and particle clusters, it introduced a hydrodynamic equivalence of the complex real structures and a
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stability condition to close the model with additional mesoscale variables. This stability condition is formulated as a compromise between the minimization of gravitational potential of the solid particles and the least energy dissipation rate of the gas flow through the bed layer. That is, the establishment of explicit new equations or correlations was avoided and replaced by variational constraints with
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more apparent and general physical meaning. As a good demonstration, the new drag law derived
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from this model has found wide applications for different gas-solid systems under various conditions (Yang et al, 2003a; 2003b; 2004; 2005; Wang and Li, 2007; Wang et al., 2008) with generally favorable
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results. It also shows a unique capability to predict the transition between different flow regimes,
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such as the choking point in fast fluidization (Li and Kwauk, 1994; Ge and Li, 2002; Wang et al., 2007). In the same approach, the EMMS model has been extended to various systems as summarized in
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Fig. 3. They include gas-liquid (Ge et al., 2007; Zhao and Ge, 2007; Yang et al., 2007; 2010; 2011), gas-liquid-solid (Liu et al., 2001) and single-phase turbulent flows (Li et al., 1999; Wang et al., 2016), and a general principle for establishing the stability conditions, which is central to this approach, has been proposed under the name of EMMS principle (Li and Kwauk, 2003). As shown in Fig. 4, competing mechanisms in a complex system may each be expressed as an extremal tendency and 10
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their spatiotemporal compromising leads to the stability condition that can be formulated as a multi-objective optimization (or variational) problem mathematically (Li and Kwauk, 2003). With this principle, more accurate and general mesoscale models can be expected in a wider range of systems. Granular flow (Ge et al., 2007; Zhou et al., 2008; 2010a; 2010b; 2013), emulsions (Ge et al., 2007), and
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reaction-diffusion processes (Huang and Li, 2016; Li et al., 2017; Huang et al., 2018b) are among the most promising candidates now. This further extension leads to the proposing of mesoscience (Li et al., 2013a; Li and Huang, 2014), which aims to explore the general theories and research
2016; Ocone, 2017; Li, 2017; Li et al., 2018a).
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methodologies in all systems with mesoscale structures (Li et al., 2013a; Li et al., 2016; Batterham,
(Here insert Figs. 3 and 4.)
It should be noted that most processes with multiscale structures are highly nonequilibrium,
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whereas well-established statistical/thermodynamic theories are valid for (quasi-)equilibrium systems
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so far. As stated previously (Li et al., 2018b), “after three decades of exploration, we reckon that the theory of dissipative structures (Prigogine, 1967) is really revolutionary, but it is a pity that it does not
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succeed completely in describing complex systems, partly due to the limitation in directly searching
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for a single-objective variational function”. The EMMS model may have provided a possibility to arrive at a unified theoretical description of these processes, that is, more than one extremal tendency may
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coexist in such systems where the stability criterion is finally defined by the compromise between these tendencies and are hence regime-dependent (Li et al., 2018a). For example, three regimes (Li, 2016; Li et al., 2016) may appear in systems with two dominant mechanisms, that is, two limiting regimes dominated by one mechanism only with simple structures, and the mesoregime in between presenting the compromise of the two dominant mechanisms with complex structures. That means 11
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mesoscience studies are not only on mesoscales, but also on mesoregimes (Li, 2017; Huang et al., 2018a), as exemplified by the reactor level in Fig. 5. (Here insert Fig. 5.)
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3.2. Multiscale computing with the EMMS Paradigm The EMMS principle discussed above has suggested an effective way to bridge variables and processes at different scales, and revealed the underlying logic and main structural features of complex systems, leading to a multiscale computing strategy for high accuracy, high speed, and ideal
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scalability. As illustrated in Fig. 6, it has two main features (Ge et al., 2011): (Here insert Fig. 6.)
Similarity among problem, model, software and hardware in logic and structure: As to
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complex systems, a common structural feature is that the system consists of many elements,
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and complex structures exist at the mesoscale between the element scale and the system scale. Such complex mesoscale structures are governed by the principle of compromise in
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competition, that is, the stability conditions expressed as a multi-objective variational problem.
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Correspondingly, such a structural and logic feature should be realized in the model, software, and computer hardware. That is, interactions among elements are constrained by global
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stability conditions at the system scale, and interactions among mesoscale cells are correlated by local stability conditions at mesoscales, giving the principle to optimize the intensity distribution of data and communication in computation (Li et al., 1988). That means, the multiscale structural features of the simulated systems should also be reflected in the physical models, numerical methods and algorithms, and computer hardware. The logical consistency 12
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provides a rational decomposition of the problem to minimize the physical dependence and hence communication throughput between different parts of the numerical model. Meanwhile, the structural consistency maps software workload to hardware with best load balance. They are both essential to guarantee the speed, scalability, and efficiency of
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simulation. Computation strategy of “first global, then local, and finally detailed”: The above-mentioned structural and logic features suggest that computation can start from the global distribution,
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then followed by the determination of the structure in a cell (Liu et al., 2012). Taking such a structure as the initial condition, the detailed structural evolution can be tracked finally at the element scale (Ge et al., 2011; Lu et al., 2016b). This strategy lifts the long-lasting scalability barriers for the simulation of industrial processes, where a major and common obstacle has
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been the increasing complexity of the data and operational dependence among different parts
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of the computational domain at larger scales, which leads to increased waiting and latency. In this strategy, such dependence is layered at different scales and decoupled among them. On
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the other hand, the dependence within each layer is also reduced by applying the stability constraints. As a result, the scalability and efficiency of the computation can be improved
EMMS
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The
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significantly.
Paradigm
also
suggests
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general
multiscale
architecture
for
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application-oriented supercomputing, which is illustrated in Fig. 7 (Ge et al., 2015) using the simulation of particle-fluid flow as a sample implementation: In the top layer, the simulation starts with the steady-state solution of the axial and radial distributions using a simple macroscale model constrained by the global stability conditions 13
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(Nst)system min (Liu et al., 2012; Hu et al., 2013; Liu et al., 2015), where, according to the EMMS model, Nst is the mass-specific energy consumption for suspending and transporting the particles. With these distributions serving as the initial condition, the time for dynamic evolution of the system at microscale and mesoscale can be shortened greatly with sufficient accuracy (Liu et al.,
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2011). It requires the general operations on networks and the global linear algebra operations to matrices (Ge et al., 2015). These operations are typically sparse and favorably performed by general-purpose multicore processors such as CPUs operating in the multiple instructions multiple
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data (MIMD) mode.
In the middle layer, to describe the evolution of the mesoscale structures, continuum-based methods are coupled with discrete particle methods either connected at interfaces or occupying the same space. The local stability conditions, (Nst)local min, also provide effective constraints to
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facilitate the coupling (Lu et al., 2014), which can accelerate the computation further by using
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larger cell size and time step. It is most important when continuum variables are mapped to discrete quantities with more degrees of freedom, otherwise no unique mapping law can be
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established and the empirical mapping law may result in inaccuracy, instability and inefficiency (Chen et al., 2016; Chen and Wang, 2017;). In this case, the major numerical operations include
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interaction detection, processing, particle motion, and status updating, which are mainly local and
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additive operations, and are preferably performed by many-core processors operating in the MIMD mode. No ideal processor has been found for such operations. Intel MIC processors (2013 Intel; 2016) can temporarily serve as a close replacement for this category.
In the bottom layer even simpler models can be applied for the detailed evolution of the systems, such as the discrete element method (Cundall and Strack, 1979), lattice Boltzmann method 14
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(McNamara and Zanetti, 1988), or lattice gas automaton (Frisch et al., 1986), which are performed by many-core processors in the SIMD mode. In fact, many stencil-based explicit PDE solvers or particle methods based on fixed neighbor lists are also suitable for this mode (Ge et al., 2017).
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(Here insert Fig. 7.) The improvement in simulation accuracy, speed and efficiency brought by the EMMS model and then the EMMS paradigm has not only facilitated fundamental research (Ren et al., 2009; Ren et al., 2012; Zhou et al., 2014; Xu et al., 2015; Hou et al., 2016) and process development (Lu et al., 2007; Li
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et al., 2013b; Ren et al., 2013; Qin et al., 2016; Lu et al., 2017; Zhou et al., 2018), but also suggested a new simulation mode in recent years. As shown in Fig. 8, 2D quasi-realtime interactive simulation of a pilot-scale CFB riser can now be achieved with integrated visualization functions and a user interface (Ge et al., 2015). The operator can adjust, for example, the gas velocity at the inlet, and the
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corresponding change in the flow field is observed on the screen almost instantly, as the simulation is
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only 50 times slower than realtime (Ge et al., 2017). This is remarkably different from traditional “offline” simulations where one has to wait days for seconds of evolution. This is indeed a
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more details.
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demonstration of VPE (Ge et al., 2011; Liu et al., 2012), as discussed in the following section with
(Here insert Fig. 8.)
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4. Main features of virtual process engineering (VPE) As a significant upgrading of traditional simulation and experiment, VPE is defined by the
replacement of real equipment and related processes (such as structural deformation, energy and material transport, and chemical reactions) with apparently identical digital counterparts for R&D 15
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purpose. With the following main features, it may bring about new possibilities to chemical and process engineering: High accuracy, speed and efficiency in simulation: VPE requires high computational speed for real time or quasi-realtime simulation, and it is not at the cost of accuracy and efficiency. Such
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comprehensive high standards can be met by the multiscale computing paradigm described in Sect. 3, and its implications are profound. First, it enables virtual operation of the simulated systems in an interactive mode, so that not only the normal steady-state in industrial productions, but also the
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dynamic behaviors of the systems in start-up, shut-down and abnormal operations can be investigated safely. It also means the test of new designing ideas will not be a chain of long waits and the brain-storm style group discussion facilitated by VPE will be new norms in process engineering. Second, simulation for very long physical time, hours to days instead of seconds to minutes as typical
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for today, will be attainable with reasonable computing time, for example, several weeks. That means,
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slow processes such as the deactivation of catalyst, attrition and wearing of the particles, and residence time distribution of the reactants in reactors can be computed directly without a
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predefined kernel model as in population balance modeling. Third, high computational speed and scalability may enable concurrent simulation of multiple equipment or even systems from
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element-scales rather than unit-scales as practiced in process systems engineering at present. In this
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way, physically realistic virtual factories can be established without empirical and phenomenological equipment models in the so-called flow-sheet simulations. Fourth, concurrent simulation of a series of similar cases with a large number of different structural or operational parameters or material properties will be affordable also, facilitating comprehensive understanding and extensive
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optimization of the system studied. In particular, it may provide the huge amount of data needed for big data analysis, data mining and the training of artificial intelligence applications. Quantitatively realistic VR: According to the Virtual Reality Society (VR Society, 2018), virtual reality is defined as a three-dimensional, computer generated environment which can be explored
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and interacted with. User is able to immerse into this virtual world and manipulate objects there or perform a series of actions. From the point of view of VR, VPE also represents a significant extension of its technological basis, that is, physical VR (PVR). While classical VR focuses on visual or
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phenomenological fidelity of qualitative nature, PVR means the produced scenes are physically accurate also. This is only possible with multiscale simulation and supercomputing. In addition to the common advantages of VR for training and educational purpose, such as immersed environment and interactivity, PVR also enables virtual (and non-intrusive) measurement by traditional data analysis,
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pattern recognition and data mining. Concentration profiles, geometrical characteristics of the
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heterogeneous structures, and macro statistical information such as throughputs and inventories, can be obtained in PVR. With these capabilities, VPE provides revolutionary virtual labs and offices for
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chemical engineers, which can catalyze new research and design ideas and improve working efficiency greatly. A major technological obstacle of PVR is, however, the in-situ integration of the computation
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and post-processing including virtualization and data analysis. This is because that, if the traditional
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way of offline post-processing is still used, the huge data generated by the high-resolution, high-speed computation would be unaffordable for any communication and storage technologies, and at least become the main bottleneck for the overall performance. The EMMS Paradigm may also suggest a new approach to tackle this difficulty, as will be discussed in Sect. 5.
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Seamless coupling between accurate simulation and precise experiment: Traditionally, simulations and experiments have been compared quantitatively at two extreme ends, either for very simple cases (such as flow past a single particle) or for very simple signals (such as the overall flow rate in a reactor) because of the limited resolution and accuracy of both sides. It can serve as a good
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validation of the model when the two ends agree well. However, when they disagree, experiments are not very helpful in determining the error in the model or numerical method because they cannot provide the same level of details as simulations. This difference can be compensated for in VPE using precisely designed elemental processes. Both the experimental and simulation conditions and
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parameters can be controlled and adjusted with sufficient precision. Then, with real time simulation and data processing for both virtual and real experiments, the behavior of the models can be compared online with the experiments strictly, systematically and comprehensively, providing a
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convenient and efficient approach for validating and improving simulation models and methods. It can significantly expedite the development of physical models and numerical methods. On the other
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hand, these capabilities also enable the integration of physical and virtual parts, and both can be
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situated locally or remotely, into a single R&D prototype. Such hybrid systems can eventually be cloud-based, which provide significantly better fidelity and flexibility than those provided by either
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experiments or simulations, and hence facilitate faster and more economic and efficient R&D
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activities. It is worthy of mention that, for some hybrid systems, the performance of computation and measurements can also be mutually enhanced as exemplified in Sect. 5. In summary, with VPE, the interplay of fundamental research, technological development and
industrial implementation can be expedited significantly and direct scaling-up of chemical processes to industrial application will become possible. 18
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5. A roadmap for the development of VPE With the concept, strategy, and key technologies discussed above, a preliminary implementation of VPE is currently possible and has recently been explored at IPE. The introduction to these attempts below is aimed to provide a clearer picture of the future development of VPE, and to demonstrate the
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typical application scenarios of VPE. 5.1. Proof of concept (VPE1.0, established)
Based on the EMMS paradigm, VPE has been explored at IPE for years and its progresses were
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discussed by (Ge et al., 2011) and (Li et al., 2013a) previously, among which the first demonstration platform for VPE, referred to as VPE 1.0, was established by Liu et al. (2012). As illustrated in Fig. 9 (Liu et al., 2012), VPE 1.0 consists of an experiment and measurement subsystem, a control and data
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acquisition subsystem, and a high-performance simulation subsystem as well as a large graphical display. The experiment and measurement subsystem is a pilot-scale circulating fluidized bed (CFB)
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equipped with several measurement devices that provide realtime physical property data, e.g., an eight-channel optical-fiber analyzer for local solids concentration, an electric capacitance tomography
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for cross-sectional solids concentration distribution, a dual-plane X-ray scanner for solids circulation
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rate (Meng et al., 2011), and multiple pressure transducers along the CFB loop. Users can change operating parameters in realtime via an industrial personal computer (IPC) or by issuing commands
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from a control computer. The realtime measurement signals can be processed online and transmitted to the display array for comparing with pre-stored simulation data. The three-level heterogeneous CPU-GPU computing system, Mole-8.5 was developed at IPE to reflect the structural similarity of the physical model and the numerical algorithm, but the EMMS 19
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paradigm could not be fully implemented yet because general-purpose parallel simulation software needed to be developed further to reproduce detailed hydrodynamic evolution in realtime. To tackle this challenge at that time, the EMMS Paradigm was partially adopted in VPE 1.0: The macro-scale steady-state hydrodynamics was obtained first by using an EMMS-based general method for full-loop
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computation of the hydrodynamics in complex gas-solid reactors (Liu et al., 2015), which serves as the initial conditions for simulating the dynamic behaviors in the bed (Hu et al., 2013; Liu et al., 2015). The dynamic simulation in a TFM used in the commercial software, Fluent, was improved by using the EMMS drag through user-defined functions (Yang et al., 2003a; Wang and Li, 2007; Wang et al., 2008).
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Using this strategy, the VPE 1.0 system takes about one week to simulate a one-minute physical process in the pilot-scale CFB system (Liu et al., 2012).
Although VPE1.0 is only a demonstration without the full capabilities of VPE described in Sect. 4,
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it can already provide some of them. For example, it can function as a comprehensive platform for the
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acquisition of basic hydrodynamic data and hence the training of new operating staff at much lower cost. This is an elementary but practically important application of VPE. Physical or digital simulators
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of real equipment or factories are widely used for training and educational purpose in process industries. Now with VPE they can be established more efficiently and also more realistic and flexible
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to explore the performance characteristics and boundaries of the target equipment or factories
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exhaustively. For some occasions that are too risky and costly to produce physically, such as severe accidents, the accuracy and predictability provided by VPE is critical as it may determine whether they are handled properly, otherwise major consequences are almost unavoidable. In the same sense, VPE is also a good tool for self-inspired learning, as new possibilities, either for the equipment or for the processes, can be explored with VPE without misleading and limitation. 20
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On the other hand, high-resolution accurate simulation and precisely designed experiments on elemental processes can now be integrated with VPE1.0. In particular, many experiments are presently limited to static, local and offline measurements due to the time consuming data processing and analysis. Now with supercomputing, it is quite promising to elevate the spatiotemporal resolution
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and scope to the realm of online measurement of industrial processes. A typical example is from the CT technology where ultrafast scanning in hardware has reached 8000 frames/s (Fischer et al., 2008) and the bottleneck for measuring dynamic process lies in realtime reconstruction of the images from original data, whose speed is traditionally in 1 frame/s range. With GPU-powered supercomputing (Xu
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and Mueller, 2007; Meng, 2009) the reconstruction time can be improved to 137 frames/s already (Bieberle et al., 2017). However, using more GPUs for higher performance is not effective currently, because ultra-fast scanning may require data transfer rate in the GByte/s range which becomes the
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limiting step. That means, even before realtime simulation is fully realized, both experiments and simulation can benefit very much if supercomputing can be improved significantly in memory access
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technology and more scalable data processing: the model behaviors can be investigated in a
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systematic and comprehensive manner, providing a convenient and efficient approach for validating
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and improving simulation models and methods. (Here insert Fig. 9.)
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5.2. Interactive multiscale simulation (VPE2.0, in progress) Based on the recent development of simulation software and upgrading of the supercomputing
system to Mole-8.5E with a complete and more compact three-level architecture and doubled performance, the successive VPE2.0 system was developed (Xu et al., 2017).
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As illustrated in Fig. 10, multiscale simulation methods are cascaded, starting with the global distribution method for the full-loop of the system (Liu et al., 2015; Hu et al., 2017), followed by an EMMS-based simplified TFM (Qiu et al., 2017) for the riser section of the system, an EMMS-based coarse-grained DPM, EMMS-DPM (Lu et al., 2014), for a cross-sectional slice of the riser, DNS of the
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detailed flow field in an element in the slice, and MD simulation of the reactions and diffusion inside the particles (Zhao, 2017). As illustrated in (Liu et al., 2012), at the top level, a few servers with only high-speed CPUs were used to conduct the global optimization with respect to the global stability condition. At the middle level, more servers with a balanced configuration of CPUs and GPUs or XPU
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(MIC) were introduced to compute the mesoscale continuum. At the bottom level, many servers with multiple GPUs were assigned to implement the microscale discrete simulation. In this mode, the dynamic evolution at a larger scale provides the initial and boundary information to the next smaller
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scale. As a result, the EMMS paradigm was fully implemented to some extent in VPE 2.0, so as to achieve higher possible accuracy, speed and efficiency for VPE. As indicated in Fig. 10, a recent test
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shows that it takes about 30 s to model the full-loop of an industrial-scale CFB system with a riser of
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8.5 m in height and 0.411 m in diameter. The simulation speed for the riser only, a slice of the riser, and a cell in the slice reached up to 200, 240 and 104 times slower than realtime, respectively, while
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the MD simulation of the reactions and diffusion inside the particles can be expected to proceed at
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the speed of 105 s/ns in VPE 2.0 (Xu et al., 2017). (Here insert Fig. 10.)
The multiscale multistage simulation style realized above has demonstrated a typical application
scenario of VPE in R&D. In principle, as explained in (Ge et al., 2017), the change of the operating conditions can also be signaled to the control subsystem and then enforced in the experimental 22
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system. In this manner, online comparison between simulation and experiments should be possible but in a “slow motion” mode, which restricts the rate of change. At the very least, this state-of-the-art system can provide a powerful virtual system for R&D purposes, inspiring new insights and design for engineering systems.
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For more detailed exploration of equipment performance, 3D full-loop simulations (Lu et al., 2016b) of pilot CFB systems (Liu et al., 2012) may demonstrate the potential of VPE better. As reported in (Ge et al., 2017), using 3 million coarse-grained particles representing nearly 50 billion real
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particles, the simulation can advance at 2.3 s per day using 60 CPU-GPU pairs (Intel Xeon E5-2680 and Nvidia Tesla K20). More recently, Xu et al. (2017) simulated the same system with 127 million coarse-grained particles at 1.2 s per day using 135 K80 GPUs and 270 cores of Intel Xeon E5-2680 CPUs (the Appendix), as shown in Fig. 11. Integration of reaction kinetics is also underway, based on
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the temporal speedup method of Lu et al. (2016a).
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(Here insert Fig. 11.)
With such VPE capability, as demonstrated in (Ge et al., 2017; Xu et al., 2016), it will be
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affordable to simulate hours or even days of the real processes within reasonable time. In traditional
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methods, locally or temporary steady states are usually assumed, so that the transient and long-term behaviors can be described almost independently and then combined with the former, providing rate
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parameters/models to the latter, for instance, through the kernels in PBM. However, in an industrial reactor, for example, a catalyst particle may follow a very complicated trajectory with significantly different flow and reaction conditions, and different particles may follow very different trajectories for varying resistance time. In this case, the transient behaviors are intrinsically coupled with the long-term behaviors, and the traditional methods for the change of particle sizes are not accurate 23
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enough. Now with VPE, direct simulation of the long-term behavior at both high spatiotemporal resolution and large scales (also for a large number of particles to ensure statistical accuracy) may become inevitable, as it is almost impossible for experimental measurement. This capability provided by VPE is, therefore, a substantial step forward for computer simulation to outperform experiments in
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a wider range of applications, and it can be used to understand slow processes such as carbon deposition, catalyst deactivation, particle wearing or attrition, etc., or to locate precisely the hot spot in a reactor.
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VPE at this stage can also facilitate the mutual improvement of experiments and simulation through their close interactions. For example, PIV is often employed to measure instantaneous singleor multiphase velocity fields. Because of the limitation of the frame frequency of CCD cameras as well as the data storage rate and capacity, traditional PIV measurements cannot capture the continuous
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evolution of mesoscale structures such as particle clusters in gas-solid systems. Computer simulations
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can then be introduced to interpolate the evolution of particle clusters between successively measured frames, using the experimental data as initial conditions. Higher temporal resolution for the
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experimental measurements and higher accuracy for the computer simulations would hence be achieved simultaneously, which would be much more difficult, if not impossible, to achieve using
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these two approaches independently. In the VPE mode, this integration can be routinely performed
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with great ease: In case of disagreement, it can also provide detailed information for the analysis of the causes and for further improvement of the simulation and/or experiment, until satisfactory results are obtained. Unlike the traditional interplay between simulation and experiment, this mode is far more effective and efficient for accelerating the research stage. 5.3. Cloud-based virtual factory on the horizon (VPE3.0, in perspective) 24
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Beyond VPE2.0, a general-purpose, sophisticated implementation of cloud-based virtual factory has been explored as well. Different definitions of virtual factory can be found (Bell and Fogler, 1996; 1997; 1998; Bell, 1997; 1999; Luo et al., 2012), even with different names, such as digital factory (Centobelli et al., 2016), virtual manufacturing or digital manufacturing (Mourtzis, 2015). Vaguely they
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seem to be referred to the same kind of things, all aiming at breakthroughs in the R&D and manufacturing mode, but actually no agreement has been reached so far. In fact, virtual factories should bear the following distinctive features, which may justify it as the identity of VPE3.0:
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(Here insert Fig. 12.)
Coupling of virtual and real systems without spatiotemporal limitation: The integration of physical and virtual systems will provide R&D prototypes with the best fidelity and flexibility significantly better than those provided by either experiments or simulation. For example, with real
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factories providing online operation data and real time simulation providing internal details, the
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reactors will become “transparent” to the operators. Virtual factories don’t have to be limited to the same physical space. It will be typical that
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different virtual or real parts are widely distributed at anywhere in the world with network
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connection. Thanks to the fast growing network technology, the “WiFi+5G+fiber transmission” solution can provide real time data acquisition remotely, meaning not only cloud-based virtual factory
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service but also cloud-operated real factories are possible, as illustrated in Fig. 12. Virtual factory can provide various services, including new process development, equipment design and optimization, modulation and optimization of operation conditions, education, pre-duty training, abnormal forecast, and so forth.
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Process safety may be a good example. Traditional abnormal monitoring relies on the comparison of on line and historical operation data. When an obvious deviation is identified alarms are triggered. In the virtual factory mode, once reliable simulation can run even faster than real time speed, it will become a prediction tool, which means the abnormal forecast can be realized via the
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online analysis of realtime simulation and online operation data. That means, alarms can be issued much earlier and more time will be available for precautions to avoid accidents.
Coupling between process engineering and PSE: Cloud-based VPE enables detailed simulation
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and digitalization of multiple equipment in a complete factory, so that the interpenetration or coupling of process engineering and PSE will become common practice. The virtual factories established in process PSE now may have provided some extent of reality but they are more focused on the global performance and visual effect using accumulated production data and simple
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correlations, whereas VPE is of quantitatively predictive nature. In fact, for computer simulation at
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the equipment level, such as CFD and particle dynamics simulations, the coupling between different reactors or equipment is not a substantial difficulty, as they are usually connected with pipe and
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valves, which can be described as typical boundary conditions already employed in these simulations, so that simulations of different parts are run in parallel. The real challenge is to maintain both high
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speed and high accuracy of the simulations and online coupling to realtime measurement data, so
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that the factories can be virtually operated and extensively optimized by evaluating different operation conditions of the different parts and their combinations. That is, the details and predictability provided by this level of the simulations should not be compromised with the speed and efficiency of PSE computations. This is only possible with cloud-based VPE and, on the other hand,
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virtual operation of individual equipment in VPE can also provide valuable data for PSE to develop or improve simpler equipment models and correlations, as practiced in traditional CFD. Application of big data analysis and artificial intelligence (AI): Virtual factories can be producers of big data also. The rapid development of accurate and easily accessible sensing technologies
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powered by the Internet of Things (IoT) can provide huge hybrid data with different spatial and temporal resolutions for data mining. High data quality is also ensured as the IoT-based sensors can acquire field data with minimum interference to the real processes.
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The integration of big data and AI has obtained encouraging success in e-commerce, finance, medical treatment. Similar examples can be found in chemistry already, for example, statistical potential functions optimized using neural network and Monte Carlo methods based on large numbers of quantum mechanics simulations of atoms have shown satisfactory accuracy. For process
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development, however, the main difficulty is the lack of enough and reliable data, either from
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simulation or experiment. Currently, industrial operation does monitor some basic parameters for each equipment, such as temperatures, pressures and flowrates at some critical points, which have
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accumulated to considerable amount and well archived in big enterprises, but these data are too
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limited to reflect the detailed dynamics of processes, and hence inadequate for mechanism-based models or correlations of general applicability. Traditional simulations, such as CFD, may produce
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much more data for this purpose, as attempted in data-driven CFD, but the amount and accumulation speed of the data are still insufficient for big data analysis and AI. Now with cloud-based VPE, reliable and efficient simulations can explore extensively the parameter space of many processes within reasonable time and cost, and huge amount of simulation data with enough resolution and accuracy will be available, together with the greatly expanded experimental and industrial data. This may 27
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justify the use of big data and AI methods in the search of practical models or correlations for the description of complex processes at a scale higher than that of the simulation models. Of course, it may also be used for the optimization of process and equipment design directly. To exploit the possibility of cloud-based VPE, a demonstration project was carried out, as Fig. 13
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shows. A pilot scale CFB system, originally built in the Swiss Federal Institute of Technology in Zurich (ETH) (Nicolai and Reh, 1995; Herbert and Nicolai, 1998), and rebuilt at IPE, CAS in 2002, has been upgraded into a remote experimental facility. The riser is 0.411 m in diameter and 12.43 m in height.
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This platform was upgraded with more than 80 sets of pressure, temperature, flow-rate and power measurement sensors as routine gauging. Besides that, optical fiber probe, mechanical probe, online solids flux scanner and portable X-ray CT were also in-house developed for the local and cross-sectional measurement. The obtained experimental data can be imported into an Oracle
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powered database for validation and refurbishing of CFB design and optimization.
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An ethernet-based distributed control system (DCS) and data acquisition system were developed to ensure the stable operation of the whole unit. The DCS consists of one central and four filed
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control stations, and two analog monitoring stations, two PID stations. The DCS can work under the
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client or server mode respectively, which means with the authorization of central control station, the whole unit can be operated remotely via ethernet or internet, and the routine measurement data
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acquisition and transmission can also be achieved remotely. The remote control of this platform just serves as a demonstration, and in theory, any remote facilities, both experimental and industrial ones, can be controlled remotely via the same technical route. With this facility as a prototype, the quasi-realtime simulation powered by HPC has been conducted, as described in Sect. 5.2. Both the
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simulation results and online measurement data are transmitted into the VPE control center, where the online virtual reality visualization is performed. (Here insert Fig. 13.)
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With these new possibilities, VPE can provide versatile services to the industries, at both R&D and production stages, and for either engineering, or academic and educational purposes. However, meeting the high standards for VPE in modeling, simulation, measurement, analysis and visualization
6. Integration of mesoscience and VPE to PSE
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call for new methods and technologies as discussed in the next section.
The discussions so far have focused on the reactor level, but in many cases the general principles they embodied are applicable to other levels. However, though all complex systems in Fig. 1 are
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shaped by the stability conditions resulted from the compromise in competition of dominant mechanisms (Li, 2015a; 2015b; Li et al., 2016; Li et al., 2018a; Li and Huang, 2018), the dominant
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mechanisms themselves are level-specific. For example, they may be related to the reaction and
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diffusion processes on the material level (Wang et al., 2013; Sun et al., 2016), and depending on the
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interactions between different phases, and the reactor types on the reactor level. On the factory level for PSE, economic and ecological factors may have to be incorporated
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(Bakshi, 2002; Grossmann, 2005; Triebl et al., 2013) in addition to physical and chemical constraints, which typically also introduce multi-objective optimization problems. For the first two levels, though these factors, such as low cost and low energy consumption, may also be considered as designing constraints, they do not determine the nature of the dynamic structures directly. For the factory level, however, the optimization is very often not limited to that level only, as explained in (Li et al., 2018b), 29
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they can first be conducted according to the specified conditions, and then the optimized results should be returned to the first two levels to check the possibility of realization, and the first two levels can offer feedback on new availability, as suggested in Fig. 14, so iterations and correlations between levels are necessary. These features specific to the factory level may further complicate the
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multi-objective optimization in PSE. In particular, when regime transitions are encountered in the optimization process and the jump changes are determined by variables on different levels, most traditional optimization methods will be challenged.
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(Here insert Fig. 14.)
Mesoscience can be integrated here to facilitate the PSE studies in that, by identifying the dominant mechanisms and establishing stability conditions in the problem rationally, a logical partition of the systems involved and their levels appears naturally. Otherwise, it will be very difficult
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to find the correct interdependence of the many variables in the problem and at least obscure the
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intrinsic ones. Similarly, mesoscience is critical for defining the mesoscale parameters (Li and Huang, 2018) and providing the pathways for inter-level parameter transfer. Anyway, the top-down transfer
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from the factory to the reactor and material levels is very difficult, especially when regime-specific
2018b).
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features have to be considered, which inevitably requires transdisciplinary collaborations (Li et al.,
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On the other hand, within each level, a concise and accurate model at the system scale is always
helpful to the modeling on the upper levels, and it should be most favorable to PSE which works on the top level. Mesoscience is a promising way to this end and VPE is the efficient simulation platform based on mesoscience. By these means, either simple global models (Hu et al., 2013; 2017; Liu et al., 2015; Zhang et al., 2016) or quasi-realtime digital counterparts can be established for the lower-level 30
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systems in PSE, which are treated as elements in the factory level simulations, analysis and optimization. As to solving the multi-objective optimization problems, a new route has been found recently (Zhang et al., 2018), validated by the two-objective optimization in the EMMS model. With these developments, more extensive optimization and more precise designing and control can be
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reached in PSE to address the most complicated problems in process engineering. 7. Conclusions & prospects
In this article, the critical challenges to modern chemical engineering are analyzed.
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Characterizing, modeling and elucidating mesoscale structures are identified as a central difficulty behind these challenges. The EMMS model was then revisited briefly and analyzed on how it can address this difficulty. The model also suggested a new multiscale architecture for supercomputing, the EMMS Paradigm, to keep the logical and structural consistency between the simulated systems
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and the simulation models, software and hardware. In this paradigm, high accuracy realtime
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simulation for industrial processes will be feasible and by integrating with virtual reality technologies, virtual process engineering can be realized as an updated form of computer simulation in chemical
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engineering. To summarize, the most important viewpoints and predictions are reiterated as follows:
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VPE, featuring physical virtual reality and seamless integration of virtual and real systems, is promising for addressing the big challenges of modern chemical and process industries, such as
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sustainable development and adapting to the new economic mode. It may bring about a revolution of chemical engineering and a wide range of engineering and scientific fields.
The EMMS Paradigm, with its emphasis on the logical and structural consistency between the simulation and the physical system plus the multiscale computing approach of “from global 31
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distribution to local and detailed evolution”, may provide a general and effective way to achieve physical virtual reality and implement VPE. Mesoscience, with the mission of characterizing, modeling, and elucidating the mesoscale structures on different levels, is behind almost all challenges of chemical engineering and hence of
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the technologies of VPE, which is critical not only for modeling and simulation but also for experiment and data analysis.
A cloud-based VPE service will soon be possible with interdisciplinary efforts, in the form of virtual
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factories, with which physical and virtual facilitates worldwide can be integrated as a unified R&D platform, improving the capability and efficiency of VPE.
To realize VPE is a promising mission in chemical engineering. It is, however, far beyond the
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current capability of the field. Transdisciplinary collaboration and the integration of global resources in academic, educational, industrial, and even social circles are needed. A paradigm
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shifting calls for not only breakthroughs in knowledge but also changes in research organization
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mode.
By addressing the factory-level complexities with both natural and social constraints (e.g.,
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providing more technical details), PSE is in urgent demand to incorporate with mesoscience and
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VPE to obtain accurate and effective models or descriptions on lower levels and to solve multi-objective optimization problems in a physically reasonable approach.
Acknowledgements The authors would like to thank all the members of the EMMS group for their contributions to the preparation of this article and for sharing some of their unpublished work, as cited in the text. The 32
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authors are grateful to the financial support received from the International Partnership Program of Chinese Academy of Sciences (Grant No. 122111KYSB20170068), the National Natural Science Foundation of China (Grants No. 91834303), and CAS (Grants Nos. QYZDJ-SSW-JSC029, XDA21030700, XDA21040400, and XXH13506-301).
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Appendix
The EMMS-DPM (Lu et al., 2016b; Lu et al., 2014) is adopted in the simulation. The computational cost of the particles is greatly reduced while maintaining the accuracy. The movement
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of CGPs is modeled as standard discrete element method (DEM) (Cundall and Strack, 1979):
dv CGP dw CGP Fb Fc Fgp Fdrag , I CGP T (1) dt dt where mCGP is the mass of all real particles in a CGP; vCGP is the CGP velocity; Fb, Fc, Fgp and Fdrag are the mCGP
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body force, the contact force, the pressure gradient force and the drag of the CGPs, respectively; ICGP
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wCGP and T are the moment of inertia, the angular velocity and torque respectively. The gas is modeled by the volume-averaged Navier–Stokes equations (Anderson and Jackson,
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1967) and solved by the continuum-based finite-volume method (FVM). The governing equations are: g g
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(2) ( g g ug ) 0 t ( g g ug ) (3) ( g g ug ug ) gpg (ug us ) ( g g ) g g g t where ρg, ug and εg are the density, velocity and voidage of the gas phase, respectively; us and β are the velocity of the CGPs and the drag coefficient between the gas and the particles, respectively; τg is the gas phase stress tensor; g is the gravity.
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The EMMS drag model (Wang and Li, 2007) is adopted to consider the heterogeneity effect on the gas-solid interactions. Please refer to our previous papers (Lu et al., 2016b; Lu et al., 2014; Xu et al., 2019) for more details of the models.
1. Table 1 Simulation parameters of the whole CFB loop (Xu et al., 2019). Variables Diameter, dp (μm) Coarse-graining ratio Density, ρp (kg/m3) Solid inventory Young’s modulus (Pa) Poisson’s ratio [-] Coefficient of restitution, e [-] Coefficient of friction [-] Coefficient of rolling friction [-] Time step, dtDEM (s) Coefficient of restitution, e [-] Coefficient of friction [-] Coefficient of rolling friction [-] Gas phase density (ρg) (kg/m3) Viscosity (μg) (kg/(m·s)) Flux at inlet of riser (m3/h) Grid number Time step, dtFVM (s) Operating pressure (Pa)
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Item Particle
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Additionally, the main conditions and parameters of the CFB simulation are summarized in Table
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Particle–wall
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Gas
Values 82 10 2450 90 kg 1.0×106 0.12 0.1 0.5 0.1 5×10-7 0.6 0.3 0.1 1.1795 1.8872×10-5 110 261,106 1×10-5 1.01×105
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Mesoscience-based virtual process engineering Wei Ge , Li Guo , Xinhua Liu , Fanyong Meng , Ji Xu , Wen Lai Huang , Jinghai Li PII: DOI: Reference:
S0098-1354(18)31216-X https://doi.org/10.1016/j.compchemeng.2019.03.042 CACE 6403
To appear in:
Computers and Chemical Engineering
Received date: Revised date: Accepted date:
20 November 2018 29 March 2019 31 March 2019
Please cite this article as: Wei Ge , Li Guo , Xinhua Liu , Fanyong Meng , Ji Xu , Wen Lai Huang , Jinghai Li , Mesoscience-based virtual process engineering, Computers and Chemical Engineering (2019), doi: https://doi.org/10.1016/j.compchemeng.2019.03.042
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Addressing the bottleneck for chemical engineering at different levels. Describing the development, extension, and application of the EMMS model. Describing the mesoscale modeling based on the EMMS principle. Describing the EMMS paradigm towards virtual process engineering. Proposing the new PSE paradigm based on mesoscience.
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Mesoscience-based virtual process engineering Wei Ge1,2,*, Li Guo1,2, Xinhua Liu1, Fanyong Meng1, Ji Xu1, Wen Lai Huang1, Jinghai Li1,2,* 1
State Key Laboratory of Multiphase Complex Systems (MPCS), Institute of Process Engineering (IPE), Chinese Academy of Sciences (CAS), Beijing 100190, China School of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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* Corresponding authors.
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Fax: 86-10-62558065
E-mail addresses: [email protected] (Wei Ge), [email protected] (Jinghai Li) Postal address:
State Key Laboratory of Multiphase Complex Systems
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Institute of Process Engineering
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Chinese Academy of Sciences Zhong Guan Cun, Haidian District
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P.O. Box 353, Beijing 100190
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People’s Republic of China
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Declarations of interest: None Abstract
Accounting for complex mesoscale structures was found to be the key to predicting system performance from elemental properties, and hence a bottleneck for process systems engineering. The development and generalization of the energy-minimization multiscale (EMMS) model may present a 2
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continuous attempt to provide this key link, where mesoscale structures are characterized by analyzing the compromise in competition of the dominant mechanisms in the systems studied, and then an accurate and efficient simulation paradigm is established. This paradigm enables the integration of high-fidelity realtime simulation with virtual reality technologies to create a physically
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realistic digital counterpart of the industrial processes, that is, virtual process engineering (VPE). VPE may present a new research and development platform for process systems engineering. In long term, the seamless integration of physical and virtual experiments, either in situ or remotely, is also possible
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with VPE.
Keywords: Coarse-graining; Mesoscale; Mesoscience; Stability condition; Virtual process engineering; Virtual reality
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1. Challenges to chemical engineering
The chemical industry can be described as a producer of materials and energies for other
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industries and society as a whole, using natural resources as feedstocks. In this sense, it is understandable that the principles of chemical engineering are generally applicable to a wider range
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of industries with similar missions, which are collectively called process industries (Li, 2000). In China,
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for example, these industries account for nearly 1/6 the GDP (Xiao et al., 2004), which include not only traditional “heavy” industries (such as power, mining, metallurgy, and material industries) but
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also fast-developing “light” industries (such as food, biological, cosmetic, and pharmaceutical industries) and are closely related to emerging fields such as nanotechnology, information technology, aerospace engineering, and ocean engineering. In addition, other major industries such as mechanical, construction, textile, electrical, electronic, and civil engineering industries consume large amounts of materials and energy generated by these process industries. The chemical and process industries are 3
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thus still of fundamental importance despite the fact that modern society is relying more and more on information industries and service sectors. At least 5 of the 17 Sustainable Development Goals (SDGs) of the United Nations (United Nations, 2018) are directly related to process industries, such as inclusive economic growth and combating climate changes. The call for reinforcing manufacturing in
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the United States (Energy.gov, 2012) and advocating for Industry 4.0 in Germany (Heng, 2014) also acknowledge the importance of greener and more intelligent process industries.
Nevertheless, this importance also implies many challenges which are largely related to the
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significant multilevel and multiscale nature of the systems faced in process engineering, as illustrated in Fig. 1. The studies on different levels may belong to different disciplines (Grossmann and Westerberg, 2000), such as chemistry and material science for the material level, chemical and process engineering for the reactor level, and process systems engineering (PSE) for the factory level.
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Each of them have to address multiscale problems involving the element scale, the system scale, and
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the mesoscale in between which is typically most complex and least understood (Li and Huang, 2014). Self-assembly, particle clustering, and process synthesis superstructure (Floudas et al., 1986; Yee et al.,
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1990) are good examples on the material, reactor, and factory levels, respectively. The lack of matured theories and research methods for the mesoscales (Coppens, 2005; Ottino, 2011; Li, 2015a;
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Li, 2016; Li et al., 2016; Li et al., 2018a) is a major cause of the long-lasting bottleneck of process and
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equipment development. So far, stepwise experimental scaling-up is still the mainstream approach. In general, it’s very costly, time-consuming and not effective enough yet (Duduković and Mills, 2015). (Here insert Fig. 1.)
Nowadays, with more and more strict requirements on sustainable development, more precise design and more extensive and comprehensive optimization become even more critical for process 4
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industries (Floudas at al., 2016). On the other hand, to survive this more competitive market, the research and development (R&D) activities must respond to its changes swiftly and the demand for mesoscale theories and methods is hence more urgent. In this article, a possible avenue forward to meet this demand is discussed, which features the analysis of the dominant mechanisms in complex
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systems and their compromise that determines the stability conditions of the systems. The multiscale models thus established are effective for the manipulation and optimization of the system behaviors for engineering purpose and, in particular, they can provide much higher simulation capabilities featuring virtual process engineering (VPE) (Ge et al., 2011; Li, 2015a; Ge et al., 2015), that is,
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interactive (quasi-) realtime simulation with virtual-reality (VR) style user interface and cloud-based services, which will revolutionize the traditional R&D mode in chemical engineering. 2. Current status of simulations in process engineering
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To understand the importance of mesoscale studies for computer simulation in chemical
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engineering, it must be understood that the simulation performance is determined by the interplay among models, numerical methods or algorithms, and computer software and hardware, which is
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difficult to be assessed by any single criterion. However, some general features of the overall status
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can still be described based on the vast amount of literatures: Reliable physical principles, well-posed numerical methods, and precisely defined simulation
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conditions can produce sufficiently accurate results, but only for very simple systems and at small scales from an engineering viewpoint. For example, the computational cost of direct numerical simulation (DNS) based rigorously on the Navier–Stokes (NS) equation scales with Re9/4, where Re is the Reynolds number (Re) (Moin and Mahesh, 1998) and the required DNS resolution scales with LRe-3/4, where L is the characteristic scale of the flow. Thus, even for the fastest 5
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supercomputer currently available (The Oak Ridge Leadership Computing Facility, 2018), it is not realistic to conduct a DNS for typical industrial reactors with Re in the range of 106, let alone that for most practical problems, reactions and heat & mass transfer should be considered as well. For multiphase systems, due to the discontinuities and/or complexities at the phase interfaces, the
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scales are even more limited (Luo et al., 2016; Schneiders et al., 2017; Bogner et al., 2018). Engineering simulation approaches at large scales are available but not satisfactory, mainly due to their physical models: Methods based on averaged descriptions, such as traditional
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continuum-based methods (Lun et al., 1984; Jop et al., 2006; Goldhirsch 2010; Zhu et al., 2011) or traditional coarse-grained discrete methods (Sakai and Koshizuka, 2009; Sakai et al., 2012; 2014; Mokhtar et al., 2012; Hilton and Cleary, 2014; Lu et al., 2016c) for multiphase flows, can be much faster than first-principles approaches and can be applied at engineering scales with reasonable
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computational cost. However, the traditional averaging laws are, in general, only feasible for
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homogeneous and linearly non-uniform systems. Because of the significant dynamic multiscale nature of the engineering systems, especially at mesoscales (Li, 2015a), they usually suffer from
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low accuracy or even poor validity. For example, the two-fluid model (TFM) for gas-solid flow with drag laws based on uniform suspension results in unreasonable prediction of the overall slip
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velocity between the two phases (Li and Kwauk, 1994; Yang et al., 2003a; Lu et al., 2013).
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Multiscale methods are promising, but their computational cost for engineering simulation is still very demanding. The essence of multiscale methods lies in explicitly taking into account the effect of mesoscale structures, which is different from simple averaging of microscale behaviors to macroscale properties directly. Multiscale methods become a must when no clear scale separation presents in the system, as in the case of gas-solid flow (Li and Kwauk, 1994; van der 6
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Hoef et al., 2008), or the first-principles approach is too expensive, as in the case of turbulence. However, developing reliable mesoscale models has been difficult, regressed correlations from experiments are usually not general enough (Li and Kwauk, 2003; Li et al., 2016) whereas theoretical models are even less developed due to limited understanding of the mechanisms
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involved in the mesoscale behavior (Li and Huang, 2018; Sundaresan et al., 2018). In fact, even when the mesoscale behaviors, rather than the macroscale behaviors in engineering systems, are of interest for the exploration of the mesoscale models, the first-principles simulation cost involved is typically beyond attainment yet (Ge et al., 2011). Another category of multiscale
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methods, namely concurrent multiscale modeling (Miller and Tadmor, 2009) with spatial and/or temporal coupling of simulation methods at different scales, may be easier to implement, but their computational cost are also very demanding. Meanwhile, the coupling schemes, such as
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those between molecular dynamics simulations and continuum methods (Connell and Thompson, 1995; Mohamed and Mohamad, 2009), are not theoretically settled yet (Wijesinghe and
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Hadjiconstantinou, 2004).
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Supercomputing is important for high speed, but software scalability is not favorable yet. As shown in Fig. 2, supercomputing performance increased from 280.6 Tflops in 2006 to 143.5 Pflops
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in November 2018, as measured by the Linpack benchmark for dense matrix operations,
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representing growth of 511.4 times. This increase is even much faster than Moore’s law, which predicts doubled performance every 18 months. However, the development for real applications is not encouraging, as indicated by the HPCG (High Performance Conjugate Gradients, a new metric for ranking HPC systems (Dongarra and Heroux, 2013)) performance in Fig. 2, which is more practical but significantly below that of Linpack and has shown much less impressive increase, and 7
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sometimes even decrease. The main reason behind this discouraging fact is that present supercomputing relies heavily on larger-scale parallelization for higher total performance, whereas the clock frequency and linewidth for single processing units are approaching their physical limits. However, most applications, such as the computational fluid dynamics (CFD) codes
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widely used in chemical engineering, are inherently difficult to be used in parallel computing. In 2015, the largest-scale CFD simulation with 6.8 x 1011 grid cells ran on Tianhe-2, the fastest supercomputer then, reached only 5% of its peak performance even after extensive optimization (Wang et al., 2015). As in many other cases, nonlocal data and instruction dependence, which
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leads to frequent and long blocking of the parallel computing processes (or threads) and interruption of streamlines, is a fundamental reason behind such poor performance. On the other hand, for methods with good scalability, such as particle methods (Ge et al., 2017), a major cause
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of low efficiency is the presence of pronounced multiscale heterogeneity with complex dynamic behaviors, which brings about very thorny dynamic load balance problems that has not been well
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solved yet (Plimpton and Hendrickson, 1996; Koumoutsakos, 2005; Watanabe et al., 2013; Jebahi
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et al., 2014; Páll et al., 2014). As the fastest computers in the world currently have more than 2.4 million cores (The Oak Ridge Leadership Computing Facility, 2018), traditional numerical methods
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and algorithms with these characteristics have faced great challenges in using such
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supercomputers.
(Here insert Fig. 2.)
It can be inferred from the discussions above that the ubiquitous presence of mesoscale
structures is indeed a common and major origin of the challenges facing process simulation currently. In terms of modeling, it prevents the findings from first-principles simulations affordable at small 8
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scales to be applied directly at large scales of practical significance, results in the low accuracy of simple averaged models, and complicates the coupling of different models in multiscale modeling. In terms of computational methods and algorithms, mesoscale structures will typically introduce long-range correlations between the computational elements, hence more complex data dependence
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among them. On the other hand, heterogeneity is also most significant at mesoscales, which leads to serve load imbalance. Furthermore, these problems are, after all analysis, an expression of the inconsistency between the logic of the simulation models and the architectures of the software and computational hardware. In this sense, mesoscale structures should also be considered in the
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development of supercomputers. How to characterize, model and predict mesoscale structures is, therefore, of general importance to the development of process simulation. 3. Exploring mesoscale structures: From EMMS model to EMMS paradigm
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The importance of understanding mesoscale structures has come to notice for a long time (Li et
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al., 1988; Li and Kwauk, 1994; Li et al., 2013a). Some recent studies have attempted a systematic approach to improve the performance of process simulation in terms of accuracy, speed, and
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efficiency, and one of them originated from the energy-minimization multiscale (EMMS) model for
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gas-solid systems proposed some 30 years ago (Li et al., 1988).
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3.1. Mesoscale modeling with the EMMS model and principle Explicit quantification of mesoscale structures is challenging due to their dynamic nature and lack
of scale separation. For example, no generally applicable and agreed upon criterion has been proposed for the characteristic cluster size in gas-solid fluidization, and the partition of eddies of different regimes in turbulent flows is also semi-quantitative (Pope, 2001). This may explain why the 9
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traditional inductive approach of characterization-quantification-correlation (identifying a set of variables to describe the structures first, quantifying them in experimental measurements and/or computer simulations, and finally correlating the variables to establish a working model for engineering purpose), is not very successful for mesoscale structures.
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The EMMS model has followed a different approach. Rather than trying to elaborate the geometrical description of the mesoscale structures in gas-solid systems, that is, the gas bubbles and particle clusters, it introduced a hydrodynamic equivalence of the complex real structures and a
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stability condition to close the model with additional mesoscale variables. This stability condition is formulated as a compromise between the minimization of gravitational potential of the solid particles and the least energy dissipation rate of the gas flow through the bed layer. That is, the establishment of explicit new equations or correlations was avoided and replaced by variational constraints with
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more apparent and general physical meaning. As a good demonstration, the new drag law derived
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from this model has found wide applications for different gas-solid systems under various conditions (Yang et al, 2003a; 2003b; 2004; 2005; Wang and Li, 2007; Wang et al., 2008) with generally favorable
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results. It also shows a unique capability to predict the transition between different flow regimes,
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such as the choking point in fast fluidization (Li and Kwauk, 1994; Ge and Li, 2002; Wang et al., 2007). In the same approach, the EMMS model has been extended to various systems as summarized in
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Fig. 3. They include gas-liquid (Ge et al., 2007; Zhao and Ge, 2007; Yang et al., 2007; 2010; 2011), gas-liquid-solid (Liu et al., 2001) and single-phase turbulent flows (Li et al., 1999; Wang et al., 2016), and a general principle for establishing the stability conditions, which is central to this approach, has been proposed under the name of EMMS principle (Li and Kwauk, 2003). As shown in Fig. 4, competing mechanisms in a complex system may each be expressed as an extremal tendency and 10
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their spatiotemporal compromising leads to the stability condition that can be formulated as a multi-objective optimization (or variational) problem mathematically (Li and Kwauk, 2003). With this principle, more accurate and general mesoscale models can be expected in a wider range of systems. Granular flow (Ge et al., 2007; Zhou et al., 2008; 2010a; 2010b; 2013), emulsions (Ge et al., 2007), and
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reaction-diffusion processes (Huang and Li, 2016; Li et al., 2017; Huang et al., 2018b) are among the most promising candidates now. This further extension leads to the proposing of mesoscience (Li et al., 2013a; Li and Huang, 2014), which aims to explore the general theories and research
2016; Ocone, 2017; Li, 2017; Li et al., 2018a).
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methodologies in all systems with mesoscale structures (Li et al., 2013a; Li et al., 2016; Batterham,
(Here insert Figs. 3 and 4.)
It should be noted that most processes with multiscale structures are highly nonequilibrium,
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whereas well-established statistical/thermodynamic theories are valid for (quasi-)equilibrium systems
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so far. As stated previously (Li et al., 2018b), “after three decades of exploration, we reckon that the theory of dissipative structures (Prigogine, 1967) is really revolutionary, but it is a pity that it does not
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succeed completely in describing complex systems, partly due to the limitation in directly searching
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for a single-objective variational function”. The EMMS model may have provided a possibility to arrive at a unified theoretical description of these processes, that is, more than one extremal tendency may
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coexist in such systems where the stability criterion is finally defined by the compromise between these tendencies and are hence regime-dependent (Li et al., 2018a). For example, three regimes (Li, 2016; Li et al., 2016) may appear in systems with two dominant mechanisms, that is, two limiting regimes dominated by one mechanism only with simple structures, and the mesoregime in between presenting the compromise of the two dominant mechanisms with complex structures. That means 11
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mesoscience studies are not only on mesoscales, but also on mesoregimes (Li, 2017; Huang et al., 2018a), as exemplified by the reactor level in Fig. 5. (Here insert Fig. 5.)
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3.2. Multiscale computing with the EMMS Paradigm The EMMS principle discussed above has suggested an effective way to bridge variables and processes at different scales, and revealed the underlying logic and main structural features of complex systems, leading to a multiscale computing strategy for high accuracy, high speed, and ideal
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scalability. As illustrated in Fig. 6, it has two main features (Ge et al., 2011): (Here insert Fig. 6.)
Similarity among problem, model, software and hardware in logic and structure: As to
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complex systems, a common structural feature is that the system consists of many elements,
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and complex structures exist at the mesoscale between the element scale and the system scale. Such complex mesoscale structures are governed by the principle of compromise in
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competition, that is, the stability conditions expressed as a multi-objective variational problem.
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Correspondingly, such a structural and logic feature should be realized in the model, software, and computer hardware. That is, interactions among elements are constrained by global
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stability conditions at the system scale, and interactions among mesoscale cells are correlated by local stability conditions at mesoscales, giving the principle to optimize the intensity distribution of data and communication in computation (Li et al., 1988). That means, the multiscale structural features of the simulated systems should also be reflected in the physical models, numerical methods and algorithms, and computer hardware. The logical consistency 12
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provides a rational decomposition of the problem to minimize the physical dependence and hence communication throughput between different parts of the numerical model. Meanwhile, the structural consistency maps software workload to hardware with best load balance. They are both essential to guarantee the speed, scalability, and efficiency of
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simulation. Computation strategy of “first global, then local, and finally detailed”: The above-mentioned structural and logic features suggest that computation can start from the global distribution,
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then followed by the determination of the structure in a cell (Liu et al., 2012). Taking such a structure as the initial condition, the detailed structural evolution can be tracked finally at the element scale (Ge et al., 2011; Lu et al., 2016b). This strategy lifts the long-lasting scalability barriers for the simulation of industrial processes, where a major and common obstacle has
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been the increasing complexity of the data and operational dependence among different parts
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of the computational domain at larger scales, which leads to increased waiting and latency. In this strategy, such dependence is layered at different scales and decoupled among them. On
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the other hand, the dependence within each layer is also reduced by applying the stability constraints. As a result, the scalability and efficiency of the computation can be improved
EMMS
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The
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significantly.
Paradigm
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suggests
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general
multiscale
architecture
for
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application-oriented supercomputing, which is illustrated in Fig. 7 (Ge et al., 2015) using the simulation of particle-fluid flow as a sample implementation: In the top layer, the simulation starts with the steady-state solution of the axial and radial distributions using a simple macroscale model constrained by the global stability conditions 13
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(Nst)system min (Liu et al., 2012; Hu et al., 2013; Liu et al., 2015), where, according to the EMMS model, Nst is the mass-specific energy consumption for suspending and transporting the particles. With these distributions serving as the initial condition, the time for dynamic evolution of the system at microscale and mesoscale can be shortened greatly with sufficient accuracy (Liu et al.,
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2011). It requires the general operations on networks and the global linear algebra operations to matrices (Ge et al., 2015). These operations are typically sparse and favorably performed by general-purpose multicore processors such as CPUs operating in the multiple instructions multiple
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data (MIMD) mode.
In the middle layer, to describe the evolution of the mesoscale structures, continuum-based methods are coupled with discrete particle methods either connected at interfaces or occupying the same space. The local stability conditions, (Nst)local min, also provide effective constraints to
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facilitate the coupling (Lu et al., 2014), which can accelerate the computation further by using
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larger cell size and time step. It is most important when continuum variables are mapped to discrete quantities with more degrees of freedom, otherwise no unique mapping law can be
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established and the empirical mapping law may result in inaccuracy, instability and inefficiency (Chen et al., 2016; Chen and Wang, 2017;). In this case, the major numerical operations include
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interaction detection, processing, particle motion, and status updating, which are mainly local and
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additive operations, and are preferably performed by many-core processors operating in the MIMD mode. No ideal processor has been found for such operations. Intel MIC processors (2013 Intel; 2016) can temporarily serve as a close replacement for this category.
In the bottom layer even simpler models can be applied for the detailed evolution of the systems, such as the discrete element method (Cundall and Strack, 1979), lattice Boltzmann method 14
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(McNamara and Zanetti, 1988), or lattice gas automaton (Frisch et al., 1986), which are performed by many-core processors in the SIMD mode. In fact, many stencil-based explicit PDE solvers or particle methods based on fixed neighbor lists are also suitable for this mode (Ge et al., 2017).
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(Here insert Fig. 7.) The improvement in simulation accuracy, speed and efficiency brought by the EMMS model and then the EMMS paradigm has not only facilitated fundamental research (Ren et al., 2009; Ren et al., 2012; Zhou et al., 2014; Xu et al., 2015; Hou et al., 2016) and process development (Lu et al., 2007; Li
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et al., 2013b; Ren et al., 2013; Qin et al., 2016; Lu et al., 2017; Zhou et al., 2018), but also suggested a new simulation mode in recent years. As shown in Fig. 8, 2D quasi-realtime interactive simulation of a pilot-scale CFB riser can now be achieved with integrated visualization functions and a user interface (Ge et al., 2015). The operator can adjust, for example, the gas velocity at the inlet, and the
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corresponding change in the flow field is observed on the screen almost instantly, as the simulation is
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only 50 times slower than realtime (Ge et al., 2017). This is remarkably different from traditional “offline” simulations where one has to wait days for seconds of evolution. This is indeed a
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more details.
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demonstration of VPE (Ge et al., 2011; Liu et al., 2012), as discussed in the following section with
(Here insert Fig. 8.)
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4. Main features of virtual process engineering (VPE) As a significant upgrading of traditional simulation and experiment, VPE is defined by the
replacement of real equipment and related processes (such as structural deformation, energy and material transport, and chemical reactions) with apparently identical digital counterparts for R&D 15
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purpose. With the following main features, it may bring about new possibilities to chemical and process engineering: High accuracy, speed and efficiency in simulation: VPE requires high computational speed for real time or quasi-realtime simulation, and it is not at the cost of accuracy and efficiency. Such
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comprehensive high standards can be met by the multiscale computing paradigm described in Sect. 3, and its implications are profound. First, it enables virtual operation of the simulated systems in an interactive mode, so that not only the normal steady-state in industrial productions, but also the
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dynamic behaviors of the systems in start-up, shut-down and abnormal operations can be investigated safely. It also means the test of new designing ideas will not be a chain of long waits and the brain-storm style group discussion facilitated by VPE will be new norms in process engineering. Second, simulation for very long physical time, hours to days instead of seconds to minutes as typical
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for today, will be attainable with reasonable computing time, for example, several weeks. That means,
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slow processes such as the deactivation of catalyst, attrition and wearing of the particles, and residence time distribution of the reactants in reactors can be computed directly without a
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predefined kernel model as in population balance modeling. Third, high computational speed and scalability may enable concurrent simulation of multiple equipment or even systems from
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element-scales rather than unit-scales as practiced in process systems engineering at present. In this
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way, physically realistic virtual factories can be established without empirical and phenomenological equipment models in the so-called flow-sheet simulations. Fourth, concurrent simulation of a series of similar cases with a large number of different structural or operational parameters or material properties will be affordable also, facilitating comprehensive understanding and extensive
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optimization of the system studied. In particular, it may provide the huge amount of data needed for big data analysis, data mining and the training of artificial intelligence applications. Quantitatively realistic VR: According to the Virtual Reality Society (VR Society, 2018), virtual reality is defined as a three-dimensional, computer generated environment which can be explored
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and interacted with. User is able to immerse into this virtual world and manipulate objects there or perform a series of actions. From the point of view of VR, VPE also represents a significant extension of its technological basis, that is, physical VR (PVR). While classical VR focuses on visual or
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phenomenological fidelity of qualitative nature, PVR means the produced scenes are physically accurate also. This is only possible with multiscale simulation and supercomputing. In addition to the common advantages of VR for training and educational purpose, such as immersed environment and interactivity, PVR also enables virtual (and non-intrusive) measurement by traditional data analysis,
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pattern recognition and data mining. Concentration profiles, geometrical characteristics of the
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heterogeneous structures, and macro statistical information such as throughputs and inventories, can be obtained in PVR. With these capabilities, VPE provides revolutionary virtual labs and offices for
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chemical engineers, which can catalyze new research and design ideas and improve working efficiency greatly. A major technological obstacle of PVR is, however, the in-situ integration of the computation
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and post-processing including virtualization and data analysis. This is because that, if the traditional
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way of offline post-processing is still used, the huge data generated by the high-resolution, high-speed computation would be unaffordable for any communication and storage technologies, and at least become the main bottleneck for the overall performance. The EMMS Paradigm may also suggest a new approach to tackle this difficulty, as will be discussed in Sect. 5.
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Seamless coupling between accurate simulation and precise experiment: Traditionally, simulations and experiments have been compared quantitatively at two extreme ends, either for very simple cases (such as flow past a single particle) or for very simple signals (such as the overall flow rate in a reactor) because of the limited resolution and accuracy of both sides. It can serve as a good
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validation of the model when the two ends agree well. However, when they disagree, experiments are not very helpful in determining the error in the model or numerical method because they cannot provide the same level of details as simulations. This difference can be compensated for in VPE using precisely designed elemental processes. Both the experimental and simulation conditions and
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parameters can be controlled and adjusted with sufficient precision. Then, with real time simulation and data processing for both virtual and real experiments, the behavior of the models can be compared online with the experiments strictly, systematically and comprehensively, providing a
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convenient and efficient approach for validating and improving simulation models and methods. It can significantly expedite the development of physical models and numerical methods. On the other
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hand, these capabilities also enable the integration of physical and virtual parts, and both can be
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situated locally or remotely, into a single R&D prototype. Such hybrid systems can eventually be cloud-based, which provide significantly better fidelity and flexibility than those provided by either
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experiments or simulations, and hence facilitate faster and more economic and efficient R&D
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activities. It is worthy of mention that, for some hybrid systems, the performance of computation and measurements can also be mutually enhanced as exemplified in Sect. 5. In summary, with VPE, the interplay of fundamental research, technological development and
industrial implementation can be expedited significantly and direct scaling-up of chemical processes to industrial application will become possible. 18
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5. A roadmap for the development of VPE With the concept, strategy, and key technologies discussed above, a preliminary implementation of VPE is currently possible and has recently been explored at IPE. The introduction to these attempts below is aimed to provide a clearer picture of the future development of VPE, and to demonstrate the
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typical application scenarios of VPE. 5.1. Proof of concept (VPE1.0, established)
Based on the EMMS paradigm, VPE has been explored at IPE for years and its progresses were
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discussed by (Ge et al., 2011) and (Li et al., 2013a) previously, among which the first demonstration platform for VPE, referred to as VPE 1.0, was established by Liu et al. (2012). As illustrated in Fig. 9 (Liu et al., 2012), VPE 1.0 consists of an experiment and measurement subsystem, a control and data
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acquisition subsystem, and a high-performance simulation subsystem as well as a large graphical display. The experiment and measurement subsystem is a pilot-scale circulating fluidized bed (CFB)
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equipped with several measurement devices that provide realtime physical property data, e.g., an eight-channel optical-fiber analyzer for local solids concentration, an electric capacitance tomography
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for cross-sectional solids concentration distribution, a dual-plane X-ray scanner for solids circulation
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rate (Meng et al., 2011), and multiple pressure transducers along the CFB loop. Users can change operating parameters in realtime via an industrial personal computer (IPC) or by issuing commands
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from a control computer. The realtime measurement signals can be processed online and transmitted to the display array for comparing with pre-stored simulation data. The three-level heterogeneous CPU-GPU computing system, Mole-8.5 was developed at IPE to reflect the structural similarity of the physical model and the numerical algorithm, but the EMMS 19
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paradigm could not be fully implemented yet because general-purpose parallel simulation software needed to be developed further to reproduce detailed hydrodynamic evolution in realtime. To tackle this challenge at that time, the EMMS Paradigm was partially adopted in VPE 1.0: The macro-scale steady-state hydrodynamics was obtained first by using an EMMS-based general method for full-loop
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computation of the hydrodynamics in complex gas-solid reactors (Liu et al., 2015), which serves as the initial conditions for simulating the dynamic behaviors in the bed (Hu et al., 2013; Liu et al., 2015). The dynamic simulation in a TFM used in the commercial software, Fluent, was improved by using the EMMS drag through user-defined functions (Yang et al., 2003a; Wang and Li, 2007; Wang et al., 2008).
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Using this strategy, the VPE 1.0 system takes about one week to simulate a one-minute physical process in the pilot-scale CFB system (Liu et al., 2012).
Although VPE1.0 is only a demonstration without the full capabilities of VPE described in Sect. 4,
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it can already provide some of them. For example, it can function as a comprehensive platform for the
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acquisition of basic hydrodynamic data and hence the training of new operating staff at much lower cost. This is an elementary but practically important application of VPE. Physical or digital simulators
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of real equipment or factories are widely used for training and educational purpose in process industries. Now with VPE they can be established more efficiently and also more realistic and flexible
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to explore the performance characteristics and boundaries of the target equipment or factories
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exhaustively. For some occasions that are too risky and costly to produce physically, such as severe accidents, the accuracy and predictability provided by VPE is critical as it may determine whether they are handled properly, otherwise major consequences are almost unavoidable. In the same sense, VPE is also a good tool for self-inspired learning, as new possibilities, either for the equipment or for the processes, can be explored with VPE without misleading and limitation. 20
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On the other hand, high-resolution accurate simulation and precisely designed experiments on elemental processes can now be integrated with VPE1.0. In particular, many experiments are presently limited to static, local and offline measurements due to the time consuming data processing and analysis. Now with supercomputing, it is quite promising to elevate the spatiotemporal resolution
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and scope to the realm of online measurement of industrial processes. A typical example is from the CT technology where ultrafast scanning in hardware has reached 8000 frames/s (Fischer et al., 2008) and the bottleneck for measuring dynamic process lies in realtime reconstruction of the images from original data, whose speed is traditionally in 1 frame/s range. With GPU-powered supercomputing (Xu
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and Mueller, 2007; Meng, 2009) the reconstruction time can be improved to 137 frames/s already (Bieberle et al., 2017). However, using more GPUs for higher performance is not effective currently, because ultra-fast scanning may require data transfer rate in the GByte/s range which becomes the
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limiting step. That means, even before realtime simulation is fully realized, both experiments and simulation can benefit very much if supercomputing can be improved significantly in memory access
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technology and more scalable data processing: the model behaviors can be investigated in a
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systematic and comprehensive manner, providing a convenient and efficient approach for validating
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and improving simulation models and methods. (Here insert Fig. 9.)
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5.2. Interactive multiscale simulation (VPE2.0, in progress) Based on the recent development of simulation software and upgrading of the supercomputing
system to Mole-8.5E with a complete and more compact three-level architecture and doubled performance, the successive VPE2.0 system was developed (Xu et al., 2017).
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As illustrated in Fig. 10, multiscale simulation methods are cascaded, starting with the global distribution method for the full-loop of the system (Liu et al., 2015; Hu et al., 2017), followed by an EMMS-based simplified TFM (Qiu et al., 2017) for the riser section of the system, an EMMS-based coarse-grained DPM, EMMS-DPM (Lu et al., 2014), for a cross-sectional slice of the riser, DNS of the
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detailed flow field in an element in the slice, and MD simulation of the reactions and diffusion inside the particles (Zhao, 2017). As illustrated in (Liu et al., 2012), at the top level, a few servers with only high-speed CPUs were used to conduct the global optimization with respect to the global stability condition. At the middle level, more servers with a balanced configuration of CPUs and GPUs or XPU
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(MIC) were introduced to compute the mesoscale continuum. At the bottom level, many servers with multiple GPUs were assigned to implement the microscale discrete simulation. In this mode, the dynamic evolution at a larger scale provides the initial and boundary information to the next smaller
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scale. As a result, the EMMS paradigm was fully implemented to some extent in VPE 2.0, so as to achieve higher possible accuracy, speed and efficiency for VPE. As indicated in Fig. 10, a recent test
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shows that it takes about 30 s to model the full-loop of an industrial-scale CFB system with a riser of
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8.5 m in height and 0.411 m in diameter. The simulation speed for the riser only, a slice of the riser, and a cell in the slice reached up to 200, 240 and 104 times slower than realtime, respectively, while
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the MD simulation of the reactions and diffusion inside the particles can be expected to proceed at
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the speed of 105 s/ns in VPE 2.0 (Xu et al., 2017). (Here insert Fig. 10.)
The multiscale multistage simulation style realized above has demonstrated a typical application
scenario of VPE in R&D. In principle, as explained in (Ge et al., 2017), the change of the operating conditions can also be signaled to the control subsystem and then enforced in the experimental 22
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system. In this manner, online comparison between simulation and experiments should be possible but in a “slow motion” mode, which restricts the rate of change. At the very least, this state-of-the-art system can provide a powerful virtual system for R&D purposes, inspiring new insights and design for engineering systems.
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For more detailed exploration of equipment performance, 3D full-loop simulations (Lu et al., 2016b) of pilot CFB systems (Liu et al., 2012) may demonstrate the potential of VPE better. As reported in (Ge et al., 2017), using 3 million coarse-grained particles representing nearly 50 billion real
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particles, the simulation can advance at 2.3 s per day using 60 CPU-GPU pairs (Intel Xeon E5-2680 and Nvidia Tesla K20). More recently, Xu et al. (2017) simulated the same system with 127 million coarse-grained particles at 1.2 s per day using 135 K80 GPUs and 270 cores of Intel Xeon E5-2680 CPUs (the Appendix), as shown in Fig. 11. Integration of reaction kinetics is also underway, based on
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the temporal speedup method of Lu et al. (2016a).
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(Here insert Fig. 11.)
With such VPE capability, as demonstrated in (Ge et al., 2017; Xu et al., 2016), it will be
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affordable to simulate hours or even days of the real processes within reasonable time. In traditional
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methods, locally or temporary steady states are usually assumed, so that the transient and long-term behaviors can be described almost independently and then combined with the former, providing rate
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parameters/models to the latter, for instance, through the kernels in PBM. However, in an industrial reactor, for example, a catalyst particle may follow a very complicated trajectory with significantly different flow and reaction conditions, and different particles may follow very different trajectories for varying resistance time. In this case, the transient behaviors are intrinsically coupled with the long-term behaviors, and the traditional methods for the change of particle sizes are not accurate 23
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enough. Now with VPE, direct simulation of the long-term behavior at both high spatiotemporal resolution and large scales (also for a large number of particles to ensure statistical accuracy) may become inevitable, as it is almost impossible for experimental measurement. This capability provided by VPE is, therefore, a substantial step forward for computer simulation to outperform experiments in
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a wider range of applications, and it can be used to understand slow processes such as carbon deposition, catalyst deactivation, particle wearing or attrition, etc., or to locate precisely the hot spot in a reactor.
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VPE at this stage can also facilitate the mutual improvement of experiments and simulation through their close interactions. For example, PIV is often employed to measure instantaneous singleor multiphase velocity fields. Because of the limitation of the frame frequency of CCD cameras as well as the data storage rate and capacity, traditional PIV measurements cannot capture the continuous
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evolution of mesoscale structures such as particle clusters in gas-solid systems. Computer simulations
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can then be introduced to interpolate the evolution of particle clusters between successively measured frames, using the experimental data as initial conditions. Higher temporal resolution for the
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experimental measurements and higher accuracy for the computer simulations would hence be achieved simultaneously, which would be much more difficult, if not impossible, to achieve using
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these two approaches independently. In the VPE mode, this integration can be routinely performed
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with great ease: In case of disagreement, it can also provide detailed information for the analysis of the causes and for further improvement of the simulation and/or experiment, until satisfactory results are obtained. Unlike the traditional interplay between simulation and experiment, this mode is far more effective and efficient for accelerating the research stage. 5.3. Cloud-based virtual factory on the horizon (VPE3.0, in perspective) 24
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Beyond VPE2.0, a general-purpose, sophisticated implementation of cloud-based virtual factory has been explored as well. Different definitions of virtual factory can be found (Bell and Fogler, 1996; 1997; 1998; Bell, 1997; 1999; Luo et al., 2012), even with different names, such as digital factory (Centobelli et al., 2016), virtual manufacturing or digital manufacturing (Mourtzis, 2015). Vaguely they
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seem to be referred to the same kind of things, all aiming at breakthroughs in the R&D and manufacturing mode, but actually no agreement has been reached so far. In fact, virtual factories should bear the following distinctive features, which may justify it as the identity of VPE3.0:
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(Here insert Fig. 12.)
Coupling of virtual and real systems without spatiotemporal limitation: The integration of physical and virtual systems will provide R&D prototypes with the best fidelity and flexibility significantly better than those provided by either experiments or simulation. For example, with real
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factories providing online operation data and real time simulation providing internal details, the
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reactors will become “transparent” to the operators. Virtual factories don’t have to be limited to the same physical space. It will be typical that
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different virtual or real parts are widely distributed at anywhere in the world with network
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connection. Thanks to the fast growing network technology, the “WiFi+5G+fiber transmission” solution can provide real time data acquisition remotely, meaning not only cloud-based virtual factory
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service but also cloud-operated real factories are possible, as illustrated in Fig. 12. Virtual factory can provide various services, including new process development, equipment design and optimization, modulation and optimization of operation conditions, education, pre-duty training, abnormal forecast, and so forth.
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Process safety may be a good example. Traditional abnormal monitoring relies on the comparison of on line and historical operation data. When an obvious deviation is identified alarms are triggered. In the virtual factory mode, once reliable simulation can run even faster than real time speed, it will become a prediction tool, which means the abnormal forecast can be realized via the
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online analysis of realtime simulation and online operation data. That means, alarms can be issued much earlier and more time will be available for precautions to avoid accidents.
Coupling between process engineering and PSE: Cloud-based VPE enables detailed simulation
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and digitalization of multiple equipment in a complete factory, so that the interpenetration or coupling of process engineering and PSE will become common practice. The virtual factories established in process PSE now may have provided some extent of reality but they are more focused on the global performance and visual effect using accumulated production data and simple
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correlations, whereas VPE is of quantitatively predictive nature. In fact, for computer simulation at
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the equipment level, such as CFD and particle dynamics simulations, the coupling between different reactors or equipment is not a substantial difficulty, as they are usually connected with pipe and
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valves, which can be described as typical boundary conditions already employed in these simulations, so that simulations of different parts are run in parallel. The real challenge is to maintain both high
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speed and high accuracy of the simulations and online coupling to realtime measurement data, so
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that the factories can be virtually operated and extensively optimized by evaluating different operation conditions of the different parts and their combinations. That is, the details and predictability provided by this level of the simulations should not be compromised with the speed and efficiency of PSE computations. This is only possible with cloud-based VPE and, on the other hand,
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virtual operation of individual equipment in VPE can also provide valuable data for PSE to develop or improve simpler equipment models and correlations, as practiced in traditional CFD. Application of big data analysis and artificial intelligence (AI): Virtual factories can be producers of big data also. The rapid development of accurate and easily accessible sensing technologies
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powered by the Internet of Things (IoT) can provide huge hybrid data with different spatial and temporal resolutions for data mining. High data quality is also ensured as the IoT-based sensors can acquire field data with minimum interference to the real processes.
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The integration of big data and AI has obtained encouraging success in e-commerce, finance, medical treatment. Similar examples can be found in chemistry already, for example, statistical potential functions optimized using neural network and Monte Carlo methods based on large numbers of quantum mechanics simulations of atoms have shown satisfactory accuracy. For process
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development, however, the main difficulty is the lack of enough and reliable data, either from
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simulation or experiment. Currently, industrial operation does monitor some basic parameters for each equipment, such as temperatures, pressures and flowrates at some critical points, which have
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accumulated to considerable amount and well archived in big enterprises, but these data are too
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limited to reflect the detailed dynamics of processes, and hence inadequate for mechanism-based models or correlations of general applicability. Traditional simulations, such as CFD, may produce
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much more data for this purpose, as attempted in data-driven CFD, but the amount and accumulation speed of the data are still insufficient for big data analysis and AI. Now with cloud-based VPE, reliable and efficient simulations can explore extensively the parameter space of many processes within reasonable time and cost, and huge amount of simulation data with enough resolution and accuracy will be available, together with the greatly expanded experimental and industrial data. This may 27
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justify the use of big data and AI methods in the search of practical models or correlations for the description of complex processes at a scale higher than that of the simulation models. Of course, it may also be used for the optimization of process and equipment design directly. To exploit the possibility of cloud-based VPE, a demonstration project was carried out, as Fig. 13
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shows. A pilot scale CFB system, originally built in the Swiss Federal Institute of Technology in Zurich (ETH) (Nicolai and Reh, 1995; Herbert and Nicolai, 1998), and rebuilt at IPE, CAS in 2002, has been upgraded into a remote experimental facility. The riser is 0.411 m in diameter and 12.43 m in height.
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This platform was upgraded with more than 80 sets of pressure, temperature, flow-rate and power measurement sensors as routine gauging. Besides that, optical fiber probe, mechanical probe, online solids flux scanner and portable X-ray CT were also in-house developed for the local and cross-sectional measurement. The obtained experimental data can be imported into an Oracle
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powered database for validation and refurbishing of CFB design and optimization.
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An ethernet-based distributed control system (DCS) and data acquisition system were developed to ensure the stable operation of the whole unit. The DCS consists of one central and four filed
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control stations, and two analog monitoring stations, two PID stations. The DCS can work under the
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client or server mode respectively, which means with the authorization of central control station, the whole unit can be operated remotely via ethernet or internet, and the routine measurement data
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acquisition and transmission can also be achieved remotely. The remote control of this platform just serves as a demonstration, and in theory, any remote facilities, both experimental and industrial ones, can be controlled remotely via the same technical route. With this facility as a prototype, the quasi-realtime simulation powered by HPC has been conducted, as described in Sect. 5.2. Both the
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simulation results and online measurement data are transmitted into the VPE control center, where the online virtual reality visualization is performed. (Here insert Fig. 13.)
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With these new possibilities, VPE can provide versatile services to the industries, at both R&D and production stages, and for either engineering, or academic and educational purposes. However, meeting the high standards for VPE in modeling, simulation, measurement, analysis and visualization
6. Integration of mesoscience and VPE to PSE
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call for new methods and technologies as discussed in the next section.
The discussions so far have focused on the reactor level, but in many cases the general principles they embodied are applicable to other levels. However, though all complex systems in Fig. 1 are
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shaped by the stability conditions resulted from the compromise in competition of dominant mechanisms (Li, 2015a; 2015b; Li et al., 2016; Li et al., 2018a; Li and Huang, 2018), the dominant
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mechanisms themselves are level-specific. For example, they may be related to the reaction and
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diffusion processes on the material level (Wang et al., 2013; Sun et al., 2016), and depending on the
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interactions between different phases, and the reactor types on the reactor level. On the factory level for PSE, economic and ecological factors may have to be incorporated
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(Bakshi, 2002; Grossmann, 2005; Triebl et al., 2013) in addition to physical and chemical constraints, which typically also introduce multi-objective optimization problems. For the first two levels, though these factors, such as low cost and low energy consumption, may also be considered as designing constraints, they do not determine the nature of the dynamic structures directly. For the factory level, however, the optimization is very often not limited to that level only, as explained in (Li et al., 2018b), 29
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they can first be conducted according to the specified conditions, and then the optimized results should be returned to the first two levels to check the possibility of realization, and the first two levels can offer feedback on new availability, as suggested in Fig. 14, so iterations and correlations between levels are necessary. These features specific to the factory level may further complicate the
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multi-objective optimization in PSE. In particular, when regime transitions are encountered in the optimization process and the jump changes are determined by variables on different levels, most traditional optimization methods will be challenged.
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(Here insert Fig. 14.)
Mesoscience can be integrated here to facilitate the PSE studies in that, by identifying the dominant mechanisms and establishing stability conditions in the problem rationally, a logical partition of the systems involved and their levels appears naturally. Otherwise, it will be very difficult
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to find the correct interdependence of the many variables in the problem and at least obscure the
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intrinsic ones. Similarly, mesoscience is critical for defining the mesoscale parameters (Li and Huang, 2018) and providing the pathways for inter-level parameter transfer. Anyway, the top-down transfer
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from the factory to the reactor and material levels is very difficult, especially when regime-specific
2018b).
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features have to be considered, which inevitably requires transdisciplinary collaborations (Li et al.,
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On the other hand, within each level, a concise and accurate model at the system scale is always
helpful to the modeling on the upper levels, and it should be most favorable to PSE which works on the top level. Mesoscience is a promising way to this end and VPE is the efficient simulation platform based on mesoscience. By these means, either simple global models (Hu et al., 2013; 2017; Liu et al., 2015; Zhang et al., 2016) or quasi-realtime digital counterparts can be established for the lower-level 30
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systems in PSE, which are treated as elements in the factory level simulations, analysis and optimization. As to solving the multi-objective optimization problems, a new route has been found recently (Zhang et al., 2018), validated by the two-objective optimization in the EMMS model. With these developments, more extensive optimization and more precise designing and control can be
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reached in PSE to address the most complicated problems in process engineering. 7. Conclusions & prospects
In this article, the critical challenges to modern chemical engineering are analyzed.
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Characterizing, modeling and elucidating mesoscale structures are identified as a central difficulty behind these challenges. The EMMS model was then revisited briefly and analyzed on how it can address this difficulty. The model also suggested a new multiscale architecture for supercomputing, the EMMS Paradigm, to keep the logical and structural consistency between the simulated systems
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and the simulation models, software and hardware. In this paradigm, high accuracy realtime
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simulation for industrial processes will be feasible and by integrating with virtual reality technologies, virtual process engineering can be realized as an updated form of computer simulation in chemical
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engineering. To summarize, the most important viewpoints and predictions are reiterated as follows:
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VPE, featuring physical virtual reality and seamless integration of virtual and real systems, is promising for addressing the big challenges of modern chemical and process industries, such as
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sustainable development and adapting to the new economic mode. It may bring about a revolution of chemical engineering and a wide range of engineering and scientific fields.
The EMMS Paradigm, with its emphasis on the logical and structural consistency between the simulation and the physical system plus the multiscale computing approach of “from global 31
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distribution to local and detailed evolution”, may provide a general and effective way to achieve physical virtual reality and implement VPE. Mesoscience, with the mission of characterizing, modeling, and elucidating the mesoscale structures on different levels, is behind almost all challenges of chemical engineering and hence of
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the technologies of VPE, which is critical not only for modeling and simulation but also for experiment and data analysis.
A cloud-based VPE service will soon be possible with interdisciplinary efforts, in the form of virtual
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factories, with which physical and virtual facilitates worldwide can be integrated as a unified R&D platform, improving the capability and efficiency of VPE.
To realize VPE is a promising mission in chemical engineering. It is, however, far beyond the
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current capability of the field. Transdisciplinary collaboration and the integration of global resources in academic, educational, industrial, and even social circles are needed. A paradigm
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shifting calls for not only breakthroughs in knowledge but also changes in research organization
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mode.
By addressing the factory-level complexities with both natural and social constraints (e.g.,
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providing more technical details), PSE is in urgent demand to incorporate with mesoscience and
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VPE to obtain accurate and effective models or descriptions on lower levels and to solve multi-objective optimization problems in a physically reasonable approach.
Acknowledgements The authors would like to thank all the members of the EMMS group for their contributions to the preparation of this article and for sharing some of their unpublished work, as cited in the text. The 32
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authors are grateful to the financial support received from the International Partnership Program of Chinese Academy of Sciences (Grant No. 122111KYSB20170068), the National Natural Science Foundation of China (Grants No. 91834303), and CAS (Grants Nos. QYZDJ-SSW-JSC029, XDA21030700, XDA21040400, and XXH13506-301).
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Appendix
The EMMS-DPM (Lu et al., 2016b; Lu et al., 2014) is adopted in the simulation. The computational cost of the particles is greatly reduced while maintaining the accuracy. The movement
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of CGPs is modeled as standard discrete element method (DEM) (Cundall and Strack, 1979):
dv CGP dw CGP Fb Fc Fgp Fdrag , I CGP T (1) dt dt where mCGP is the mass of all real particles in a CGP; vCGP is the CGP velocity; Fb, Fc, Fgp and Fdrag are the mCGP
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body force, the contact force, the pressure gradient force and the drag of the CGPs, respectively; ICGP
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wCGP and T are the moment of inertia, the angular velocity and torque respectively. The gas is modeled by the volume-averaged Navier–Stokes equations (Anderson and Jackson,
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1967) and solved by the continuum-based finite-volume method (FVM). The governing equations are: g g
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(2) ( g g ug ) 0 t ( g g ug ) (3) ( g g ug ug ) gpg (ug us ) ( g g ) g g g t where ρg, ug and εg are the density, velocity and voidage of the gas phase, respectively; us and β are the velocity of the CGPs and the drag coefficient between the gas and the particles, respectively; τg is the gas phase stress tensor; g is the gravity.
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The EMMS drag model (Wang and Li, 2007) is adopted to consider the heterogeneity effect on the gas-solid interactions. Please refer to our previous papers (Lu et al., 2016b; Lu et al., 2014; Xu et al., 2019) for more details of the models.
1. Table 1 Simulation parameters of the whole CFB loop (Xu et al., 2019). Variables Diameter, dp (μm) Coarse-graining ratio Density, ρp (kg/m3) Solid inventory Young’s modulus (Pa) Poisson’s ratio [-] Coefficient of restitution, e [-] Coefficient of friction [-] Coefficient of rolling friction [-] Time step, dtDEM (s) Coefficient of restitution, e [-] Coefficient of friction [-] Coefficient of rolling friction [-] Gas phase density (ρg) (kg/m3) Viscosity (μg) (kg/(m·s)) Flux at inlet of riser (m3/h) Grid number Time step, dtFVM (s) Operating pressure (Pa)
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Item Particle
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Additionally, the main conditions and parameters of the CFB simulation are summarized in Table
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Particle–wall
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Gas
Values 82 10 2450 90 kg 1.0×106 0.12 0.1 0.5 0.1 5×10-7 0.6 0.3 0.1 1.1795 1.8872×10-5 110 261,106 1×10-5 1.01×105
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Figure Legends Fig. 1. (Color online) Three levels and corresponding mesoscales of chemical processes. Modified from
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