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Optimal operation and control of intensified processes — challenges and opportunities
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ScienceDirect Optimal operation and control of intensified processes — challenges and opportunities Lisia S Dias and Marianthi G Ierapetritou Process systems engineering (PSE) tools can be utilized to enable the optimal operation and control of intensified processes. In this work, the challenges in the control of intensified processes are summarized, including the difficulties in modelling and performing online optimization of these highly complex dynamic systems. Nevertheless, PSE progress in the areas of nonlinear programming, reduced-order modeling, development of software tools, parallel computing and artificial intelligence are enabling the implementation and optimization of large-scale intensified systems. Future opportunities for research are identified, including the simultaneous optimization of scheduling of operation and control, a promising strategy that has proven its value in regular operations, and can expand the operating windows of intensified processes. Address Dept. of Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08854, United States Corresponding author: Ierapetritou, Marianthi G ([email protected])
Current Opinion in Chemical Engineering 2018, 22:xx–yy This review comes from a themed issue on Process systems engineering: process intensification Edited by Jim Bielenberg, Karen Fletcher, Mahmoud El-Hawlwagi, and Ka Ming Ng
https://doi.org/10.1016/j.coche.2018.12.008
waste disposal regulations [2]. In this context, Process Intensification (PI) and Process Systems Engineering (PSE) emerge as tools to address current challenges in the operation and optimization of manufacturing facilities. A general definition for process intensification has been given by AI Stankiewicz and JA Moulijn [3] as “any chemical engineering development that leads to substantially smaller, cleaner, and more energy efficient technology”. Several extensions to this definition have been proposed over the years in order to accommodate the progress and developments of the field [4,5,6]. Recently, Tian et al. [7] summarized activities that results in intensified processes, including the combination of multiple process tasks or equipment into a single unit (e.g. membrane reactors, reactive distillations), the miniaturization of process equipment (e.g. microreactors), the operation of equipment in a periodic manner (e.g. simulated moving bed, pressure adsorption swing), and a tight process integration (e.g. dividing wall distillation). Judging from the growth of research interest in process intensification [1], it is clear that PI is a promising field that can enable a paradigm shift to the process industry, offering novel processing methods and equipment to achieve higher efficiency and safer operation. Nevertheless, challenges concerning the operability and controllability of intensified processes can prevent the successful implementation and optimal operation of such processes in the chemical industry.
2211-3398/ã 2018 Elsevier Ltd. All rights reserved.
Introduction In a highly competitive economic global environment, fomented by variability and extensive information exchange, companies must adapt and embrace changes in order to survive. Manufacturing facilities are required to be more flexible to accommodate the needs of dynamic markets. Frequent variations in raw material compositions, together with prices and demand fluctuations, give much more emphasis on process dynamics [1]. This raises the need for incorporating process dynamics in decisionmaking processes in order to guarantee optimal operations. A more sustainable industrial development should be pursued as environmental pressures increase, including carbon dioxide emission consideration and restrictive www.sciencedirect.com
Process Systems Engineering can contribute to this challenge by providing tools for a systematic approach for the design, optimization, control and operation of intensified processes. Over the years, the PSE community has developed novel representations and models that capture nontrivial features, enabling the simulation of complex processes, advances in process control, and improvements of decision-making processes for the operation of the chemical supply chain [2]. PSE has also contributed to the development of computationally efficient solution methods and software tools for complex optimization problems. Of particular interest to this article, PSE has proposed a representation of the hierarchy of decisionmaking process in the operations and control of a process industry, and has systematically addressed the challenges emerging in each level of the hierarchy [8,9]. Such representation is depicted in Figure 1, and encompasses the problems of planning, scheduling and control of manufacturing facilities. Current Opinion in Chemical Engineering 2018, 22:1–5
Please cite this article in press as: Dias LS, Ierapetritou MG: Optimal operation and control of intensified processes — challenges and opportunities, Curr Opin Chem Eng (2019), https://doi.org/ 10.1016/j.coche.2018.12.008
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2 Process systems engineering: process intensification
Figure 1
Then, the integration among different decision-making stages (moving from control to scheduling decisions) are investigated. Interesting results from classical operations are presented, in the hopes that they will instigate efforts from the PI and PSE communities in exploring this direction. The integration of scheduling and control can be seen as the natural ‘next step’ on the optimization of intensified operations, and a new tool to guarantee efficient and clean manufacturing.
PLANNING Planning horizon, PH t=1
t=2
t=M
Detailed Scheduling
…
Production Initial Inventory
Production
Production
… Inventory
Inventory Demand
Demand
Inventory
Production Targets
SCHEDULING Scheduling horizon, SH = t k=1
k=2
k=N …
Process Control
Machine 1 Machine 2 Machine 3
Sequence, setpoints
CONTROL Task i event point n x
V unit j
. . .
k
k+1
u t
Event Point in scheduling
unit n Process Control
V: amount of processing material x: state variable u: manipulated variable
Current Opinion in Chemical Engineering
Decision-making hierarchy in process operations.
In this article, an analysis of the tools and systematic approaches developed by the PSE community regarding the optimization of the decision-making processes in intensified processes is performed. In particular, an investigation of scheduling and control tools and its performance on intensified systems is conducted. First, ongoing efforts on the operability and control of PI systems are briefly reviewed. Challenges in the control of intensified process are summarized, and opportunities for further enhancement of the control of PI systems are identified. Current Opinion in Chemical Engineering 2018, 22:1–5
Control of intensified process Process control can enable the safe, economical, and environmentally optimal performance of intensified processes. This capability has been demonstrated with a special focus on reactive distillation columns, which has received wide acceptance in the chemical industry [10]. An excellent overview of process control in process intensification has been presented in Nika9cevic et al. [1]. In this paper, the authors demonstrate how the majority of current research in control of intensified processes is oriented towards advanced-model and optimization-based control. They also highlight challenges that are prevalent in the field of process control, as well as challenges that are specific to the control of intensified processes. Here, some of the challenges faced by the PSE and PI communities in controlling intensified process using advanced control techniques are summarized:
Demand
The implementation of model-based control requires extensive efforts on modeling and parameter tuning, which is usually expensive and time consuming. Process models can be obtained either by rigorous first principle modeling or by system identification techniques. On one hand, mathematical modelling requires expertise and intensive experimental validation; on the other hand, models identified from experimental data are usually suited for a limited operating range and may fail to capture crucial relationships between process variables The dynamic behavior of intensified processes is usually complex and highly nonlinear due to the integration of multiple physical and chemical phenomena into single, smaller device. Reactive distillation columns, for example, feature steady-state multiplicity, complex interactions between the vapor-liquid equilibrium and great degree of nonlinearity [11]. Such complex dynamic behavior makes intensified processes less suitable for online applications and requires further developments of nonlinear model predictive control (NMPC) and multi-parametric MPC. Process intensified systems feature a reduced number of degrees of freedom. Such affirmation can be argued intuitively, given that, when unit operations are performed in distinctive equipment, there are more possibilities for measurement and manipulation of streams www.sciencedirect.com
Please cite this article in press as: Dias LS, Ierapetritou MG: Optimal operation and control of intensified processes — challenges and opportunities, Curr Opin Chem Eng (2019), https://doi.org/ 10.1016/j.coche.2018.12.008
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Challenges and opportunities Dias and Ierapetritou 3
in between units. This intuitive concept has also been rigorously proved in Baldea [12]. Evidently, process intensification raises challenges in operation and control. Fortunately, significant progresses made on operability, controllability and optimization can foster the implementation of PI systems. Numerous efforts have been conducted on the simulation, modelling and control of reactive distillation [13], simulated moving bed processes [14], pressure swing adsorption [15,16,17], amongst other intensified processes. An excellent overview of state-of-the-art approaches for the modeling, simulation and synthesis of several representative PI technologies was given by Tian et al. [7]. The variety of works is outstanding and has significantly improved the understanding of intensified systems. To address the challenges related to the online implementation of NMPC, several algorithms and optimization software have been developed in the last three decades. The most efficient nonlinear programming tools now handle millions of variables and constraints with modest computational effort. Complex nonlinear process models can be optimized fast enough for online purposes, and dynamic optimization problems can be solved efficiently and reliably [18,19,20]. Furthermore, a variety of software tools have been recently developed in the academia facilitating the implementation of nonlinear MPC, such as NMPC tools [21], DO-MPC [22,23], and a software developed by Santamarı´a and Go´mez [24] for the implementation of NMPC and advanced-step NMPC [25]. Finally, an open-source modeling and discretization framework for optimization with differential and algebraic equations has been recently presented by Nicholson et al. [26], enabling the implementation of generalized frameworks in a high-level programming language. To address the remaining challenges related to large-scale problems, continuous efforts on the development of parallel computing, software and algorithms should be encouraged.
behavior of the system. Such strategy has been implemented in intensified processes for the control of simulated moving beds [31] and pressure swing adsorption [32,33]. The advances mentioned so far encompass traditional tools in the process systems engineering field that are enabling optimal control of intensified processes. A less explored direction includes the incorporation of artificial intelligence in the decision-making hierarchy, which promise to enable the handling of complex processes that were once thought to be out of reach [34]. Such effort requires a shift of the optimization-based paradigm in PSE. However, it may bring interesting solutions to complex problems such as the control and operation of PI systems, especially with new fundamental advances in machine learning presenting significant new technological and commercial opportunities [35]. Some works along this direction have already appeared, such as the use of neural networks and nonparametric approximators in control problems [36,37].
Integration of scheduling and control Scheduling of operations is a level above process control in the decision-making hierarchy. Scheduling can be defined as the problem of allocating resources, defining production sequences, equipment usage and assignment of tasks in an industrial plant, in order to achieve production targets while minimizing productions costs or production makespan. Such problems have been traditionally solved independently of the dynamic behavior of the system, although the solution of scheduling problems in usually implemented by the control level. However, recent studies and developments in PSE show that a tighter relation and simultaneous solution of scheduling and control problems can result in more efficient and economical operations [38].
The computational complexity of model predictive controllers can be reduced with the implementation of multiparametric mp-MPC, which enables the offline solution of MPC problem by reformulating it into a multiparametric optimization problem and obtaining explicit control laws [27,28]. Such strategy has been applied to pressure swing adsorption systems by Khajuria and Pistikopoulos [29] and extended in Khajuria and Pistikopoulos [30]. Multi-parametric programming presents its challenges related to the exponential growth of critical regions and the number of dimensions increase. Nevertheless, it is a promising alternative for the online implementation of MPC.
Research attempts to achieve the integration of scheduling and control so far have followed two main paradigms: top-down and bottom-up approaches [39]. Top-down approaches focus on transmitting detailed dynamic behavior of the system to the scheduling problem, such that more informed and feasible decisions are made at the scheduling level. Integrated scheduling decisions are then made offline and transmitted to an online control problem. Bottom-up approaches focus on embedding economic considerations in the formulation of the control problem and extending its prediction horizon, giving rise to economic model predictive control (EMPC). Conceptually, such strategy would result in an overall optimal operation. However, the incorporation of binary decision variables, and the online solution of complex nonlinear problem using scheduling-length prediction horizons remain open challenges for realistic size problems.
Finally, the computational effort can also be reduced by identifying reduced order models to represent the dynamic
Some initial efforts related to the implementation of EMPC in intensified systems have been realized in the
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4 Process systems engineering: process intensification
work of Haßkerl et al. [13]. In this work, the authors discuss the implementation of an economic motivated control of a multi-product transesterification reaction performed in a reactive distillation process. The process model, the specification of the economics optimizing controller, the implementation of the dynamic optimization, and the robustification of the controller by a multistage approach are discussed using simulation studies. The authors conclude that the optimization of the economic performance through an economic motivated control is a promising approach that has several advantages over classical control methodologies. Furthermore, they demonstrated how the available computational methods can solve large and rigorous DAE models in reasonable times, bringing the conceptual frameworks closer to practice and implementation in an industrial setting. Top-down approaches for the integrated scheduling and control have not been discusses for intensified processes in the open literature. Nevertheless, the recent promising results of the conceptual studies of such strategy in regular process operations are worth mentioning. In particular, an interesting application involves the integration of scheduling and control of air separation units (ASU) operating under real-time electricity pricing [40–42]. In this case study, the dynamic behavior of prices drives the need for the transient operation of a cryogenic distillation column in order to achieve optimal, economical and efficient operation. The transient behavior is translated to a constant change of setpoints, that are determined by the scheduling problem, while taking into account demand specifications and electricity prices. Optimal schedules can only be determined if the transient behavior is considered at the scheduling level, and therefore integration of scheduling and control becomes essential. The studies report significant cost reductions when compared to standard and nominal operation of the separation process. In the process intensification field, the need for integrated scheduling and control arises not only from exogenous factors, such as price fluctuations and raw material variability, but also from the specific nature of intensified processes. Processes that make use of periodic operation, such as pressure swing adsorption and moving bed reactor will benefit from the integration of scheduling and control, as well as multi-product reactive distillation columns. The integration of operation and control can actually provide a paradigm shift in the operation of intensified processes. Much like the air separation problem, intensified processes may benefit from the operation in a transient regime, and the system nonlinearities can be exploited to reach favorable operating profiles. Overall, the integration of scheduling and control can expand the operating windows of intensified processes, contributing to the ultimate goal of process intensification, the achievement of cleaner, safer and more energy efficient operations. Current Opinion in Chemical Engineering 2018, 22:1–5
Conclusions Process Intensification and Process Systems Engineering are moving together in the pursue of efficient manufacturing. Recent advances in PSE are naturally enabling advances in PI, and the modeling, simulation and control of intensified processes is becoming feasible. Nevertheless, significant challenges still need to be overcome, specially related to large-size and highly nonlinear dynamic behaviors. In this article, some promising areas that are providing tools and concepts to confront these challenges have been identified. The continuous efforts on the development of parallel computing, software and algorithms for nonlinear programming are enabling the efficient implementation of nonlinear controllers. Reduced order models and multi-parametric programming techniques emerge as alternatives to the online solution of complex nonlinear problems. Recent efforts on the incorporation of artificial intelligence concepts in process control suggest some exciting new venues for research. Finally, the integration among different decision-making stages (moving from control to scheduling decisions) for intensified processes can result in substantial cost savings and improved performance. A variety of challenges in this integration still need to be overcome, including the coupling of different time scales and different objectives into a single, large-scale and complex optimization model. Nevertheless, it should be emphasized that integrated scheduling and control strategies can expand even further the operating windows of intensified processes, bringing us closer to optimality in process operations.
Conflict of interest statement Nothing declared.
Acknowledgement M.G.I. acknowledges financial support from the Food and Drug Administration under grand (DHHS - FDA - 1 U01 FD005295-01) and National Science Foundation under grant CBET 1159244, grant CBET 1839007 and grant CBET 1547171. L.S.D acknowledges financial support from CNPQ - Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico - Brazil under grant 215670/2014-0.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
NM, Huesman AEM, Van den Hof PMJ, Stankiewicz AI: Nika9cevic Opportunities and challenges for process control in process intensification. Chem Eng Process Process Intensif 2012, 52:1-15.
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Stankiewicz AI, Moulijn JA: Process intensification: transforming chemical engineering. Chem Eng Prog 2000, 96:22-34.
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Ponce-Ortega JM, Al-Thubaiti MM, El-Halwagi MM: Process intensification: new understanding and systematic approach. Chem Eng Process Process Intensif 2012, 53:63-75. www.sciencedirect.com
Please cite this article in press as: Dias LS, Ierapetritou MG: Optimal operation and control of intensified processes — challenges and opportunities, Curr Opin Chem Eng (2019), https://doi.org/ 10.1016/j.coche.2018.12.008
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Becht S, Franke R, Geißelmann A, Hahn H: An industrial view of process intensification. Chem Eng Process Process Intensif 2009, 48:329-332.
24. Lozano Santamarı´a F, Go´mez JM: Framework in PYOMO for the assessment and implementation of (as)NMPC controllers. Comput Chem Eng 2016, 92:93-111.
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Van Gerven T, Stankiewicz A: Structure, energy, synergy, time— the fundamentals of process intensification. Ind Eng Chem Res 2009, 48:2465-2474.
25. Huang R, Zavala VM, Biegler LT: Advanced step nonlinear model predictive control for air separation units. J Process Control 2009, 19:678-685.
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Tian Y, Demirel SE, Hasan MMF, Pistikopoulos EN: An overview of process systems engineering approaches for process intensification: state of the art. Chem Eng Process Process Intensif 2018, 133:160-210. This paper provides an outstanding overview of process systems engineering approaches for process intensification, identifying advances in the areas of modeling, simulation, control, synthesis and design of intensified processes.
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Dias LS, Ierapetritou MG: From process control to supply chain management: an overview of integrated decision making strategies. Comput Chem Eng 2017, 106:826-835.
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Brunaud B, Grossmann IE: Perspectives in multilevel decisionmaking in the process industry. Front Eng 2017, 4:256-270.
10. Harmsen GJ: Reactive distillation: the front-runner of industrial process intensification: a full review of commercial applications, research, scale-up, design and operation. Chem Eng Process Process Intensif 2007, 46:774-780. 11. Sharma N, Singh K: Control of reactive distillation column: a review. Int J Chem React Eng 2010, 8. 12. Baldea M: From process integration to process intensification. Comput Chem Eng 2015, 81:104-114. 13. Haßkerl D, Lindscheid C, Subramanian S, Diewald P, Tatulea Codrean A, Engell S: Economics optimizing control of a multiproduct reactive distillation process under model uncertainty. Comput Chem Eng 2018, 118:25-48. This paper introduces economic oriented control in the process intensification field. 14. Kawajiri Y, Biegler LT: Nonlinear programming superstructure for optimal dynamic operations of simulated moving bed processes. Ind Eng Chem Res 2006, 45:8503-8513. 15. Agarwal A, Biegler LT, Zitney SE: Superstructure-based optimal synthesis of pressure swing adsorption cycles for precombustion CO2 capture. Ind Eng Chem Res 2010, 49: 5066-5079. 16. Hasan MMF, Baliban RC, Elia JA, Floudas CA: Modeling, simulation, and optimization of postcombustion CO2 capture for variable feed concentration and flow rate. 2. Pressure swing adsorption and vacuum swing adsorption processes. Ind Eng Chem Res 2012, 51:15665-15682. 17. Fu Q, Yan H, Shen Y, Qin Y, Zhang D, Zhou Y: Optimal design and control of pressure swing adsorption process for N2/CH4 separation. J Clean Prod 2018, 170:704-714. 18. Biegler LT: New nonlinear programming paradigms for the future of process optimization. Aiche J 2017, 63:1178-1193. This paper explore paradigms for process optimization that promises significant advances in the implementation of optimization in the engineering workplace. 19. Biegler LT: New directions for nonlinear process optimization. Curr Opin Chem Eng 2018, 21:32-40. 20. Biegler LT: Advanced optimization strategies for integrated dynamic process operations. Comput Chem Eng 2018, 114:3-13. 21. Amrit R, Rawlings JB: Nonlinear Model Predictive Control Tools (NMPC Tools). Online Report. 2008. tulea-Codrean A, Schoppmeyer C, Engell S: An 22. Lucia S, Ta environment for the efficient testing and implementation of robust NMPC. 2014 IEEE Conference on Control Applications (CCA) 8–10 Oct. 2014. 2014:1843-1848. tulea-Codrean A, Schoppmeyer C, Engell S: Rapid 23. Lucia S, Ta development of modular and sustainable nonlinear model predictive control solutions. Control Eng Pract 2017, 60:51-62.
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26. Nicholson B, Siirola JD, Watson J-P, Zavala VM, Biegler LT: pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations. Math Program Comput 2018, 10:187-223. 27. Bemporad A, Bozinis NA, Dua V, Morari M, Pistikopoulos EN: Model predictive control: a multi-parametric programming approach. In Computer Aided Chemical Engineering, vol 8. Edited by Pierucci S. Elsevier; 2000:301-306. 28. Pistikopoulos E: Perspectives in multiparametric programming and explicit model predictive control. Aiche J 2009, 55:1918-1925. 29. Khajuria H, Pistikopoulos EN: Dynamic modeling and explicit/ multi-parametric MPC control of pressure swing adsorption systems. J Process Control 2011, 21:151-163. 30. Khajuria H, Pistikopoulos EN: Optimization and control of pressure swing adsorption processes under uncertainty. Aiche J 2013, 59:120-131. 31. Natarajan S, Lee JH: Repetitive model predictive control applied to a simulated moving bed chromatography system. Comput Chem Eng 2000, 24:1127-1133. 32. Du W, Alkebsi KAM: Model predictive control and optimization of vacuum pressure swing adsorption for carbon dioxide capture. Advanced Control of Industrial Processes (AdCONIP), 2017 6th International Symposium on: IEEE. 2017:412-417. 33. Agarwal A, Biegler LT, Zitney SE: Simulation and optimization of pressure swing adsorption systems using reduced-order modeling. Ind Eng Chem Res 2008, 48:2327-2343. 34. Daoutidis P, Lee JH, Harjunkoski I, Skogestad S, Baldea M, Georgakis C: Integrating operations and control: a perspective and roadmap for future research. Comput Chem Eng 2018, 115:179-184. 35. Lee JH, Shin J, Realff MJ: Machine learning: overview of the recent progresses and implications for the process systems engineering field. Comput Chem Eng 2018, 114:111-121. This paper critically examines the main advances in deep learning, and discusses its role in control and decision problems. 36. Lee JH, Lee JM: Approximate dynamic programming based approach to process control and scheduling. Comput Chem Eng 2006, 30:1603-1618. 37. Bertsekas DP, Tsitsiklis JN: Neuro-dynamic programming: an overview. Proceedings of the 34th IEEE Conference on Decision and Control 1995:560-564. 38. Dias LS, Ierapetritou MG: Integration of scheduling and control under uncertainties: review and challenges. Chem Eng Res Des 2016, 116:98-113. 39. Baldea M, Harjunkoski I: Integrated production scheduling and process control: a systematic review. Comput Chem Eng 2014, 71:377-390. 40. Pattison RC, Touretzky CR, Johansson T, Harjunkoski I, Baldea M: Optimal process operations in fast-changing electricity markets: framework for scheduling with low-order dynamic models and an air separation application. Ind Eng Chem Res 2016, 55:4562-4584. 41. Pattison RC, Touretzky CR, Harjunkoski I, Baldea M: Moving horizon closed-loop production scheduling using dynamic process models. Aiche J 2017, 63:639-651. 42. Dias LS, Pattison RC, Tsay C, Baldea M, Ierapetritou MG: A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units. Comput Chem Eng 2018, 113:139-151.
Current Opinion in Chemical Engineering 2018, 22:1–5
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Available online at www.sciencedirect.com
ScienceDirect Optimal operation and control of intensified processes — challenges and opportunities Lisia S Dias and Marianthi G Ierapetritou Process systems engineering (PSE) tools can be utilized to enable the optimal operation and control of intensified processes. In this work, the challenges in the control of intensified processes are summarized, including the difficulties in modelling and performing online optimization of these highly complex dynamic systems. Nevertheless, PSE progress in the areas of nonlinear programming, reduced-order modeling, development of software tools, parallel computing and artificial intelligence are enabling the implementation and optimization of large-scale intensified systems. Future opportunities for research are identified, including the simultaneous optimization of scheduling of operation and control, a promising strategy that has proven its value in regular operations, and can expand the operating windows of intensified processes. Address Dept. of Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08854, United States Corresponding author: Ierapetritou, Marianthi G ([email protected])
Current Opinion in Chemical Engineering 2018, 22:xx–yy This review comes from a themed issue on Process systems engineering: process intensification Edited by Jim Bielenberg, Karen Fletcher, Mahmoud El-Hawlwagi, and Ka Ming Ng
https://doi.org/10.1016/j.coche.2018.12.008
waste disposal regulations [2]. In this context, Process Intensification (PI) and Process Systems Engineering (PSE) emerge as tools to address current challenges in the operation and optimization of manufacturing facilities. A general definition for process intensification has been given by AI Stankiewicz and JA Moulijn [3] as “any chemical engineering development that leads to substantially smaller, cleaner, and more energy efficient technology”. Several extensions to this definition have been proposed over the years in order to accommodate the progress and developments of the field [4,5,6]. Recently, Tian et al. [7] summarized activities that results in intensified processes, including the combination of multiple process tasks or equipment into a single unit (e.g. membrane reactors, reactive distillations), the miniaturization of process equipment (e.g. microreactors), the operation of equipment in a periodic manner (e.g. simulated moving bed, pressure adsorption swing), and a tight process integration (e.g. dividing wall distillation). Judging from the growth of research interest in process intensification [1], it is clear that PI is a promising field that can enable a paradigm shift to the process industry, offering novel processing methods and equipment to achieve higher efficiency and safer operation. Nevertheless, challenges concerning the operability and controllability of intensified processes can prevent the successful implementation and optimal operation of such processes in the chemical industry.
2211-3398/ã 2018 Elsevier Ltd. All rights reserved.
Introduction In a highly competitive economic global environment, fomented by variability and extensive information exchange, companies must adapt and embrace changes in order to survive. Manufacturing facilities are required to be more flexible to accommodate the needs of dynamic markets. Frequent variations in raw material compositions, together with prices and demand fluctuations, give much more emphasis on process dynamics [1]. This raises the need for incorporating process dynamics in decisionmaking processes in order to guarantee optimal operations. A more sustainable industrial development should be pursued as environmental pressures increase, including carbon dioxide emission consideration and restrictive www.sciencedirect.com
Process Systems Engineering can contribute to this challenge by providing tools for a systematic approach for the design, optimization, control and operation of intensified processes. Over the years, the PSE community has developed novel representations and models that capture nontrivial features, enabling the simulation of complex processes, advances in process control, and improvements of decision-making processes for the operation of the chemical supply chain [2]. PSE has also contributed to the development of computationally efficient solution methods and software tools for complex optimization problems. Of particular interest to this article, PSE has proposed a representation of the hierarchy of decisionmaking process in the operations and control of a process industry, and has systematically addressed the challenges emerging in each level of the hierarchy [8,9]. Such representation is depicted in Figure 1, and encompasses the problems of planning, scheduling and control of manufacturing facilities. Current Opinion in Chemical Engineering 2018, 22:1–5
Please cite this article in press as: Dias LS, Ierapetritou MG: Optimal operation and control of intensified processes — challenges and opportunities, Curr Opin Chem Eng (2019), https://doi.org/ 10.1016/j.coche.2018.12.008
COCHE-511; NO. OF PAGES 5
2 Process systems engineering: process intensification
Figure 1
Then, the integration among different decision-making stages (moving from control to scheduling decisions) are investigated. Interesting results from classical operations are presented, in the hopes that they will instigate efforts from the PI and PSE communities in exploring this direction. The integration of scheduling and control can be seen as the natural ‘next step’ on the optimization of intensified operations, and a new tool to guarantee efficient and clean manufacturing.
PLANNING Planning horizon, PH t=1
t=2
t=M
Detailed Scheduling
…
Production Initial Inventory
Production
Production
… Inventory
Inventory Demand
Demand
Inventory
Production Targets
SCHEDULING Scheduling horizon, SH = t k=1
k=2
k=N …
Process Control
Machine 1 Machine 2 Machine 3
Sequence, setpoints
CONTROL Task i event point n x
V unit j
. . .
k
k+1
u t
Event Point in scheduling
unit n Process Control
V: amount of processing material x: state variable u: manipulated variable
Current Opinion in Chemical Engineering
Decision-making hierarchy in process operations.
In this article, an analysis of the tools and systematic approaches developed by the PSE community regarding the optimization of the decision-making processes in intensified processes is performed. In particular, an investigation of scheduling and control tools and its performance on intensified systems is conducted. First, ongoing efforts on the operability and control of PI systems are briefly reviewed. Challenges in the control of intensified process are summarized, and opportunities for further enhancement of the control of PI systems are identified. Current Opinion in Chemical Engineering 2018, 22:1–5
Control of intensified process Process control can enable the safe, economical, and environmentally optimal performance of intensified processes. This capability has been demonstrated with a special focus on reactive distillation columns, which has received wide acceptance in the chemical industry [10]. An excellent overview of process control in process intensification has been presented in Nika9cevic et al. [1]. In this paper, the authors demonstrate how the majority of current research in control of intensified processes is oriented towards advanced-model and optimization-based control. They also highlight challenges that are prevalent in the field of process control, as well as challenges that are specific to the control of intensified processes. Here, some of the challenges faced by the PSE and PI communities in controlling intensified process using advanced control techniques are summarized:
Demand
The implementation of model-based control requires extensive efforts on modeling and parameter tuning, which is usually expensive and time consuming. Process models can be obtained either by rigorous first principle modeling or by system identification techniques. On one hand, mathematical modelling requires expertise and intensive experimental validation; on the other hand, models identified from experimental data are usually suited for a limited operating range and may fail to capture crucial relationships between process variables The dynamic behavior of intensified processes is usually complex and highly nonlinear due to the integration of multiple physical and chemical phenomena into single, smaller device. Reactive distillation columns, for example, feature steady-state multiplicity, complex interactions between the vapor-liquid equilibrium and great degree of nonlinearity [11]. Such complex dynamic behavior makes intensified processes less suitable for online applications and requires further developments of nonlinear model predictive control (NMPC) and multi-parametric MPC. Process intensified systems feature a reduced number of degrees of freedom. Such affirmation can be argued intuitively, given that, when unit operations are performed in distinctive equipment, there are more possibilities for measurement and manipulation of streams www.sciencedirect.com
Please cite this article in press as: Dias LS, Ierapetritou MG: Optimal operation and control of intensified processes — challenges and opportunities, Curr Opin Chem Eng (2019), https://doi.org/ 10.1016/j.coche.2018.12.008
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Challenges and opportunities Dias and Ierapetritou 3
in between units. This intuitive concept has also been rigorously proved in Baldea [12]. Evidently, process intensification raises challenges in operation and control. Fortunately, significant progresses made on operability, controllability and optimization can foster the implementation of PI systems. Numerous efforts have been conducted on the simulation, modelling and control of reactive distillation [13], simulated moving bed processes [14], pressure swing adsorption [15,16,17], amongst other intensified processes. An excellent overview of state-of-the-art approaches for the modeling, simulation and synthesis of several representative PI technologies was given by Tian et al. [7]. The variety of works is outstanding and has significantly improved the understanding of intensified systems. To address the challenges related to the online implementation of NMPC, several algorithms and optimization software have been developed in the last three decades. The most efficient nonlinear programming tools now handle millions of variables and constraints with modest computational effort. Complex nonlinear process models can be optimized fast enough for online purposes, and dynamic optimization problems can be solved efficiently and reliably [18,19,20]. Furthermore, a variety of software tools have been recently developed in the academia facilitating the implementation of nonlinear MPC, such as NMPC tools [21], DO-MPC [22,23], and a software developed by Santamarı´a and Go´mez [24] for the implementation of NMPC and advanced-step NMPC [25]. Finally, an open-source modeling and discretization framework for optimization with differential and algebraic equations has been recently presented by Nicholson et al. [26], enabling the implementation of generalized frameworks in a high-level programming language. To address the remaining challenges related to large-scale problems, continuous efforts on the development of parallel computing, software and algorithms should be encouraged.
behavior of the system. Such strategy has been implemented in intensified processes for the control of simulated moving beds [31] and pressure swing adsorption [32,33]. The advances mentioned so far encompass traditional tools in the process systems engineering field that are enabling optimal control of intensified processes. A less explored direction includes the incorporation of artificial intelligence in the decision-making hierarchy, which promise to enable the handling of complex processes that were once thought to be out of reach [34]. Such effort requires a shift of the optimization-based paradigm in PSE. However, it may bring interesting solutions to complex problems such as the control and operation of PI systems, especially with new fundamental advances in machine learning presenting significant new technological and commercial opportunities [35]. Some works along this direction have already appeared, such as the use of neural networks and nonparametric approximators in control problems [36,37].
Integration of scheduling and control Scheduling of operations is a level above process control in the decision-making hierarchy. Scheduling can be defined as the problem of allocating resources, defining production sequences, equipment usage and assignment of tasks in an industrial plant, in order to achieve production targets while minimizing productions costs or production makespan. Such problems have been traditionally solved independently of the dynamic behavior of the system, although the solution of scheduling problems in usually implemented by the control level. However, recent studies and developments in PSE show that a tighter relation and simultaneous solution of scheduling and control problems can result in more efficient and economical operations [38].
The computational complexity of model predictive controllers can be reduced with the implementation of multiparametric mp-MPC, which enables the offline solution of MPC problem by reformulating it into a multiparametric optimization problem and obtaining explicit control laws [27,28]. Such strategy has been applied to pressure swing adsorption systems by Khajuria and Pistikopoulos [29] and extended in Khajuria and Pistikopoulos [30]. Multi-parametric programming presents its challenges related to the exponential growth of critical regions and the number of dimensions increase. Nevertheless, it is a promising alternative for the online implementation of MPC.
Research attempts to achieve the integration of scheduling and control so far have followed two main paradigms: top-down and bottom-up approaches [39]. Top-down approaches focus on transmitting detailed dynamic behavior of the system to the scheduling problem, such that more informed and feasible decisions are made at the scheduling level. Integrated scheduling decisions are then made offline and transmitted to an online control problem. Bottom-up approaches focus on embedding economic considerations in the formulation of the control problem and extending its prediction horizon, giving rise to economic model predictive control (EMPC). Conceptually, such strategy would result in an overall optimal operation. However, the incorporation of binary decision variables, and the online solution of complex nonlinear problem using scheduling-length prediction horizons remain open challenges for realistic size problems.
Finally, the computational effort can also be reduced by identifying reduced order models to represent the dynamic
Some initial efforts related to the implementation of EMPC in intensified systems have been realized in the
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4 Process systems engineering: process intensification
work of Haßkerl et al. [13]. In this work, the authors discuss the implementation of an economic motivated control of a multi-product transesterification reaction performed in a reactive distillation process. The process model, the specification of the economics optimizing controller, the implementation of the dynamic optimization, and the robustification of the controller by a multistage approach are discussed using simulation studies. The authors conclude that the optimization of the economic performance through an economic motivated control is a promising approach that has several advantages over classical control methodologies. Furthermore, they demonstrated how the available computational methods can solve large and rigorous DAE models in reasonable times, bringing the conceptual frameworks closer to practice and implementation in an industrial setting. Top-down approaches for the integrated scheduling and control have not been discusses for intensified processes in the open literature. Nevertheless, the recent promising results of the conceptual studies of such strategy in regular process operations are worth mentioning. In particular, an interesting application involves the integration of scheduling and control of air separation units (ASU) operating under real-time electricity pricing [40–42]. In this case study, the dynamic behavior of prices drives the need for the transient operation of a cryogenic distillation column in order to achieve optimal, economical and efficient operation. The transient behavior is translated to a constant change of setpoints, that are determined by the scheduling problem, while taking into account demand specifications and electricity prices. Optimal schedules can only be determined if the transient behavior is considered at the scheduling level, and therefore integration of scheduling and control becomes essential. The studies report significant cost reductions when compared to standard and nominal operation of the separation process. In the process intensification field, the need for integrated scheduling and control arises not only from exogenous factors, such as price fluctuations and raw material variability, but also from the specific nature of intensified processes. Processes that make use of periodic operation, such as pressure swing adsorption and moving bed reactor will benefit from the integration of scheduling and control, as well as multi-product reactive distillation columns. The integration of operation and control can actually provide a paradigm shift in the operation of intensified processes. Much like the air separation problem, intensified processes may benefit from the operation in a transient regime, and the system nonlinearities can be exploited to reach favorable operating profiles. Overall, the integration of scheduling and control can expand the operating windows of intensified processes, contributing to the ultimate goal of process intensification, the achievement of cleaner, safer and more energy efficient operations. Current Opinion in Chemical Engineering 2018, 22:1–5
Conclusions Process Intensification and Process Systems Engineering are moving together in the pursue of efficient manufacturing. Recent advances in PSE are naturally enabling advances in PI, and the modeling, simulation and control of intensified processes is becoming feasible. Nevertheless, significant challenges still need to be overcome, specially related to large-size and highly nonlinear dynamic behaviors. In this article, some promising areas that are providing tools and concepts to confront these challenges have been identified. The continuous efforts on the development of parallel computing, software and algorithms for nonlinear programming are enabling the efficient implementation of nonlinear controllers. Reduced order models and multi-parametric programming techniques emerge as alternatives to the online solution of complex nonlinear problems. Recent efforts on the incorporation of artificial intelligence concepts in process control suggest some exciting new venues for research. Finally, the integration among different decision-making stages (moving from control to scheduling decisions) for intensified processes can result in substantial cost savings and improved performance. A variety of challenges in this integration still need to be overcome, including the coupling of different time scales and different objectives into a single, large-scale and complex optimization model. Nevertheless, it should be emphasized that integrated scheduling and control strategies can expand even further the operating windows of intensified processes, bringing us closer to optimality in process operations.
Conflict of interest statement Nothing declared.
Acknowledgement M.G.I. acknowledges financial support from the Food and Drug Administration under grand (DHHS - FDA - 1 U01 FD005295-01) and National Science Foundation under grant CBET 1159244, grant CBET 1839007 and grant CBET 1547171. L.S.D acknowledges financial support from CNPQ - Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico - Brazil under grant 215670/2014-0.
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Current Opinion in Chemical Engineering 2018, 22:1–5
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