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Mathematical process modelling past, present and future – a personal perspective
Anton A. Kiss, Edwin Zondervan, Richard Lakerveld, Leyla Özkan (Eds.) Proceedings of the 29th European Symposium on Computer Aided Process Engineering June 16th to 19th, 2019, Eindhoven, The Netherlands. © 2019 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/B978-0-12-818634-3.50022-9
Mathematical process modelling past, present and future – a personal perspective J.P. Schmal Process Systems Enterprise Inc., 4 Century Drive, Ste 130, Parsippany, NJ, 07054, USA1 [email protected]
Abstract Progress in mathematical process modelling has been tied primarily to hardware developments and secondarily to better software, advanced algorithms and improved data availability. In this paper we review modelling history to extract some trends for the future. Modelling will play an increasingly important role in process industries. Historically separate techniques such as flowsheeting, custom modelling and CFD will likely merge as computational power increases. Initially parallel computing will give the needed additional computation power. Neural nets are expected to play a role in empirical models and analysis. We need to move from storing data to storing useful information. Keywords: modelling, digitalization, big data, history.
1. Introduction Mathematical process modelling has been around for many decades, but has evolved significantly with the advent of more powerful computers, better algorithms and the availability of more data. To help understand where mathematical process modelling may go in the future, we need to understand the past and the present. This paper represents a personal view and is based on historic events and personal experiences and beliefs. In a short paper, it is impossible to do justice to all people that contributed to these areas and to cover the history in the detail it deserves. The goal is to abstract general trends in the field and use them to look ahead. Modelling technology encompasses four main elements: 1) modelling approaches, 2) software & algorithms, 3) hardware and 4) data. We will cover the status of each of these elements. For modelling approaches, we will cover custom modelling, flowsheeting, CFD and neural networks.
2. Past 2.1. Pre-1950 Process modelling has been around for a very long time. Initially the models needed to be solved analytically due to a lack of computers. The art of modelling was therefore to try and find a problem that was as close as possible to reality, yet could be solved analytically. As a consequence many simplifying assumptions were needed to make the 1
Per May 1st 2019: ExxonMobil, 1545 US-22, Annandale, NJ 08801, USA
128
J.P. Schmal
problem solvable. Several techniques were employed to get general solutions that could be used for different cases. For example, dimensionless numbers were extensively used to reduce the number of variables and allow for plotting solutions in 2-D. Dynamic behaviour was studied with the help of bifurcation studies, using the steady-state as a demarcation between regions of different dynamic behaviour (Aris, 1994). These general solutions or solutions to certain equations would often be published in books. Process control was all manual and measurements were virtually non-existent. Chemical processes were run based on experience and by trial and error. 2.2. 1950-2018 With the advent of digital computers and the introduction of software, many areas such as modelling, control and neural networks evolved rapidly. Process modelling could relax some of the simplifying assumptions. One of the earliest industrial applications was by Sargent (1958) using a punch card computer to design a distillation column and a multistream heat-exchanger. Steady-state models were difficult to solve and dynamic models were used to find the steady-state as they were easier to initialise. Initial computations could only use limited memory on what were slow computers, but memory, CPU clock speed and hard drive space grew rapidly. This allowed for ever more complex and realistic problems to be solved as demonstrated in Figure 1.
Figure 1: Historic size of RAM and hard drive (created with http://organdi.net/article.php3?id_article=82, https://en.wikipedia.org/wiki/History_of_supercomputing and personal experience)
data
from
Besides hardware improvements, advancements in several other areas were equally important to progress in modelling. New simulation-specific software, advanced algorithms and improved data availability all contributed to the development of more detailed and accurate models. In 1957, Fortran (FORmula TRANslation) was developed and it allowed for easy implementation of equations. It is worth mentioning Matlab, whose first commercial version became available in 1985, Python, whose first version became available in 1980 and excel (1985; first spreadsheet was developed in 1969). Matlab, Python and excel have found wide spread use in the engineering community. With regards to algorithms for sets of equations, broadly speaking two types of solutions were considered: sequential modular and simultaneous or equation-oriented. Sequential modular solvers were prominent in flowsheeting tools while simultaneous solvers were
Mathematical process modelling past, present and future
129
more often used in custom modelling tools. In the sequential modular approach the structure of the problem is exploited and each process unit is solved in turn. This requires the inputs to the units to be known or given. Back to front calculations or including recycles and/or heat-integration is difficult or requires intermediate steps and/or iterations through the loop. Equation-oriented solvers, solve all equations simultaneously, allowing for back to front calculations, easier solution of recycles and heat-integration. Because equation-oriented solvers are Newton-based solvers, good initial guesses are required. Process Systems Enterprise developed automated initialization procedures to get these good initial guesses. As all equations are solved simultaneously, debugging a problem is usually less straightforward than in sequential modular approaches. Gear was one of the key pioneers in solving sets of Differential-Algebraic equations (DAEs). In 1971 he introduced a multi-step, backward difference formula (BDF) method. Petzold (1982) developed DASSL, which is at the basis of many of today’s BDF solvers. In 1989 the semi-implicit Runge-Kutta method, a one-step method, was introduced and the rivalry between BDF and Runge-Kutta caused significant improvements in both solvers. The interest in partial differential equations bloomed in 1996. Although many algorithmic improvements have been made in linear algebra (e.g. MA28 in 1977 & MA48 in 1996), non-linear solvers and integration solvers, the next logical avenue for speed an capability improvements was the exploitation of parallelization. With regards to software we will briefly consider Computational Fluid Dynamic (CFD), Flowsheeting, custom modelling and neural networks. 2-D CFD solutions were available as early as 1930 and the first CFD calculations using finite differences were done on ENIAC in 1940, one of the world’s first computers. In 1957 Los Alamos solved the first 3D CFD calculations with first publications by Douglas aircraft and Boeing in 1967 and 1968 respectively. Unsteady aerodynamic flows were first successfully solved in the 1970’s. Two-phase flow and reacting media were developed in the early 1990’s until today. The first flowsheeting tool was developed in 1959 by Monsanto and was called Flowtran. They later participated in the Aspen project with MIT and DOE, which led to first version of Aspen in 1981. Two other notable developments for flowsheeting tools are the development of PRO/II by SimSci which was established in 1967 and later became part of AVEVA and CHESS developed in 1968 at the University of Houston and the US Navy, which became Chemcad in 1985. In 1996 Hyprotech, a company spun out of the University of Calgary, developed HYSYS, which was sold to Aspen and then divested due to the fact it was anticompetitive and bought by Honeywell. Some of the developers of HYSYS also developed Unisim in 2005. Most flowsheeting tools are sequential modular, but Aspen EO, ROMeo, gPROMS ProcessBuilder and gPROMS FormulatedProducts are examples of equation-oriented flowsheeting tools. Hundreds of tools were developed for custom modelling (able to solve general integralpartial-differential-algebraic equations, IPDAE), but two key developments were SPEEDUP in 1964 developed by Sargent and Westerberg. SPEEDUP was sold to Aspen and later became Aspen Custom Modeller. Pantelides and Barton developed gPROMS in 1993 and Process Systems Enterprise was set-up to commercialize gPROMS in 1997. Neural networks (NN) were first developed in 1943 where electrical networks were used to represent the neural network. In 1950 IBM had its first successful neural network and in 1957 Cornell university developed percepton for pattern recognition. In 1959 Stanford used NN to remove echoes in telephone calls, but in 1969 a critical paper proved that
130
J.P. Schmal
percepton could not solve the “exclusive or” problem. As a consequence the interest in NN dissipated until 1982 when multi-layer NN were introduced. In 1989 feedforward was introduced, allowing for any function to be implemented in an NN. In 1995 another dead period started since people were unable to teach NN how to play chess. In 2006 the interest in NN rebounded by a rebranding to deep learning. Research led to a couple of key innovations with regards to initialization of the weights used to indicate the influence of each node in the previous layer on a node in the current layer. The weights traditionally were set randomly, requiring huge data sets to “train” the NN. The advent of big data and the use of GPUs (Graphical Processing Units) which sped up calculations drastically, allowed NN to become powerful (e.g. Watson, Google, Amazon etc.). With the advent of computers and software to simulate complex models, the techniques of optimisation, parameter estimation and design of experiments became readily available. This in turn led to a need for data to estimate unknown or uncertain parameters and optimize the design or operation. In the early days measurements were analogue, often making use of pneumatic measurements, typically a pen moving up and down while a piece of paper was moving underneath it. This meant data had to be transcribed from the scrolls into usable forms. For example, the first electrical signal pressure measurements became available in the 1930’s, but the first digital pressure sensor was developed by Honeywell in 1967 (http://www.sensorland.com/HowPage059.html). With the digitalization of operations more sensors were introduced in plants and the sensors were digital, meaning the data was easily accessible. The OPC, OLE (Microsoft Object Linking and Embedding) for Process Control) standard was introduced in 1995 and in 2001 the Historical Data Access (HDA) was introduced. As an example of this change, in 1994 during my internship at Hoechst, the unit I was modelling only had a few measurements (~25), while during my Ph.D. from 1999-2003, I modelled a large petrochemical plant from Shell and 2300 sensors were available, (350 used in the modelling).
3. Present Present capabilities include among many others: Flowsheets of connected gas processing plants (Aluma et al., 2016), combined product and process design (Martin and Martinez, 2013), coupled DEM (discrete element models), custom multi-scale models of wet granulation (Barrasso et al. 2015), three-phase CFD simulations of units and neural nets that control more and more parts of our daily life. Tsay et al. (2018) give an overview of optimal process design capabilities and practices. They interviewed 110 industrial people and found that there is limited adoption of optimization and modelling tools. With regards to hardware there is a clear trend towards parallelization and cloud computing as the improvements of a single core seem to be flattening off. As an example we ran the same large model using the same software version on a high end i7 machine in 2012 taking 156 s CPU time, while on a high end Xeon machine in 2017 it took 105 s CPU time (~32% improvement). During this same time period we have achieved speed improvements of a factor of 3 for large models due to algorithmic improvements. With parallelization we have managed to achieve speed increases of close to a factor of 1000. The latter, is case dependent and not generally applicable however. Data standards, integration and increased hard disc space have led to much wider availability of data for analysis and training of neural nets. The fact that chemical plants
Mathematical process modelling past, present and future
131
typically operate in a narrow operating window makes using neural nets both easier and harder: easier because it only needs to cover a limited number of possibilities and harder because it also means the information content in the data is limited and there is a danger of training the neural net how to predict noise. On-line applications of models both linear and non-linear are common and models are used as soft sensors for unmeasurable quantities and phenomena such as coking and fouling. Advanced model-based control and optimization is applied in many different industries and on hundreds of plants world-wide.
4. Future All we can do is consider different alternative scenarios and discuss their likelihood. It is clear from history (see Figure 2) that we have always tried to get the most out of the hardware and software. We always have been able to add complexity faster than hardware has been able to grow. The current digitalization trend is expected to increase the use of models both off-line and on-line. As such model-based engineering is expected to be a standard practice (e.g. see INCOSE vision 2025). In on-line models the use of more detailed models is now possible due to robust and fast solvers.
Figure 2: Summary of history
With regards to hardware, the trend we see towards cloud computing and parallelization most likely will continue and it seems it is the most secure way to get more computation power. There are still new developments in miniaturization allowing for more power. The development of the quantum computer is still progressing and has been for a long time (since 1982), chances of a major breakthrough in the next decade seem unlikely. Although progress is steady, the algorithms that can benefit from quantum computing are still limited. A breakthrough in quantum computing would be a disruptive event. With the availability of more computational power, two trends most likely will continue: bigger envelops/systems and more detailed models. At the moment these bigger problems are solved using relatively simple models. Using more detailed models will require more computational power and bring additional benefits. The level of detail will also increase due to software like CFD, flowsheeting and custom modelling merging into a single tool.
132
J.P. Schmal
Algorithms will keep on improving and there is still a lot of room for improvement in areas such as global mixed-integer non-linear programming. The application of neural nets will increase, but research interest (measured by hits in Google scholar) shows a decline in papers in this area. Neural nets can play an important role in cases where firstprinciples are not well-known or in areas such as data analytics. Compared to nuclear physics, data analytics in chemical engineering can be improved upon. Also, techniques that combine information from signals to get a better overall picture (similar to PAT, Process Analytical Technology) can be improved. Finally, data is simply stored for a certain time and later simply deleted. With the increased availability of data it most likely will be necessary to extract useful information from the data to keep data storage requirements under control and the use of this data manageable.
5. Conclusions Progress in hardware and software has driven mathematical process modelling and will continue to do so in the future. Single core hardware capabilities are flattening out, but parallelization and cloud-based solutions will allow us to keep expanding. A potentially disruptive technology of quantum computing is not expected to be available to engineers any time soon. Algorithm development has continued steadily and there is still room for improvements. With the availability of more data the application of neural nets will likely grow, but research interest seems to slow down. Neural nets will likely play a role in more traditional roles of pattern recognition for empirical elements of models, raw data analysis and generated data analysis. With the additional availability of data there is room for better analysis techniques such as PAT and a need for smarter storage, e.g. extracting the useful information before storing.
References D.Aluma, N. Thijssen, K.M. Nauta, C.C. Pantelides, N.Shah, 2016, Optimize an integrated natural gas production and distribution network, Gas processing & LNG, October D. Barrasso, T. Eppinger, F.E. Pereira, R. Algave, K. Debus, S.K. Bermingham and R. Ramachandran, 2015, A multi-scale, mechanistic model of a wet granulation process using a novel bi-directional PBM/DEM coupling algorithm, Chem. Eng. Sc., 123, 500-513 R. Aris, 1994, Mathematical modelling techniques, General Publishing Company Ltd. INCOSE vision 2025, 2014, A world in motion- systemengineering vision 2025, https://www.incose.org/docs/default-source/aboutse/se-vision-2025.pdf?sfvrsn=b69eb4c6_4 M. Martin and A. Martinez, 2013, A methodology for simultaneous product and process design in the customer products industry: The case study of the laundry business, Comput.Chem. Engng., 32, 715-720 L. Petzold, 1982, A description of DASSL: A differential/algebraic system solver, IMACS World Congress, August 8-13, Montreal, Canada R.W.H. Sargent, 1958, Applications of an electronic digital computer in the design of low temperature plant, Trans. Instit. Chem. Eng. 36, 201–214 C. Tsay, R.C. Pattison,M.R. Piana and M. Baldea, 2018, A survey of optimal process design capabilities and practices in the chemical and petrochemical industries, Comput. Chem. Engng. 112, 180-189.
Mathematical process modelling past, present and future – a personal perspective J.P. Schmal Process Systems Enterprise Inc., 4 Century Drive, Ste 130, Parsippany, NJ, 07054, USA1 [email protected]
Abstract Progress in mathematical process modelling has been tied primarily to hardware developments and secondarily to better software, advanced algorithms and improved data availability. In this paper we review modelling history to extract some trends for the future. Modelling will play an increasingly important role in process industries. Historically separate techniques such as flowsheeting, custom modelling and CFD will likely merge as computational power increases. Initially parallel computing will give the needed additional computation power. Neural nets are expected to play a role in empirical models and analysis. We need to move from storing data to storing useful information. Keywords: modelling, digitalization, big data, history.
1. Introduction Mathematical process modelling has been around for many decades, but has evolved significantly with the advent of more powerful computers, better algorithms and the availability of more data. To help understand where mathematical process modelling may go in the future, we need to understand the past and the present. This paper represents a personal view and is based on historic events and personal experiences and beliefs. In a short paper, it is impossible to do justice to all people that contributed to these areas and to cover the history in the detail it deserves. The goal is to abstract general trends in the field and use them to look ahead. Modelling technology encompasses four main elements: 1) modelling approaches, 2) software & algorithms, 3) hardware and 4) data. We will cover the status of each of these elements. For modelling approaches, we will cover custom modelling, flowsheeting, CFD and neural networks.
2. Past 2.1. Pre-1950 Process modelling has been around for a very long time. Initially the models needed to be solved analytically due to a lack of computers. The art of modelling was therefore to try and find a problem that was as close as possible to reality, yet could be solved analytically. As a consequence many simplifying assumptions were needed to make the 1
Per May 1st 2019: ExxonMobil, 1545 US-22, Annandale, NJ 08801, USA
128
J.P. Schmal
problem solvable. Several techniques were employed to get general solutions that could be used for different cases. For example, dimensionless numbers were extensively used to reduce the number of variables and allow for plotting solutions in 2-D. Dynamic behaviour was studied with the help of bifurcation studies, using the steady-state as a demarcation between regions of different dynamic behaviour (Aris, 1994). These general solutions or solutions to certain equations would often be published in books. Process control was all manual and measurements were virtually non-existent. Chemical processes were run based on experience and by trial and error. 2.2. 1950-2018 With the advent of digital computers and the introduction of software, many areas such as modelling, control and neural networks evolved rapidly. Process modelling could relax some of the simplifying assumptions. One of the earliest industrial applications was by Sargent (1958) using a punch card computer to design a distillation column and a multistream heat-exchanger. Steady-state models were difficult to solve and dynamic models were used to find the steady-state as they were easier to initialise. Initial computations could only use limited memory on what were slow computers, but memory, CPU clock speed and hard drive space grew rapidly. This allowed for ever more complex and realistic problems to be solved as demonstrated in Figure 1.
Figure 1: Historic size of RAM and hard drive (created with http://organdi.net/article.php3?id_article=82, https://en.wikipedia.org/wiki/History_of_supercomputing and personal experience)
data
from
Besides hardware improvements, advancements in several other areas were equally important to progress in modelling. New simulation-specific software, advanced algorithms and improved data availability all contributed to the development of more detailed and accurate models. In 1957, Fortran (FORmula TRANslation) was developed and it allowed for easy implementation of equations. It is worth mentioning Matlab, whose first commercial version became available in 1985, Python, whose first version became available in 1980 and excel (1985; first spreadsheet was developed in 1969). Matlab, Python and excel have found wide spread use in the engineering community. With regards to algorithms for sets of equations, broadly speaking two types of solutions were considered: sequential modular and simultaneous or equation-oriented. Sequential modular solvers were prominent in flowsheeting tools while simultaneous solvers were
Mathematical process modelling past, present and future
129
more often used in custom modelling tools. In the sequential modular approach the structure of the problem is exploited and each process unit is solved in turn. This requires the inputs to the units to be known or given. Back to front calculations or including recycles and/or heat-integration is difficult or requires intermediate steps and/or iterations through the loop. Equation-oriented solvers, solve all equations simultaneously, allowing for back to front calculations, easier solution of recycles and heat-integration. Because equation-oriented solvers are Newton-based solvers, good initial guesses are required. Process Systems Enterprise developed automated initialization procedures to get these good initial guesses. As all equations are solved simultaneously, debugging a problem is usually less straightforward than in sequential modular approaches. Gear was one of the key pioneers in solving sets of Differential-Algebraic equations (DAEs). In 1971 he introduced a multi-step, backward difference formula (BDF) method. Petzold (1982) developed DASSL, which is at the basis of many of today’s BDF solvers. In 1989 the semi-implicit Runge-Kutta method, a one-step method, was introduced and the rivalry between BDF and Runge-Kutta caused significant improvements in both solvers. The interest in partial differential equations bloomed in 1996. Although many algorithmic improvements have been made in linear algebra (e.g. MA28 in 1977 & MA48 in 1996), non-linear solvers and integration solvers, the next logical avenue for speed an capability improvements was the exploitation of parallelization. With regards to software we will briefly consider Computational Fluid Dynamic (CFD), Flowsheeting, custom modelling and neural networks. 2-D CFD solutions were available as early as 1930 and the first CFD calculations using finite differences were done on ENIAC in 1940, one of the world’s first computers. In 1957 Los Alamos solved the first 3D CFD calculations with first publications by Douglas aircraft and Boeing in 1967 and 1968 respectively. Unsteady aerodynamic flows were first successfully solved in the 1970’s. Two-phase flow and reacting media were developed in the early 1990’s until today. The first flowsheeting tool was developed in 1959 by Monsanto and was called Flowtran. They later participated in the Aspen project with MIT and DOE, which led to first version of Aspen in 1981. Two other notable developments for flowsheeting tools are the development of PRO/II by SimSci which was established in 1967 and later became part of AVEVA and CHESS developed in 1968 at the University of Houston and the US Navy, which became Chemcad in 1985. In 1996 Hyprotech, a company spun out of the University of Calgary, developed HYSYS, which was sold to Aspen and then divested due to the fact it was anticompetitive and bought by Honeywell. Some of the developers of HYSYS also developed Unisim in 2005. Most flowsheeting tools are sequential modular, but Aspen EO, ROMeo, gPROMS ProcessBuilder and gPROMS FormulatedProducts are examples of equation-oriented flowsheeting tools. Hundreds of tools were developed for custom modelling (able to solve general integralpartial-differential-algebraic equations, IPDAE), but two key developments were SPEEDUP in 1964 developed by Sargent and Westerberg. SPEEDUP was sold to Aspen and later became Aspen Custom Modeller. Pantelides and Barton developed gPROMS in 1993 and Process Systems Enterprise was set-up to commercialize gPROMS in 1997. Neural networks (NN) were first developed in 1943 where electrical networks were used to represent the neural network. In 1950 IBM had its first successful neural network and in 1957 Cornell university developed percepton for pattern recognition. In 1959 Stanford used NN to remove echoes in telephone calls, but in 1969 a critical paper proved that
130
J.P. Schmal
percepton could not solve the “exclusive or” problem. As a consequence the interest in NN dissipated until 1982 when multi-layer NN were introduced. In 1989 feedforward was introduced, allowing for any function to be implemented in an NN. In 1995 another dead period started since people were unable to teach NN how to play chess. In 2006 the interest in NN rebounded by a rebranding to deep learning. Research led to a couple of key innovations with regards to initialization of the weights used to indicate the influence of each node in the previous layer on a node in the current layer. The weights traditionally were set randomly, requiring huge data sets to “train” the NN. The advent of big data and the use of GPUs (Graphical Processing Units) which sped up calculations drastically, allowed NN to become powerful (e.g. Watson, Google, Amazon etc.). With the advent of computers and software to simulate complex models, the techniques of optimisation, parameter estimation and design of experiments became readily available. This in turn led to a need for data to estimate unknown or uncertain parameters and optimize the design or operation. In the early days measurements were analogue, often making use of pneumatic measurements, typically a pen moving up and down while a piece of paper was moving underneath it. This meant data had to be transcribed from the scrolls into usable forms. For example, the first electrical signal pressure measurements became available in the 1930’s, but the first digital pressure sensor was developed by Honeywell in 1967 (http://www.sensorland.com/HowPage059.html). With the digitalization of operations more sensors were introduced in plants and the sensors were digital, meaning the data was easily accessible. The OPC, OLE (Microsoft Object Linking and Embedding) for Process Control) standard was introduced in 1995 and in 2001 the Historical Data Access (HDA) was introduced. As an example of this change, in 1994 during my internship at Hoechst, the unit I was modelling only had a few measurements (~25), while during my Ph.D. from 1999-2003, I modelled a large petrochemical plant from Shell and 2300 sensors were available, (350 used in the modelling).
3. Present Present capabilities include among many others: Flowsheets of connected gas processing plants (Aluma et al., 2016), combined product and process design (Martin and Martinez, 2013), coupled DEM (discrete element models), custom multi-scale models of wet granulation (Barrasso et al. 2015), three-phase CFD simulations of units and neural nets that control more and more parts of our daily life. Tsay et al. (2018) give an overview of optimal process design capabilities and practices. They interviewed 110 industrial people and found that there is limited adoption of optimization and modelling tools. With regards to hardware there is a clear trend towards parallelization and cloud computing as the improvements of a single core seem to be flattening off. As an example we ran the same large model using the same software version on a high end i7 machine in 2012 taking 156 s CPU time, while on a high end Xeon machine in 2017 it took 105 s CPU time (~32% improvement). During this same time period we have achieved speed improvements of a factor of 3 for large models due to algorithmic improvements. With parallelization we have managed to achieve speed increases of close to a factor of 1000. The latter, is case dependent and not generally applicable however. Data standards, integration and increased hard disc space have led to much wider availability of data for analysis and training of neural nets. The fact that chemical plants
Mathematical process modelling past, present and future
131
typically operate in a narrow operating window makes using neural nets both easier and harder: easier because it only needs to cover a limited number of possibilities and harder because it also means the information content in the data is limited and there is a danger of training the neural net how to predict noise. On-line applications of models both linear and non-linear are common and models are used as soft sensors for unmeasurable quantities and phenomena such as coking and fouling. Advanced model-based control and optimization is applied in many different industries and on hundreds of plants world-wide.
4. Future All we can do is consider different alternative scenarios and discuss their likelihood. It is clear from history (see Figure 2) that we have always tried to get the most out of the hardware and software. We always have been able to add complexity faster than hardware has been able to grow. The current digitalization trend is expected to increase the use of models both off-line and on-line. As such model-based engineering is expected to be a standard practice (e.g. see INCOSE vision 2025). In on-line models the use of more detailed models is now possible due to robust and fast solvers.
Figure 2: Summary of history
With regards to hardware, the trend we see towards cloud computing and parallelization most likely will continue and it seems it is the most secure way to get more computation power. There are still new developments in miniaturization allowing for more power. The development of the quantum computer is still progressing and has been for a long time (since 1982), chances of a major breakthrough in the next decade seem unlikely. Although progress is steady, the algorithms that can benefit from quantum computing are still limited. A breakthrough in quantum computing would be a disruptive event. With the availability of more computational power, two trends most likely will continue: bigger envelops/systems and more detailed models. At the moment these bigger problems are solved using relatively simple models. Using more detailed models will require more computational power and bring additional benefits. The level of detail will also increase due to software like CFD, flowsheeting and custom modelling merging into a single tool.
132
J.P. Schmal
Algorithms will keep on improving and there is still a lot of room for improvement in areas such as global mixed-integer non-linear programming. The application of neural nets will increase, but research interest (measured by hits in Google scholar) shows a decline in papers in this area. Neural nets can play an important role in cases where firstprinciples are not well-known or in areas such as data analytics. Compared to nuclear physics, data analytics in chemical engineering can be improved upon. Also, techniques that combine information from signals to get a better overall picture (similar to PAT, Process Analytical Technology) can be improved. Finally, data is simply stored for a certain time and later simply deleted. With the increased availability of data it most likely will be necessary to extract useful information from the data to keep data storage requirements under control and the use of this data manageable.
5. Conclusions Progress in hardware and software has driven mathematical process modelling and will continue to do so in the future. Single core hardware capabilities are flattening out, but parallelization and cloud-based solutions will allow us to keep expanding. A potentially disruptive technology of quantum computing is not expected to be available to engineers any time soon. Algorithm development has continued steadily and there is still room for improvements. With the availability of more data the application of neural nets will likely grow, but research interest seems to slow down. Neural nets will likely play a role in more traditional roles of pattern recognition for empirical elements of models, raw data analysis and generated data analysis. With the additional availability of data there is room for better analysis techniques such as PAT and a need for smarter storage, e.g. extracting the useful information before storing.
References D.Aluma, N. Thijssen, K.M. Nauta, C.C. Pantelides, N.Shah, 2016, Optimize an integrated natural gas production and distribution network, Gas processing & LNG, October D. Barrasso, T. Eppinger, F.E. Pereira, R. Algave, K. Debus, S.K. Bermingham and R. Ramachandran, 2015, A multi-scale, mechanistic model of a wet granulation process using a novel bi-directional PBM/DEM coupling algorithm, Chem. Eng. Sc., 123, 500-513 R. Aris, 1994, Mathematical modelling techniques, General Publishing Company Ltd. INCOSE vision 2025, 2014, A world in motion- systemengineering vision 2025, https://www.incose.org/docs/default-source/aboutse/se-vision-2025.pdf?sfvrsn=b69eb4c6_4 M. Martin and A. Martinez, 2013, A methodology for simultaneous product and process design in the customer products industry: The case study of the laundry business, Comput.Chem. Engng., 32, 715-720 L. Petzold, 1982, A description of DASSL: A differential/algebraic system solver, IMACS World Congress, August 8-13, Montreal, Canada R.W.H. Sargent, 1958, Applications of an electronic digital computer in the design of low temperature plant, Trans. Instit. Chem. Eng. 36, 201–214 C. Tsay, R.C. Pattison,M.R. Piana and M. Baldea, 2018, A survey of optimal process design capabilities and practices in the chemical and petrochemical industries, Comput. Chem. Engng. 112, 180-189.