Decision support for the development, simulation and optimization of dynamic process models

Abstract

Simulation is besides experimentation the major method for designing, analyzing and optimizing chemical processes. The ability of simulations to reflect real process behavior strongly depends on model quality. Validation and adaption of process models are usually based on available plant data. Using such a model in various simulation and optimization studies can support the process designer in his task. Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation. In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.

Graphical abstract

Keywords

decision support / multicriteria optimization / model validation / dynamic model / sensitivity analysis

References

[1] Mitsos A, Asprion N, Floudas C A, Bortz M, Baldea M, Bonvin D, Caspari A, Schäfer P. Challenges in process optimization for new feedstocks and energy sources. Computers & Chemical Engineering, 2018, 113: 209–221

[2] Biegler L T, Grossmann I E, Westerberg A W. Systematic Methods of Chemical Process Design. Pearson Education (1997)

[3] Asprion N, Bortz M. Process modeling, simulation and optimization: from single solutions to a multitude of solutions to support decision making. Chemieingenieurtechnik (Weinheim), 2018, 90(11): 1727–1738

[4] Bortz M, Burger J, von Harbou E, Klein M, Schwientek J, Asprion N, Böttcher R, Köfer K H, Hasse H. Efficient approach for calculating Pareto boundaries under uncertainties in chemical process design. Industrial & Engineering Chemistry Research, 2017, 56(44): 12672–12681

[5] Asprion N. Modeling, simulation and optimization 4.0 of a distillation column. Chemieingenieurtechnik (Weinheim), 2020, 92(7): 879–889

[6] Asprion N, Böttcher R, Pack R, Stavrou M E, Höller J, Schwientek J, Bortz M. Gray-box modeling for the optimization of chemical processes. Chemieingenieurtechnik (Weinheim), 2019, 91(3): 305–313

[7] Kahrs O, Marquardt W. Incremental identification of hybrid process models. Computers & Chemical Engineering, 2008, 32(4-5): 694–705

[8] Kahrs O, Marquardt W. The validity domain of hybrid models and its application in process engineering. Chemical Engineering and Processing, 2007, 46(11): 1054–1066

[9] Franceschini G, Macchietto S. Model-based design of experiments for parameter precision: state of the art. Chemical Engineering Science, 2008, 63(19): 4846–4872

[10] Bortz M, Burger J, Asprion N, Blagov S, Böttcher R, Nowak U, Scheithauer A, Welke R, Küfer K H, Hasse H. Multi-criteria optimization in chemical process design and decision support by navigation on Pareto sets. Computers & Chemical Engineering, 2014, 60(01): 354–363

[11] Burger J, Asprion N, Blagov S, Böttcher R, Nowak U, Bortz M, Welke R, Küfer K H, Hasse H. Multi-objective optimization and decision support in process engineering—implementation and application. Chemieingenieurtechnik (Weinheim), 2014, 86(7): 1065–1072

[12] Asprion N, Benfer R, Blagov S, Böttcher R, Bortz M, Berezhnyi M, Burger J, Von Harbou E, Küfer K H, Hasse H. INES—interface between experiments and simulation. Chemieingenieurtechnik (Weinheim), 2015, 87(12): 1810–1825

[13] Asprion N, Blagov S, Böttcher R, Schwientek J, Burger J, von Harbou E, Bortz M. Simulation and multi-criteria optimization under uncertain model parameters of a cumene process. Chemieingenieurtechnik (Weinheim), 2017, 89(5): 665–674

[14] Forte E, Von Harbou E, Burger J, Asprion N, Bortz M. Optimal design of laboratory and pilot-plant experiments using multiobjective optimization. Chemieingenieurtechnik (Weinheim), 2017, 89(5): 645–654

[15] Burger J, Asprion N, Blagov S, Bortz M. Simple perturbation scheme to consider uncertainty in equations of state for the use in process simulation. Journal of Chemical & Engineering Data, 2017, 62(1): 268–274

[16] Von Harbou E, Ryll O, Schrabback M, Bortz M, Hasse H. Reactive distillation in a dividing-wall column: model development, simulation, and error analysis. Chemieingenieurtechnik (Weinheim), 2017, 89(10): 1315–1324

[17] Höller J, Bickert P, Schwartz P, Von Kurnatowski M, Kerber J, Künzle N, Lorenz H M, Asprion N, Blagov S, Bortz M. Parameter estimation strategies in thermodynamics. ChemEngineering, 2019, 3(2): 56

[18] Asprion N, Böttcher R, Mairhofer J, Yliruka M, Höller J, Schwientek J, Vanaret C, Bortz M. Implementation and application of model-based design of experiments in a flowsheet simulator. Journal of Chemical & Engineering Data, 2020, 65(3): 1135–1145

[19] Charpentier J C. Among the trends for a modern chemical engineering, the third paradigm: the time and length multiscale approach as an efficient tool for process intensification and product design and engineering. Chemical Engineering Research & Design, 2010, 88(3): 248–254

[20] Bardow A, Steur K, Gross J. Continuous-molecular targeting for integrated solvent and process design. Industrial & Engineering Chemistry Research, 2010, 49(6): 2834–2840

[21] Bortz M, Heese R, Scherrer A, Gerlach T, Runowski T. Estimating mixture properties from batch distillation using semi-rigorous and rigorous models. Computer-Aided Chemical Engineering, 2019, 46: 295–300

[22] Galán S, Feehery W F, Barton P I. Parametric sensitivity functions for hybrid discrete/continuous systems. Applied Numerical Mathematics, 1999, 31(1): 17–47

[23] Nad M, Spiegel L. Simulation of batch distillation by computer and comparison with experiment. Proceedings CEF '87', Computers and Chemical Engineering/EFCE Giardini Naxos, Taormina, Italy, 1987, 737

[24] Schittkowski K. NLPQLP: a fortran implementation of a sequential quadratic programming algorithm with distributed and non-monotone line search—user’s guide, version 4.2. Report, Department of Computer Science, University of Bayreuth, 2009

[25] Hanneman-Tamás R, Marquardt W. How to verify optimal controls computed by direct shooting methods?—a tutorial. Journal of Process Control, 2012, 22(2): 494–507

[26] Logist F, Vallerio M, Houska B, Diehl M, van Impe J. Multi-objective optimal control of chemical processes using ACADO toolkit. Computers & Chemical Engineering, 2012, 37: 191–199

[27] Nimmeggers P, Valerio M, Telen D, van Impe J, Logist F. Interactive multi-objective dynamic optimization of bioreactors under parametric uncertainty. Chemieingenieurtechnik (Weinheim), 2019, 91(3): 1–15

[28] Maußner J, Freund H. Multi-objective reactor design under uncertainty: a decomposition approach based on cubature rules. Chemical Engineering Science, 2020, 212: 115304

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