Advances in conceptual process design: From conventional strategies to AI-assisted methods

doi:10.1016/j.cjche.2025.05.014

Chinese Journal of Chemical Engineering ›› 2025, Vol. 84 ›› Issue (8): 60-76.DOI: 10.1016/j.cjche.2025.05.014

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Advances in conceptual process design: From conventional strategies to AI-assisted methods

Ali Tarik Karagoz¹, Omar Alqusair^1,2, Chao Liu¹, Jie Li¹

1. Centre for Process Integration, Department of Chemical Engineering, The University of Manchester, Manchester, M13 9PL, UK;
2. Department of Chemical Engineering, College of Engineering, King Saud University, P. O. Box 800, Riyadh 11421, Saudi Arabia

Received:2025-02-17 Revised:2025-05-11 Accepted:2025-05-11 Online:2025-06-07 Published:2025-08-28
Contact: Jie Li,E-mail:jie.li-2@manchester.ac.uk
Supported by:
ATK would like to appreciate financial support from The University of Manchester. ATK is a participant of Study Abroad Programme from Republic of Turkey which he is grateful for their continuous support from Ministry of Education and Secretariat of Defence Industries. OA would like to appreciate financial support from King Saud University.

Advances in conceptual process design: From conventional strategies to AI-assisted methods

Ali Tarik Karagoz¹, Omar Alqusair^1,2, Chao Liu¹, Jie Li¹

1. Centre for Process Integration, Department of Chemical Engineering, The University of Manchester, Manchester, M13 9PL, UK;
2. Department of Chemical Engineering, College of Engineering, King Saud University, P. O. Box 800, Riyadh 11421, Saudi Arabia

通讯作者: Jie Li,E-mail:jie.li-2@manchester.ac.uk
基金资助:
ATK would like to appreciate financial support from The University of Manchester. ATK is a participant of Study Abroad Programme from Republic of Turkey which he is grateful for their continuous support from Ministry of Education and Secretariat of Defence Industries. OA would like to appreciate financial support from King Saud University.

Abstract

Abstract: Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as heuristics, mathematical programming, and pinch analysis. Nonetheless, progress continues from different formulations of design problems using bottom-up approaches, to the utilization of new tools such as artificial intelligence (AI). It was not until recently that AI methods were involved again in assisting the decision-making steps for chemical engineers. This has led to a gap in understanding AI's capabilities and limitations within the field of CPD research. Thus, this article aims to provide an overview of conventional methods for process synthesis, integration, and intensification approaches and survey emerging AI-assisted process design applications to bridge the gap. A review of all AI-assisted methods is highlighted, where AI is used as a key component within a design framework, to explain the utility of AI with comparative examples. The studies were categorized into supervised and reinforcement learning based on the machine learning training principles they used to enhance the understanding of requirements, benefits, and challenges that come with it. Furthermore, we provide challenges and prospects that can facilitate or hinder the progress of AI-assisted approaches in the future.

Key words: Process systems, Process design, Mathematical programming, Artificial intelligence, Machine learning, Neural networks

摘要： Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as heuristics, mathematical programming, and pinch analysis. Nonetheless, progress continues from different formulations of design problems using bottom-up approaches, to the utilization of new tools such as artificial intelligence (AI). It was not until recently that AI methods were involved again in assisting the decision-making steps for chemical engineers. This has led to a gap in understanding AI's capabilities and limitations within the field of CPD research. Thus, this article aims to provide an overview of conventional methods for process synthesis, integration, and intensification approaches and survey emerging AI-assisted process design applications to bridge the gap. A review of all AI-assisted methods is highlighted, where AI is used as a key component within a design framework, to explain the utility of AI with comparative examples. The studies were categorized into supervised and reinforcement learning based on the machine learning training principles they used to enhance the understanding of requirements, benefits, and challenges that come with it. Furthermore, we provide challenges and prospects that can facilitate or hinder the progress of AI-assisted approaches in the future.

关键词: Process systems, Process design, Mathematical programming, Artificial intelligence, Machine learning, Neural networks

Ali Tarik Karagoz, Omar Alqusair, Chao Liu, Jie Li. Advances in conceptual process design: From conventional strategies to AI-assisted methods[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 60-76.

Ali Tarik Karagoz, Omar Alqusair, Chao Liu, Jie Li. Advances in conceptual process design: From conventional strategies to AI-assisted methods[J]. 中国化学工程学报, 2025, 84(8): 60-76.

References

[1] M. Martin, T.A. Adams II, Challenges and future directions for process and product synthesis and design, Comput. Chem. Eng. 128 (2019) 421-436.
[2] A.W. Westerberg, A retrospective on design and process synthesis, Comput. Chem. Eng. 28 (4) (2004) 447-458.
[3] C. Baier, Chemical process design and integration, 2nd Edition. John Wiley & Sons, 2016.
[4] J.M. Douglas, Conceptual design of chemical processes, McGraw-Hili Chemical Engineering Series. 1988.
[5] L. Mencarelli, Q. Chen, A. Pagot, I.E. Grossmann, A review on superstructure optimization approaches in process system engineering, Comput. Chem. Eng. 136 (2020) 106808.
[6] X.N. Li, A. Kraslawski, Conceptual process synthesis: past and current trends, Chem. Eng. Process. Process. Intensif. 43 (5) (2004) 583-594.
[7] H. Mothes. Process Design : Synthesis, Intensification, and Integration of Chemical Processes, Volume 1. Helmut Mothes; 2015.
[8] V.H. AGREDA, W.H. Heise. High-purity methyl acetate via reactive distillation. Chem. Eng. Prog., 86(2) (1990)40-46.
[9] A.H. Masso, D.F. Rudd, The synthesis of system designs. II. Heuristic structuring, AIChE. J. 15 (1) (1969) 10-17.
[10] B. Linnhoff, E. Hindmarsh, The pinch design method for heat exchanger networks, Chem. Eng. Sci. 38 (5) (1983) 745-763.
[11] J.J. Klemes, P.S. Varbanov, Z. Kravanja, Recent developments in process integration, Chem. Eng. Res. Des. 91 (10) (2013) 2037-2053.
[12] C. Ramshaw. The incentive for process intensification. In: BHR Group Conference Series Publication. Mechanical Engineering Publications Limited, 1995.
[13] A.I. Stankiewicz, J.A. Moulijn, Process intensification: transforming chemical engineering, Chem. Eng. Prog. 96 (1) (2000) 22-33.
[14] H. Freund, K. Sundmacher, Towards a methodology for the systematic analysis and design of efficient chemical processes Part 1. From unit operations to elementary process functions, Chem. Eng. Process. Process. Intensif. 47 (12) (2008) 2051-2060.
[15] T. Van Gerven, A. Stankiewicz, Structure, energy, synergy, time: the fundamentals of process intensification, Ind. Eng. Chem. Res. 48 (5) (2009) 2465-2474.
[16] V. Venkatasubramanian, The promise of artificial intelligence in chemical engineering: Is it here, finally? AIChE. J. 65 (2) (2019) 466-478.
[17] G. Stephanopoulos, J. Johnston, T. Kriticos, R. Lakshmanan, M. Mavrovouniotis, C. Siletti, Design-kit: an object-oriented environment for process engineering, Comput. Chem. Eng. 11 (6) (1987) 655-674.
[18] J.H. Lee, J. Shin, M.J. Realff, Machine learning: Overview of the recent progresses and implications for the process systems engineering field, Comput. Chem. Eng. 114 (2018) 111-121.
[19] A.M. Schweidtmann, E. Esche, A. Fischer, M. Kloft, J.U. Repke, S. Sager, A. Mitsos, Machine learning in chemical engineering: a perspective, Chem. Ing. Tech. 93 (12) (2021) 2029-2039.
[20] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of Go with deep neural networks and tree search, Nature 529 (7587) (2016) 484-489.
[21] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, 2024. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.
[22] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, In: Advances in Neural Information Processing Systems. Curran Associates, Inc., 2012.
[23] S. Cremaschi, A perspective on process synthesis: challenges and prospects, Comput. Chem. Eng. 81 (2015) 130-137.
[24] J. Gong, F.Q. You, Sustainable design and synthesis of energy systems, Curr. Opin. Chem. Eng. 10 (2015) 77-86.
[25] D.C.Y. Foo, R.R. Tan, A review on process integration techniques for carbon emissions and environmental footprint problems, Process. Saf. Environ. Prot. 103 (2016) 291-307.
[26] A. Nemet, P.S. Varbanov, J.J. Klemes, Cleaner production, process integration and intensification, Clean Technol. Environ. Policy 18 (7) (2016) 2029-2035.
[27] V. Andiappan, State-of-the-art review of mathematical optimisation approachesfor synthesis of energy systems, Process. Integr. Optim. Sustain. 1 (3) (2017) 165-188.
[28] Q. Chen, I.E. Grossmann, Recent developments and challenges in optimization-based process synthesis, Annu Rev Chem Biomol Eng 8 (2017) 249-283.
[29] P.Y. Liew, W.L. Theo, S.R. Wan Alwi, J.S. Lim, Z. Abdul Manan, J.J. Klemes, P.S. Varbanov, Total Site Heat Integration planning and design for industrial, urban and renewable systems, Renew. Sustain. Energy Rev. 68 (2017) 964-985.
[30] F.J. Keil, Process intensification, Rev. Chem. Eng. 34 (2) (2018) 135-200.
[31] M. Skiborowski, Process synthesis and design methods for process intensification, Curr. Opin. Chem. Eng. 22 (2018) 216-225.
[32] Y.H. Tian, S.E. Demirel, M.M.F. Hasan, E.N. Pistikopoulos, An overview of process systems engineering approaches for process intensification: State of the art, Chem. Eng. Process. Process. Intensif. 133 (2018) 160-210.
[33] S.E. Demirel, J.P. Li, M.F. Hasan, Systematic process intensification, Curr. Opin. Chem. Eng. 25 (2019) 108-113.
[34] F. Friedler, K.B. Aviso, B. Bertok, D.C. Foo, R.R. Tan, Prospects and challenges for chemical process synthesis with P-graph, Curr. Opin. Chem. Eng. 26 (2019) 58-64.
[35] L.X. Kang, Y.Z. Liu, Synthesis of flexible heat exchanger networks: a review, Chin. J. Chem. Eng. 27 (7) (2019) 1485-1497.
[36] P. Marin, F.V. Diez, S. Ordonez, Reverse flow reactors as sustainable devices for performing exothermic reactions: Applications and engineering aspects, Chem. Eng. Process. Process. Intensif. 135 (2019) 175-189.
[37] S. Sitter, Q. Chen, I.E. Grossmann, An overview of process intensification methods, Curr. Opin. Chem. Eng. 25 (2019) 87-94.
[38] Y.H. Tian, E.N. Pistikopoulos, Synthesis of operable process intensification systems: steady-state design with safety and operability considerations, Ind. Eng. Chem. Res. 58 (15) (2019) 6049-6068.
[39] K.O. Yoro, P.T. Sekoai, A.J. Isafiade, M.O. Daramola, A review on heat and mass integration techniques for energy and material minimization during CO₂ capture, Int. J. Energy Environ. Eng. 10 (3) (2019) 367-387.
[40] Y.V. Fan, H.H. Chin, J.J. Klemes, P.S. Varbanov, X. Liu, Optimisation and process design tools for cleaner production, J. Clean. Prod. 247 (2020) 119181.
[41] A.K. Tula, M.R. Eden, R. Gani, Computer-aided process intensification: challenges, trends and opportunities, AIChE. J. 66 (1) (2020) e16819.
[42] X.Y. Li, B. Xu, H. Tian, G.Q. Shu, Towards a novel holistic design of organic Rankine cycle (ORC) systems operating under heat source fluctuations and intermittency, Renew. Sustain. Energy Rev. 147 (2021) 111207.
[43] S. Haase, P. Tolvanen, V. Russo, Process intensification in chemical reaction engineering, Processes 10 (1) (2022) 99.
[44] E.A. Lopez-Guajardo, F. Delgado-Licona, A. Alejandro J, K.D.P. Nigam, A. Montesinos-Castellanos, R. Morales-Menendez, Process intensification 4.0: a new approach for attaining new, sustainable and circular processes enabled by machine learning, Chem. Eng. Process. Process. Intensif. 180 (2022) 108671.
[45] M. Martin, R. Gani, I.M. Mujtaba, Sustainable process synthesis, design, and analysis: Challenges and opportunities, Sustain. Prod. Consum. 30 (2022) 686-705.
[46] S.K. Bishnu, S.Y. Alnouri, D.M. Al-Mohannadi, Computational applications using data driven modeling in process Systems: a review, Digit. Chem. Eng. 8 (2023) 100111.
[47] T. Haghpanah, M.A. Sobati, M.S. Pishvaee, Process synthesis for the microalgae-based bio-refineries through superstructure optimization: a systematic review, Comput. Chem. Eng. 177 (2023) 108352.
[48] C. Ramirez-Marquez, M.M. Al-Thubaiti, M. Martin, M.M. El-Halwagi, J.M. Ponce-Ortega, Processes intensification for sustainability: prospects and opportunities, Ind. Eng. Chem. Res. 62 (6) (2023) 2428-2443.
[49] J.G. Segovia-Hernandez, S. Hernandez, E. Cossio-Vargas, E. Sanchez-Ramirez, Challenges and opportunities in process intensification to achieve the UN’s 2030 agenda: Goals 6, 7, 9, 12 and 13, Chem. Eng. Process. Process. Intensif. 192 (2023) 109507.
[50] C.L. Zhou, J.L. Deng, F. Gao, Y. Zhu, C.Y. Jia, J.T. Wang, Integration of reactor network synthesis: a literature review and opportunities for future research, Asia Pac. J. Chem. Eng. 18 (4) (2023) e2917.
[51] X.C. Ding, J. Li, H.J. Chen, T. Zhou, Superstructure-based carbon capture and utilization process design, Curr. Opin. Chem. Eng. 43 (2024) 100995.
[52] Q.H. Gao, A.M. Schweidtmann, Deep reinforcement learning for process design: Review and perspective, Curr. Opin. Chem. Eng. 44 (2024) 101012.
[53] A.A. Kiss, R. Muthia, I. Pazmino-Mayorga, J. Harmsen, M. Jobson, X. Gao, Conceptual methods for synthesis of reactive distillation processes: recent developments and perspectives, J. Chem. Technol. Biotechnol. 99 (6) (2024) 1263-1290.
[54] Q. Li, A. Somoza-Tornos, J. Grievink, A.A. Kiss, Challenges and opportunities for process intensification in Europe from a process systems engineering perspective, Front. Energy Res. 12 (2024) 1340635.
[55] E.N. Pistikopoulos, Y.H. Tian, Advanced modeling and optimization strategies for process synthesis, Annu. Rev. Chem. Biomol. Eng. 15 (1) (2024) 81-103.
[56] J.J. Siirola, D.F. Rudd, Computer-aided synthesis of chemical process designs. from reaction path data to the process task network, Ind. Eng. Chem. Fund. 10 (3) (1971) 353-362.
[57] V. Mahalec, R.L. Motard, Procedures for the initial design of chemical processing systems, Comput. Chem. Eng. 1 (1) (1977) 57-68.
[58] J.M. Douglas, A hierarchical decision procedure for process synthesis, AIChE. J. 31 (3) (1985) 353-362.
[59] B. Linnhoff. Esso Energy Award Lecture, 1981 - New concepts in thermodynamics for better chemical process design, Proc. R. Soc. Lond. A 386 (1790) (1983) 1-33.
[60] P. Lutze, D.K. Babi, J.M. Woodley, R. Gani, Phenomena based methodology for process synthesis incorporating process intensification, Ind. Eng. Chem. Res. 52 (22) (2013) 7127-7144.
[61] W.Z. Wu, C.A. Henao, C.T. Maravelias, A superstructure representation, generation, and modeling framework for chemical process synthesis, AIChE. J. 62 (9) (2016) 3199-3214.
[62] M. Bogataj, Z. Kravanja, An alternative strategy for global optimization of heat exchanger networks, Appl. Therm. Eng. 43 (2012) 75-90.
[63] L. Cucek, S. Boldyryev, J.J. Klemes, Z. Kravanja, G. Krajacic, P.S. Varbanov, N. Duic, Approaches for retrofitting heat exchanger networks within processes and Total Sites, J. Clean. Prod. 211 (2019) 884-894.
[64] M. Pan, I. Bulatov, R. Smith, Exploiting tube inserts to intensify heat transfer for the retrofit of heat exchanger networks considering fouling mitigation, Ind. Eng. Chem. Res. 52 (8) (2013) 2925-2943.
[65] H. Liu, L.J. Ren, H.M. Zhuo, S.Z. Fu, Water footprint and water pinch analysis in ethanol industrial production for water management, Water 11 (3) (2019) 518.
[66] I. Grossmann, Enterprise-wide optimization: a new frontier in process systems engineering, AIChE. J. 51 (7) (2005) 1846-1857.
[67] M.M. El-Halwagi, I.M. Glasgow, X.Y. Qin, M.R. Eden, Property integration: Componentless design techniques and visualization tools, AIChE. J. 50 (8) (2004) 1854-1869.
[68] X.P. Liu, J. Liu, C. Deng, J.Y. Lee, R.R. Tan, Synthesis of refinery hydrogen network integrated with hydrogen turbines for power recovery, Energy 201 (2020) 117623.
[69] R.V. Rao, A. Saroj, Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm, Energy Syst. 9 (2) (2018) 305-341.
[70] S.R. Kazi, M. Short, A.J. Isafiade, L.T. Biegler, Heat exchanger network synthesis with detailed exchanger designs: 2. Hybrid optimization strategy for synthesis of heat exchanger networks, AIChE. J. 67 (1) (2021) e17057.
[71] S.D. Barnicki, J.J. Siirola, Process synthesis prospective, Comput. Chem. Eng. 28 (4) (2004) 441-446.
[72] A.A. Kiss, R. Smith, Rethinking energy use in distillation processes for a more sustainable chemical industry, Energy 203 (2020) 117788.
[73] T. Umeda, A. Hirai, A. Ichikawa, Synthesis of optimal processing system by an integrated approach, Chem. Eng. Sci. 27 (4) (1972) 795-804.
[74] E. Kondili, C.C. Pantelides, R.W.H. Sargent, A general algorithm for short-term scheduling of batch operations: I. MILP formulation, Comput. Chem. Eng. 17 (2) (1993) 211-227.
[75] F. Friedler, K. Tarjan, Y.W. Huang, L.T. Fan, Combinatorial algorithms for process synthesis, Comput. Chem. Eng. 16 (1992) S313-S320.
[76] E.M.B. Smith, C.C. Pantelides, Design of reaction/separation networks using detailed models, Comput. Chem. Eng. 19 (1995) 83-88.
[77] T. Farkas, E. Rev, Z. Lelkes, Process flowsheet superstructures: structural multiplicity and redundancy part I: basic GDP and MINLP representations, Comput. Chem. Eng. 29 (10) (2005) 2180-2197.
[78] D. Zyngier, J.D. Kelly. UOPSS: a new paradigm for modeling production planning & scheduling systems. In: Symposium on Computer Aided Process Engineering, London, United Kingdom, 2012.
[79] L. D’Anterroches, R. Gani, Group contribution based process flowsheet synthesis, design and modelling, Fluid Phase Equilib. 228 (2005) 141-146.
[80] Q. Chen, Y.S. Liu, G. Seastream, J.D. Siirola, I.E. Grossmann, Pyosyn: a new framework for conceptual design modeling and optimization, Comput. Chem. Eng. 153 (2021) 107414.
[81] GAMS [Internet]. [cited 2024 Sep 24]. User's guide. https://www.gams.com/latest/docs/UG_MAIN.html.
[82] R. Fourer, D.M. Gay, B.W. Kernighan, A modeling language for mathematical programming, Manag. Sci. 36 (5) (1990) 519-554.
[83] R. Saigal, Linear programming: a modern integrated analysis, J. Oper. Res. Soc. 48 (7) (1997) 762.
[84] A.S. Nemirovski, M.J. Todd, Interior-point methods for optimization, Acta Numer. 17 (2008) 191-234.
[85] M. Yannakakis, Expressing combinatorial optimization problems by linear programs. In: Proceedings of the twentieth annual ACM symposium on Theory of computing, Chicago, Illinois, USA, 1988.
[86] B.A. Murtagh, M.A. Saunders, A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints. Algorithms for Constrained Minimization of Smooth Nonlinear Functions. Springer Berlin Heidelberg, (1982), pp 4-117.
[87] C. Schmid, L.T. Biegler, Quadratic programming methods for reduced hessian SQP, Comput. Chem. Eng. 18 (9) (1994) 817-832.
[88] N.V. Sahinidis, BARON: a general purpose global optimization software package, J. Glob. Optim. 8 (2) (1996) 201-205.
[89] R. Misener, C.A. Floudas, ANTIGONE: algorithms for coNTinuous/integer global optimization of nonlinear equations, J. Glob. Optim. 59 (2) (2014) 503-526.
[90] S. Vigerske, A. Gleixner, SCIP: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework, optim meth softw 33 (3) (2018) 563-593.
[91] Y.J. Ma, J. Li, Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed-integer nonlinear optimization, AIChE. J. 68 (6) (2022) e17629.
[92] K.P. Papalexandri, E.N. Pistikopoulos, Generalized modular representation framework for process synthesis, AIChE. J. 42 (4) (1996) 1010-1032.
[93] P. Proios, E.N. Pistikopoulos, Hybrid generalized modular/collocation framework for distillation column synthesis, AIChE. J. 52 (3) (2006) 1038-1056.
[94] Y.H. Tian, I. Pappas, B. Burnak, J. Katz, E.N. Pistikopoulos, A Systematic Framework for the synthesis of operable process intensification systems-Reactive separation systems, Comput. Chem. Eng. 134 (2020) 106675.
[95] S. Wilson, V. Manousiouthakis, IDEAS approach to process network synthesis: Application to multicomponent MEN, AIChE. J. 46 (12) (2000) 2408-2416.
[96] J.F. Burri, S.D. Wilson, V.I. Manousiouthakis, Infinite DimEnsionAl State-space approach to reactor network synthesis: application to attainable region construction, Comput. Chem. Eng. 26 (6) (2002) 849-862.
[97] A. Peschel, H. Freund, K. Sundmacher, Methodology for the design of optimal chemical reactors based on the concept of elementary process functions, Ind. Eng. Chem. Res. 49 (21) (2010) 10535-10548.
[98] N.M. Kaiser, R.J. Flassig, K. Sundmacher, Reactor-network synthesis via flux profile analysis, Chem. Eng. J. 335 (2018) 1018-1030.
[99] N. Garg, G.M. Kontogeorgis, R. Gani, J.M. Woodley, A process synthesis-intensification method for generation of novel and intensified solutions, Chem. Eng. Process. Process. Intensif. 156 (2020) 108103.
[100] S.E. Demirel, J.P. Li, M.M.F. Hasan, Systematic process intensification using building blocks, Comput. Chem. Eng. 105 (2017) 2-38.
[101] S.E. Demirel, J.P. Li, M.M. Faruque Hasan, A general framework for process synthesis, integration, and intensification, Ind. Eng. Chem. Res. 58 (15) (2019) 5950-5967.
[102] S.E. Demirel, J.P. Li, M. El-Halwagi, M.M. Faruque Hasan, Sustainable process intensification using building blocks, ACS Sustainable Chem. Eng. 8 (48) (2020) 17664-17679.
[103] H. Kuhlmann, H. Veith, M. Moller, K.P. Nguyen, A. Gorak, M. Skiborowski. Optimization-based approach to process synthesis for process intensification: synthesis of reaction-separation processes. Ind. Eng. Chem. Res. 57(10)(2018):3639-3655.
[104] T.Y. Algusane, P. Proios, M.C. Georgiadis, E.N. Pistikopoulos, A framework for the synthesis of reactive absorption columns, Chem. Eng. Process. Process. Intensif. 45 (4) (2006) 276-290.
[105] Y.H. Tian, E.N. Pistikopoulos, Toward an envelope of design solutions for combined/intensified reaction/separation systems, Ind. Eng. Chem. Res. 59 (24) (2020) 11350-11354.
[106] Y.H. Tian, V. Meduri, R. Bindlish, E.N. Pistikopoulos, A Process Intensification synthesis framework for the design of dividing wall column systems, Comput. Chem. Eng. 160 (2022) 107679.
[107] M.J. Bagajewicz, V. Manousiouthakis, Mass/heat-exchange network representation of distillation networks, AIChE. J. 38 (11) (1992) 1769-1800.
[108] J.E. Drake, V. Manousiouthakis, IDEAS approach to process network synthesis: minimum plate area for complex distillation networks with fixed utility cost, Ind. Eng. Chem. Res. 41 (20) (2002) 4984-4992.
[109] H. Kuhlmann, M. Skiborowski, Optimization-based approach to process synthesis for process intensification: general approach and application to ethanol dehydration, Ind. Eng. Chem. Res. 56 (45) (2017) 13461-13481.
[110] T. Svitnic, K. Sundmacher, Renewable methanol production: Optimization-based design, scheduling and waste-heat utilization with the FluxMax approach, Appl. Energy 326 (2022) 120017.
[111] J. Oeing, F.B. Henke, N. Kockmann, Machine learning based suggestions of separation units for process synthesis in process simulation, Chem. Ing. Tech. 93 (12) (2021) 1930-1936.
[112] J. Oeing, W. Welscher, N. Krink, L. Jansen, F. Henke, N. Kockmann, Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard, Digit. Chem. Eng. 4 (2022) 100038.
[113] J. Oeing, K. Brandt, M. Wiedau, D. Gregor Tolksdorf, W. Welscher, P.D.I. Norbert Kockmann, Graph learning in machine-readable plant topology data, Chem. Ing. Tech. 95 (7) (2023) 1049-1060.
[114] T. Nabil, Y. Le Moullec, A. Le Coz. Machine learning based design of a supercritical CO₂ concentrating solar power plant. In: Proceedings of the 3rd European supercritical CO₂ Conference, Paris, France,2019.
[115] T. Nabil, J.M. Commenge, T. Neveux, Generative approaches for the synthesis of process structures. 14th International Symposium on Process Systems Engineering. Elsevier, (2022), pp 89-294.
[116] G. Vogel, L. Schulze Balhorn, A.M. Schweidtmann, Learning from flowsheets: a generative transformer model for autocompletion of flowsheets, Comput. Chem. Eng. 171 (2023) 108162.
[117] L.I. Midgley. Deep reinforcement learning for process synthesis. arXiv, 2020. Available from: http://arxiv.org/abs/2009.13265.
[118] A. Khan, A. Lapkin, Searching for optimal process routes: a reinforcement learning approach, Comput. Chem. Eng. 141 (2020) 107027.
[119] A.A. Khan, A.A. Lapkin, Designing the process designer: Hierarchical reinforcement learning for optimisation-based process design, Chem. Eng. Process. Process. Intensif. 180 (2022) 108885.
[120] S.J. Plathottam, B. Richey, G. Curry, J. Cresko, C.O. Iloeje, Solvent extraction process design using deep reinforcement learning, J. Adv. Manuf. Process. 3 (2) (2021) e10079.
[121] Q. Gottl, Y. Tonges, D.G. Grimm, J. Burger, Automated flowsheet synthesis using hierarchical reinforcement learning: proof of concept, Chem. Ing. Tech. 93 (12) (2021) 2010-2018.
[122] Q. Gottl, D.G. Grimm, J. Burger, Automated synthesis of steady-state continuous processes using reinforcement learning, Front. Chem. Sci. Eng. 16 (2) (2022) 288-302.
[123] L. Stops, R. Leenhouts, Q.H. Gao, A.M. Schweidtmann, Flowsheet generation through hierarchical reinforcement learning and graph neural networks, AIChE. J. 69 (1) (2023) e17938.
[124] Q.H. Gao, H.Y. Yang, S.M. Shanbhag, A.M. Schweidtmann, Transfer learning for process design with reinforcement learning. 33rd European Symposium on Computer Aided Process Engineering. Elsevier, (2023), pp 005-2010.
[125] S. Reynoso-Donzelli, L.A. Ricardez-Sandoval, A reinforcement learning approach with masked agents for chemical process flowsheet design, AIChE. J. 71 (1) (2025) e18584.
[126] C. Joo, H. Kwon, J. Kim, H. Cho, J. Lee, Machine-learning-based optimization of operating conditions of naphtha cracking furnace to maximize plant profit. 33rd European Symposium on Computer Aided Process Engineering. Elsevier, (2023), pp 397-1402.
[127] W.R. Wang, Y.J. Ma, A. Maroufmashat, N. Zhang, J. Li, X. Xiao, Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework, Appl. Energy 305 (2022) 117751.
[128] P. Schafer, A. Caspari, K. Kleinhans, A. Mhamdi, A. Mitsos, Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks, AIChE. J. 65 (5) (2019) e16568.
[129] E. Mosqueira-Rey, E. Hernandez-Pereira, D. Alonso-Rios, J. Bobes-Bascaran, A. Fernandez-Leal, Human-in-the-loop machine learning: a state of the art, Artif. Intell. Rev. 56 (4) (2023) 3005-3054.
[130] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C.L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, R. Lowe, Training language models to follow instructions with human feedback. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. Red Hook, NY, USA,2022.
[131] M. Wiedau, L. von Wedel, H. Temmen, R. Welke, N. Papakonstantinou, ENPRO data integration: extending DEXPI towards the asset lifecycle, Chem. Ing. Tech. 91 (3) (2019) 240-255.
[132] A. Graves. Generating sequences with recurrent neural networks. arXiv, 2014. Available from: http://arxiv.org/abs/1308.0850.
[133] A. Radford, K. Narasimhan, Improving language understanding by generative pre-training, North American Chapter of the Association for Computational Linguistics, 2018. https://www.mikecaptain.com/resources/pdf/GPT-1.pdf.
[134] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. October 4-6, 1995, Nagoya, Japan. IEEE, (1995) 39-43.
[135] T. Nabil, M. Noaman, T. Morosuk, Data-driven structural synthesis of supercritical CO₂ power cycles, Front. Chem. Eng. 5 (2023) 1144115.
[136] T. Zhang, N.V. Sahinidis, J.J. Siirola, Pattern recognition in chemical process flowsheets, AIChE. J. 65 (2) (2019) 592-603.
[137] T. Ishii, R. Ueda, Y. Miyao. Empirical analysis of the inductive Bias of recurrent neural networks by discrete Fourier transform of output sequences. arXiv, 2023. Available from: http://arxiv.org/abs/2305.09178.
[138] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. In: Advances in Neural Information Processing Systems. Long Beach, California,US, 2017.
[139] G. Vogel, E. Hirtreiter, L. Schulze Balhorn, A.M. Schweidtmann, SFILES 2.0: an extended text-based flowsheet representation, Optim. Eng. 24 (4) (2023) 2911-2933.
[140] L. d’Anterroches, R. Gani, Process flow sheet generation & design through a group contribution approach, Ph.D. Thesis, University of Denmark, Copenhagen, 2006.
[141] D. Weininger, SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules, J. Chem. Inf. Comput. Sci. 28 (1) (1988) 31-36.
[142] C.F. Shi, H.R. Yang, D. Cai, Z.S. Zhang, Y.F. Wang, Y.J. Yang, W. Lam, A Thorough Examination of Decoding Methods in the Era of LLMs, In:Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida, USA. Stroudsburg, PA, USAACL, 2024.
[143] L.S. Balhorn, Q. Gao, D. Goldstein, A.M. Schweidtmann. Flowsheet recognition using deep convolutional neural networks. In: Yamashita Y, Kano M, editors. Computer Aided Chemical Engineering. Elsevier, 2022.
[144] M.F. Theisen, K.N. Flores, L. Schulze Balhorn, A.M. Schweidtmann, Digitization of chemical process flow diagrams using deep convolutional neural networks, Digit. Chem. Eng. 6 (2023) 100072.
[145] K. Maharana, S. Mondal, B. Nemade, A review: Data pre-processing and data augmentation techniques, Glob. Transitions Proc. 3 (1) (2022) 91-99.
[146] L.S. Balhorn, E. Hirtreiter, L. Luderer, A.M. Schweidtmann, Data augmentation for machine learning of chemical process flowsheets. 33rd European Symposium on Computer Aided Process Engineering. Athens, Greece,Elsevier, 2023.
[147] C. Berner, G. Brockman, B. Chan, V. Cheung, P. Debiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, R. Jozefowicz, S. Gray, C. Olsson. Dota 2 with Large Scale Deep Reinforcement Learning. arXiv, 2019. Available from: http://arxiv.org/abs/1912.06680.
[148] R.S. Sutton, A.G. Barto, Reinforcement learning: an introduction, IEEE Trans. Neural Network. 9 (5)1054.
[149] T. Haarnoja, A. Zhou, P. Abbeel, S. Levine. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden, 2018.
[150] B. van Jasper, B. Richard. COCO - the CAPE-OPEN to CAPE-OPEN simulator. 2024. Available from: https://www.cocosimulator.org.
[151] H. Kooijman, R. Taylor. Chemsep. 2024. Available from: http://www.chemsep.org/index.html.
[152] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov. Proximal Policy Optimization Algorithms. arXiv, 2017. Available from: http://arxiv.org/abs/1707.06347.
[153] Q. Gottl, J. Pirnay, J. Burger, D.G. Grimm, Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge, Comput. Chem. Eng. 194 (2025) 108975.
[154] S.C.P.A. van Kalmthout, L.I. Midgley, M.B. Franke. Synthesis of separation processes with reinforcement learning. arXiv; 2022. Available from: http://arxiv.org/abs/2211.04327.
[155] S. Narvekar, B. Peng, M. Leonetti, J. Sinapov, M.E. Taylor, P. Stone, Curriculum learning for reinforcement learning domains: a framework and survey, (2020): 2003.04960.
[156] M. Martin, T.A. Adams II, Challenges and future directions for process and product synthesis and design, Comput. Chem. Eng. 128 (2019) 421-436.

Advances in conceptual process design: From conventional strategies to AI-assisted methods

Advances in conceptual process design: From conventional strategies to AI-assisted methods

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