Proceedings of ESCAPE 36ISSN: 2818-4734
Volume: 5 (2026)
Table of Contents
LAPSE:2026.0472
Published Article
LAPSE:2026.0472
An Extended Superstructure Formulation for Non-Isobaric Flowsheet Synthesis
June 12, 2026
Abstract
Flowsheet synthesis is an integral step in process design, entailing the selection of a set of unit operations and their connectivity to convert raw materials to products. Superstructure optimisation represents a promising class of synthesis approaches, allowing for the systematic exploration of the flowsheet design space. Despite this, many superstructure formulations suffer from numerical instabilities, combinatorial explosion, and/or rely on restrictive assumptions on the types of flowsheet alternatives that can be considered. The modified state-operator network (MSON) formalism has recently been proposed to address some of these issues for isobaric flowsheets. The constant-pressure assumption restricts the applicability of the MSON to real process applications as pressure is a key process variable in many unit operations, such as distillation, reaction, and extrusion, and is necessary to elicit flow. In this work, we present the extended MSON (E-MSON) which inherits the numerical stability of MSON, whilst removing the isobaric assumption. This is achieved through the introduction of new constraints that further improve the numerical behaviour of the MSON. The E-MSON is then applied to a simple non-isobaric superstructure optimisation problem, the results of which demonstrate that the E-MSON can serve as a framework for non-isobaric flowsheet synthesis, enabling a broader range of flowsheet alternatives to be considered.
Suggested Citation
Fraser HA, Gopinath S, Sefcik J, Jackson G, Galindo A, Adjiman CS. An Extended Superstructure Formulation for Non-Isobaric Flowsheet Synthesis. Systems and Control Transactions 5:2152-2160 (2026) https://doi.org/10.69997/sct.145962
Author Affiliations
Fraser HA: Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, SW7 2AZ, London, United Kingdom [ORCID]
Gopinath S: School of Chemical, Materials and Biological Engineering, University of Sheffield, S1 3JD, Sheffield, United Kingdom [ORCID]
Sefcik J: Department of Chemical and Process Engineering, University of Strathclyde, G1 1XJ, Glasgow, United Kingdom [ORCID]
Jackson G: Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, SW7 2AZ, London, United Kingdom [ORCID]
Galindo A: Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, SW7 2AZ, London, United Kingdom [ORCID]
Adjiman CS: Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, SW7 2AZ, London, United Kingdom [ORCID]
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Journal Name
Systems and Control Transactions
Volume
5
First Page
2152
Last Page
2160
Year
2026
Publication Date
2026-06-12
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Original Submission
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PII: 2152-2160-314-SCT-5-2026, Publication Type: Journal Article
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LAPSE:2026.0472
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LAPSE:2026.0034
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References Cited
  1. Mencarelli L, Chen Q, Pagot A, Grossmann IE. A review on superstructure optimization approaches in process system engineering. Computers & Chemical Engineering 136:106808 (2020) https://doi.org/10.1016/j.compchemeng.2020.106808
  2. Douglas JM. A hierarchical decision procedure for process synthesis. AIChE Journal 31:353-362 (2004) https://doi.org/10.1002/aic.690310302
  3. Alcántara-Avila JR. 50 years of optimization in japan using superstructures. Journal of Chemical Engineering of Japan 56: (2023) https://doi.org/10.1080/00219592.2023.2188112
  4. Cremaschi S. A perspective on process synthesis: challenges and prospects. Computers & Chemical Engineering 81:130-137 (2015) https://doi.org/10.1016/j.compchemeng.2015.05.007
  5. Kondili E, Pantelides CC, Sargent RWH. A general algorithm for short-term scheduling of batch operations-i. MILP formulation. Computers & Chemical Engineering 17:211-227 (1993) https://doi.org/10.1016/0098-1354(93)80015-f
  6. Yeomans H, Grossmann IE. A systematic modeling framework of superstructure optimization in process synthesis. Computers & Chemical Engineering 23:709-731 (1999) https://doi.org/10.1016/s0098-1354(99)00003-4
  7. Smith EMB, Pantelides CC. Design of reaction/separation networks using detailed models. Computers & Chemical Engineering 19:83-88 (1995) https://doi.org/10.1016/0098-1354(95)87019-9
  8. Lutze P, Babi DK, Woodley JM, Gani R. Phenomena based methodology for process synthesis incorporating process intensification. Ind. Eng. Chem. Res. 52:7127-7144 (2013) https://doi.org/10.1021/ie302513y
  9. Demirel SE, Li J, Hasan MMF. A general framework for process synthesis, integration, and intensification. Ind. Eng. Chem. Res. 58:5950-5967 (2019) https://doi.org/10.1021/acs.iecr.8b05961
  10. Dowling AW, Biegler LT. A framework for efficient large scale equation-oriented flowsheet optimization. Computers & Chemical Engineering 72:3-20 (2015) https://doi.org/10.1016/j.compchemeng.2014.05.013
  11. Schilling J, Horend C, Bardow A. Integrating superstructure?based design of molecules, processes, and flowsheets. AIChE Journal 66: (2020) https://doi.org/10.1002/aic.16903
  12. Lee S, Grossmann IE. Global optimization of nonlinear generalized disjunctive programming with bilinear equality constraints: applications to process networks. Computers & Chemical Engineering 27:1557-1575 (2003) https://doi.org/10.1016/s0098-1354(03)00098-x
  13. Yeomans H, Grossmann IE. Disjunctive programming models for the optimal design of distillation columns and separation sequences. Ind. Eng. Chem. Res. 39:1637-1648 (2000) https://doi.org/10.1021/ie9906520
  14. Navarro-Amorós MA, Ruiz-Femenia R, Caballero JA. Integration of modular process simulators under the generalized disjunctive programming framework for the structural flowsheet optimization. Computers & Chemical Engineering 67:13-25 (2014) https://doi.org/10.1016/j.compchemeng.2014.03.014
  15. Gopinath S, Adjiman CS. Superstructure optimization with rigorous models via an exact reformulation. Computers & Chemical Engineering 194:108972 (2025) https://doi.org/10.1016/j.compchemeng.2024.108972
  16. Gopinath S, Adjiman CS. Advances in process synthesis: new robust formulations. Systems and Control Transactions 3:145-152 (2024) https://doi.org/10.69997/sct.169290
  17. Poling BE, Prausnitz JM, O'Connell JP. Properties of Gases and Liquids, Fifth Edition. McGraw-Hill Education (2020).
  18. Liang S, Cao Y, Liu X, Li X, Zhao Y, Wang Y, Wang Y. Insight into pressure-swing distillation from azeotropic phenomenon to dynamic control. Chemical Engineering Research and Design 117:318-335 (2017) https://doi.org/10.1016/j.cherd.2016.10.040
  19. Wang R, Lin S. Pore model for nanofiltration: history, theoretical framework, key predictions, limitations, and prospects. Journal of Membrane Science 620:118809 (2021) https://doi.org/10.1016/j.memsci.2020.118809
  20. Wang X, Liu X. High pressure: a feasible tool for the synthesis of unprecedented inorganic compounds. Inorg. Chem. Front. 7:2890-2908 (2020) https://doi.org/10.1039/d0qi00477d
  21. Pundir SS, Kumar S. Improving cost estimation for compressors: evaluating existing and proposing new comprehensive correlations. Chemical Engineering Research and Design 224:79-90 (2025) https://doi.org/10.1016/j.cherd.2025.10.050
  22. Lee S, Grossmann IE. New algorithms for nonlinear generalized disjunctive programming. Computers & Chemical Engineering 24:2125-2141 (2000) https://doi.org/10.1016/s0098-1354(00)00581-0
  23. Viswanathan J, Grossmann IE. A combined penalty function and outer-approximation method for MINLP optimization. Computers & Chemical Engineering 14:769-782 (1990) https://doi.org/10.1016/0098-1354(90)87085-4
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