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

doi:10.1016/j.cjche.2025.05.014

中国化学工程学报 ›› 2025, Vol. 84 ›› Issue (8): 60-76.DOI: 10.1016/j.cjche.2025.05.014

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

收稿日期:2025-02-17 修回日期:2025-05-11 接受日期:2025-05-11 出版日期:2025-08-28 发布日期:2025-06-07
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.

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:2025-08-28 Published:2025-06-07
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.

摘要/Abstract

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

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

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.

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.

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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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