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Multiscale process systems engineering for electrochemically mediated CO2 capture: A mini-review

CHEMICAL engineering SCIENCE 2025年 307卷

作者： Hu, Yongxin Sun, Longgang Zhou, Teng Hong Kong Univ Sci & Technol Guangzhou Sustainable Energy & Environm Thrust Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

Electrochemically mediated CO2 capture (EMCC) is a novel carbon capture technology. Despite its great potential, EMCC encounters technical and economic challenges. The process systems engineering (PSE) methods and tools have potential to bridge the gap and overcome some obstacles in EMCC. This mini review offers an overview of innovations, opportunities, and challenges in EMCC from a multiscale PSE perspective. First, a brief introduction of different types of EMCC implementations is provided. Next, a thermodynamic model is proposed to analyze the impact of functional molecules (electrolyte), microstructure of electrode surface and cell structures on the system's performance. Then, a multi-scale analysis is performed to examine the system across molecular-, meso-, unit-, and process-scales, with the potential applications of PSE methods explored. This study also shows that the EMCC technology can be further promoted by learning the experience from applying PSE methods to the flow battery and electrochemical reduction processes.

关键词： Multi-scale modeling and analysis process systems engineering electrochemically mediated CO2 capture Flow battery CO2 reduction reaction

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Artificial intelligence and machine learning at various stages and scales of process systems engineering

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CANADIAN JOURNAL OF CHEMICAL engineering 2025年第3期103卷 1004-1035页

作者： Srinivasan, Karthik Puliyanda, Anjana Thosar, Devavrat Bhakte, Abhijit Singh, Kuldeep Addo, Prince Srinivasan, Rajagopalan Prasad, Vinay Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada Indian Inst Technol Madras Dept Chem Engn Chennai India Indian Inst Technol Madras Amer Express Lab Data Analyt Risk & Technol Chennai India

We review the utility and application of artificial intelligence (AI) and machine learning (ML) at various process scales in this work, from molecules and reactions to materials to processes, plants, and supply chains;furthermore, we highlight whether the application is at the design or operational stage of the process. In particular, we focus on the distinct representational frameworks employed at the various scales and the physics (equivariance, additivity, injectivity, connectivity, hierarchy, and heterogeneity) they capture. We also review AI techniques and frameworks important in process systems, including hybrid AI modelling, human-AI collaborations, and generative AI techniques. In hybrid AI models, we emphasize the importance of hyperparameter tuning, especially in the case of physics-informed regularization. We highlight the importance of studying human-AI interactions, especially in the context of automation, and distinguish the features of human-complements-AI systems from those of AI-complements-human systems. Of particular importance in the AI-complements-human framework are model explanations, including rule-based explanation, explanation-by-example, explanation-by-simplification, visualization, and feature relevance. Generative AI methods are becoming increasingly relevant in process systems engineering, especially in contexts that do not belong to 'big data', primarily due to the lack of high quality labelled data. We highlight the use of generative AI methods including generative adversarial networks, graph neural networks, and large language models/transformers along with non-traditional process data (images, audio, and text).

关键词： artificial intelligence human-AI interaction machine learning process systems engineering

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Exploring clustering tools in process systems engineering: Innovations, applications, and future directions

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CHEMICAL engineering RESEARCH & DESIGN 2025年 217卷 13-23页

作者： Lopez-Flores, Francisco Javier Raya-Tapia, Alma Yunuen Ramirez-Marquez, Cesar Ponce-Ortega, Jose Maria Univ Michoacana Chem Engn Dept Morelia 58060 Mich Mexico

Clustering techniques play a transformative role in optimizing complex systems across various domains, including process systems engineering (PSE), healthcare, and network analysis. In PSE, these methods enhance fault detection, renewable energy systems, supply chain management, and chemical process optimization by simplifying large datasets, improving decision-making, and enabling sustainable solutions. Beyond industrial applications, clustering supports medical diagnostics and epidemiological studies, as well as social and technological network optimization, enabling better information flow and resource allocation. Researchers have expanded clustering applications by integrating them with other advanced machine learning techniques and realtime data analytics, fostering resilience and adaptability in dynamic environments. Despite these advancements, challenges persist, particularly in managing computational demands and developing adaptive, real-time clustering frameworks that address evolving landscapes. This review critically examines state-of-the-art clustering methodologies across these fields, comparing their contributions, limitations, and future potential. The discussion highlights interdisciplinary approaches and ethical considerations, emphasizing clustering's role in tackling global challenges such as sustainability and energy transitions. Finally, the paper outlines emerging trends that will shape clustering applications in next-generation systems, digitalization, and artificial intelligence-driven optimization strategies.

关键词： Clustering process systems engineering Optimization Data analysis Anomaly detection

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Development and prospect of the acetylene production chain based on the process systems engineering: a focus on the polyvinyl chloride production

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REVIEWS IN CHEMICAL engineering 2024年第8期40卷 917-949页

作者： Ma, Xiangcheng Wei, Huilong Luo, Zhenghong Ningxia Univ Sch Chem & Chem Engn State Key Lab High Efficiency Utilizat Coal & Gree Yinchuan 750021 Ningxia Peoples R China Shanghai Jiao Tong Univ Sch Chem & Chem Engn State Key Lab Met Matrix Composites Shanghai 200240 Peoples R China

With the shortage of international oil resources and rising oil prices, the acetylene chemical industry has been revitalized. As an important basic raw material in the petrochemical industry, it can produce many chemical products and extend the acetylene production chain. Firstly, the production, conversion, and utilization of acetylene are outlined. Subsequently, this paper focuses on reviewing the core production process routes and the corresponding research progress in the acetylene production chain and points out the development trends of the domestic acetylene downstream conversion. Then, considering that acetylene is produced by calcium carbide technology in Ningdong and the extended downstream polyvinyl chloride industry chain is a large energy consumer and the focus of environmental safety monitoring, the entire process from coal-based calcium carbide acetylene production to polyvinyl chloride synthesis is elaborated from the perspective of process systems engineering. The resource utilization status and bottlenecks in the process are declared, and the direction of process improvement and optimization is proposed. Finally, the entire acetylene production chain is briefly analyzed and reviewed.

关键词： acetylene acetylene production chain calcium carbide method polyvinyl chloride process systems engineering

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A combined genetic algorithm and active learning approach to build and test surrogate models in process systems engineering

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COMPUTERS & CHEMICAL engineering 2024年 181卷

作者： Castro-Amoedo, Rafael Granacher, Julia Marechal, Francois Ecole Polytech Fed Lausanne Ind Proc & Energy Syst Engn IPESE Sion Switzerland

In process systems engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing decision support. Indeed, optimization results need to be obtained efficiently without compromising accuracy or solution quality. Surrogate models are a cheap way of replacing complex ones, while still capturing the intrinsic features that make them unique and valuable. In this work, a methodology to build surrogate models is developed. It combines a genetic algorithm with an active learning method, harvesting the benefits of both approaches - on the one hand leveraging nature-inspired optimization procedures that explore the optimization space area, and conversely a 'smart' approach to adding meaningful sample points to the training stage. The methodology, coined GA-AL, is tested and validated for chemical absorption of CO2 in a biogas mixture inserted in a utility superstructure framework. Nine surrogate modes are tested, with Artificial Neural Networks, Random Forest and Kringing outperforming other approaches, assessed via four performance criteria.

关键词： Surrogates Genetic algorithm Active learning Artificial intelligence Optimization process systems engineering

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A process systems engineering view of environmental impact assessment in renewable and sustainable energy production: Status and perspectives

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COMPUTERS & CHEMICAL engineering 2024年 180卷

作者： Gargalo, Carina L. Yu, Haoshui Vollmer, Nikolaus Arabkoohsar, Ahmad Gernaey, Krist, V Sin, Guerkan Tech Univ Denmark Proc & Syst Engn Ctr PROSYS Dept Chem & Biochem Engn Bldg 228A DK-2800 Lyngby Denmark Aalborg Univ Dept Chem & Biosci Niels Bohrs Vej 8A DK-6700 Esbjerg Denmark Tech Univ Denmark Dept Civil & Mech Engn Lyngby Denmark

With the increasing concern for climate change, renewable and sustainable energy production has attracted considerable attention from the scientific community, industrial practitioners, and policy and decision-makers. There are many technological alternatives for each sub-category of complex sustainable energy systems. Life cycle assessment (LCA) can be an effective tool to compare the environmental impacts of each pathway and identify the most promising alternatives from an environmental impact perspective. This contribution first re-views the environmental assessment methods and tools developed over the years. Secondly, a comprehensive review of the contribution of the PSE community to the environmental impact analysis of renewable energy systems is performed. It is observed that while LCA is the preferred method, these studies differed widely concerning the choice of impact assessment method used, the level of details shared concerning the underlying LCA calculations, and whether or not sensitivity and uncertainty analyses were carried out, among many others. This makes the comparison of results from different studies difficult and often impossible. It is clear that the PSE community, with its emphasis on systems thinking and holistic approaches, plays a critical role in the design, integration, and operation of complex sustainable energy systems. However, the thorough calculations necessary to ensure a robust and transparent LCA analysis require a shared methodology and a detailed description of the rules. Such explicit, systematic, and transparent methods will set the bar for a minimum requirement for thorough LCA calculations, ensuring fair comparison and discussions of different technical solutions developed in the wider PSE community for sustainable renewables.

关键词： Renewable and sustainable energy systems Environmental impact assessment process systems engineering Life cycle assessment Sustainability

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Reloading process systems engineering within Chemical engineering

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CHEMICAL engineering RESEARCH & DESIGN 2024年 209卷 1-19页

作者： Vassiliadis, Vassilios S. Mappas, Vasileios Espaas, Thomas A. Dorneanu, Bogdan Isafiade, Adeniyi Moller, Klaus Arellano-Garcia, Harvey Univ Cambridge 5 Ammohostou St CY-7550 Larnax Cyprus Univ Cambridge Dept Chem Engn & Biotechnol Philippa Fawcett Dr Cambridge CB3 0AS England Brandenburg Tech Univ Cottbus Senftenberg LS Prozess & Anlagentech D-03046 Cottbus Germany Univ Cape Town Dept Chem Engn ZA-7700 Rondebosch South Africa

Established as a sub-discipline of Chemical engineering in the 1960s by the late Professor R.W.H. Sargent at Imperial College London, process systems engineering (PSE) has played a significant role in advancing the field, positioning it as a leading engineering discipline in the contemporary technological landscape. Rooted in Applied Mathematics and Computing, PSE aligns with the key components driving advancements in our modern, information-centric era. Sargent's visionary foresight anticipated the evolution of early computational tools into fundamental elements for future technological and scientific breakthroughs, all while maintaining a central focus on Chemical engineering. This paper aims to present concise and concrete ideas for propelling PSE into a new era of progress. The objective is twofold: to preserve PSE's extensive and diverse knowledge base and to reposition it more prominently within modern Chemical engineering, while also establishing robust connections with other data-driven engineering and applied science domains that play important roles in industrial and technological advancements. Rather than merely reacting to contemporary challenges, this article seeks to proactively create opportunities to lead the future of Chemical engineering across its vital contributions in education, research, technology transfer, and business creation, fully leveraging its inherent multidisciplinarity and versatile character.

关键词： Chemical engineering process systems engineering process model construction and deployment Digital Twinning Machine Learning

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Generative AI and process systems engineering: The next frontier

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COMPUTERS & CHEMICAL engineering 2024年 187卷

作者： Decardi-Nelson, Benjamin Alshehri, Abdulelah S. Ajagekar, Akshay You, Fengqi Cornell Univ Syst Engn Ithaca NY 14853 USA Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA King Saud Univ Coll Engn Dept Chem Engn Riyadh 11421 Saudi Arabia

This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, generalpurpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

关键词： Generative AI process systems engineering Large language models Multiscale

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Beyond the Colours of Hydrogen: Opportunities for process systems engineering in Hydrogen Economy

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process INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2023年第4期7卷 941-950页

作者： Chew, Yick Eu Cheng, Xin Hui Loy, Adrian Chun Minh How, Bing Shen Andiappan, Viknesh Univ Nottingham Malaysia Dept Chem & Environm Engn Semenyih 43500 Selangor Malaysia Heriot Watt Univ Malaysia Sch Engn & Phys Sci 1Jalan Venna P5-2Precinct 5 Putrajaya 62200 Wilayah Perseku Malaysia Monash Univ Dept Chem & Biol Engn Melbourne Vic 3800 Australia Swinburne Univ Technol Sarawak Fac Engn Res Ctr Sustainable Technol Biomass Waste to Wealth Special Interest GrpRes C Jalan Simpang Tiga Kuching 93350 Sarawak Malaysia

In the midst of a climate crisis, alternative and low-carbon energy resources must be put to scale in order to achieve carbon emission reductions in the coming decades. In this respect, hydrogen has gained attention as an alternative energy carrier. Hydrogen can be produced from methods that are commonly classified by a range of colours. However, each hydrogen source has its own challenges in terms of energy security, energy equity, and environmental sustainability. This perspective offers insights about the critical role that process systems engineering (PSE) will play in addressing these key challenges. We also present suggestions on possible future PSE studies in the area of the hydrogen economy.

关键词： Hydrogen economy Hydrogen value chains process systems engineering Energy trilemma

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Domain-Informed Language Models for process systems engineering

Domain-Informed Language Models for Process Systems Engineer...

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作者： Mann, Vipul Columbia University

学位级别：Ph.D., Doctor of Philosophy

process systems engineering (PSE) involves a systems-level approach to solving problems in chemical engineering related to process modeling, design, control, and optimization and involves modeling interactions between various systems (and subsystems) governing the process. This requires using a combination of mathematical methods, physical intuition, and recently machine learning techniques. Recently, language models have seen tremendous advances due to new and more efficient model architectures (such as transformers), computing power, and large volumes of training data. Many of these language models could be appropriately adapted to solve several PSE-related problems. However, language models are inherently complex and are often characterized by several million parameters, which could only be trained efficiently in data-rich areas, unlike PSE. Moreover, PSE is characterized by decades of rich process knowledge that must be utilized during model training to avoid mismatch between process knowledge and data-driven language models. This thesis presents a framework for building domain-informed language models for several central problems in PSE spanning multiple scales. Specifically, the frameworks presented include molecular property prediction, forward and retrosynthesis reaction outcome prediction, chemical flowsheet representation and generation, pharmaceutical information extraction, and reaction classification. Domain knowledge is integrated with language models using custom model architectures, standard and custom-built ontologies, linguistics-inspired chemistry and process flowsheet grammar, adapted problem formulations, graph theory techniques, and so on. This thesis is intended to provide a path for future developments of domain-informed language models in process systems engineering that respect domain knowledge, but leverage their computational advantages.

关键词： Subsystems Computational advantages Language models Machine learning process systems engineering

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