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

Abstract

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).