(167c) AI and Physics-Informed AI in Chemical Engineering: Machine Learning, Multiscale Modeling-Simulation and Digital Twins

The simultaneous use of hierarchical integrated artificial intelligence (AI)-assisted multi-scale modeling-simulation (MMS) is crucial for expediting the development, commercialization, utilization, and problem-solving of new technologies, systems, and processes. These approaches greatly improve the entire technology development process by reducing cost and time and allow us to tackle problems that cannot be solved using AI or MMS methods alone. AI and MMS can naturally complement one another to produce robust predictive models that incorporate the underlying physics in order to manage ill-posed problems and investigate vast design spaces. Physics-Informed Artificial Intelligence (PI+AI) is a new computing paradigm that provides a novel solution to multiphysics modeling challenges commonly encountered in chemical engineering. These challenges frequently involve complex transport processes, nonlinear reaction kinetics, and multiphysics interaction. This study begins with a taxonomy of multiscale modeling-simulation and integrated artificial paradigms and approaches, followed by a study of their strengths and limitations. Then, it focuses on an overview of AI+MMS applications, opportunities, and current research in chemical engineering, such as the design, development, and operation of innovative technologies, systems, and processes. Finally, this study discusses key development issues and future research prospects.