Case Studies of Artificial Intelligence in Industrial Fluid and Thermal Processes

This review discusses the application of artificial intelligence (AI) in industrial fluid flow and heat transfer operations. It provides a critical overview of case studies demonstrating the effectiveness of AI-based applications. Ab initio , the scope is set, and it is on various industrial applications such as chemical processing, power generation, and manufacturing, where fluid dynamics and heat transfer are significant. The overarching goal is to harness AI to automate processes, lower energy consumption, improve system efficiency, and predict maintenance needs, thereby reducing operational costs and promoting sustainability. Machine learning (ML), neural networks (NN), and predictive modeling are the AI approaches employed. These methods are utilized in conjunction with computational fluid dynamics (CFD) simulations as well as online data analysis to gain a better understanding of process control and decision making. To a greater extent, the review is focused on specific industrial case studies where AI has been utilized efficiently, such as heat exchanger optimization, improvement in reactor fluid mixing, and electronic component thermal management. All case studies are analyzed on the grounds of problem definition, the development of the AI model, and evaluation. Key findings from the case studies that were conducted were that AI could potentially increase prediction accuracy for heat transfer and fluid flow, and could lead to better process optimization and planning of resources. Issues associated with quality data demand and integrating AI with existing installed industrial systems were noted, and recommendations were provided on how to work around these drawbacks.