Author Description

<p><b>Thorough discussion of data-driven and first principles models for energy-relevant systems and processes, approached through various in-depth case studies</b> <p><i>Applied AI Techniques in the Process Industry</i> identifies and categorizes the various hybrid models available that integrate data-driven models for energy-relevant systems and processes with different forms of process knowledge and domain expertise. State-of-the-art techniques such as reduced-order modeling, sparse identification, and physics-informed neural networks are comprehensively summarized, along with their benefits, such as improved interpretability and predictive power. <p>Numerous in-depth case studies regarding the covered models and methods for data-driven modeling, process optimization, and machine learning are presented, from screening high-performance ionic liquids and AI-assisted drug design to designing heat exchangers with physics-informed deep learning. <p>Edited by two highly qualified academics and contributed to by a number of leading experts in the field, <i>Applied AI Techniques in the Process Industry</i> includes information on: <ul><li>Integration of observed data and reaction mechanisms in deep learning for designing sustainable glycolic acid</li><li>Machine learning-aided rational screening of task-specific ionic liquids and AI for property modeling and solvent tailoring</li><li>Integration of incomplete prior knowledge into data-driven inferential sensor models under the variational Bayesian framework</li><li>AI-aided high-throughput screening, optimistic design of MOF materials for adsorptive gas separation, and reduced-order modeling and optimization of cooling tower systems</li><li>Surrogate modeling for accelerating optimization of complex systems in chemical engineering</li></ul> <p><i>Applied AI Techniques in the Process Industry</i> is an essential reference on the subject for process, chemical, and pharmaceutical engineers seeking to improve physical interp