Author Description

<p>Thorough discussion of data-driven and first principles models for energy-relevant systems and processes, approached through various in-depth case studies<br></p><p>Applied AI Techniques in the Process Industry 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.<br></p><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.<br></p><p>Edited by two highly qualified academics and contributed to by a number of leading experts in the field, Applied AI Techniques in the Process Industry includes information on:<br></p><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>Applied AI Techniques in the Process Industry is an essential reference on the subject for process, chemical, and pharmaceutical engineers seeking to improve physic