Applied AI Techniques in the Process Industry : From Molecular Design to Process Design and Optimization

in the field, Applied AI Techniques in the Process Industry includes information on: Integration of observed data and reaction mechanisms in deep learning for designing sustainable glycolic acid Machine learning-aided rational screening of task-specific ionic liquids and AI for property modeling and solvent tailoring Integration of incomplete prior knowledge into data-driven inferential sensor models under the variational Bayesian framework AI-aided high-throughput screening, optimistic design of MOF materials for adsorptive gas separation, and reduced-order modeling and optimization of cooling tower systems Surrogate modeling for accelerating optimization of complex systems in chemical engineering Applied AI Techniques in the Process Industry is an essential reference on the subject for process, chemical, and pharmaceutical engineers seeking to improve physical interpretability in data-driven models to enable usage that scales with a system and reduce inaccuracies and mismatch issues. [Publisher's Text]

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