{"product_id":"applied-ai-techniques-in-the-process-industry-from-molecular-design-to-process-design-and-optimization-9783527353392","title":"Applied AI Techniques in the Process Industry: From Molecular Design to Process Design and Optimization","description":"\u003cp\u003e • Author(s): Chang He\u003cbr\u003e • Publisher: Wiley-Vch\u003cbr\u003e • Publisher Imprint: Wiley-Vch\u003cbr\u003e • BISAC: Artificial Intelligence - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThorough discussion of data-driven and first principles models for energy-relevant systems and processes, approached through various in-depth case studies\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplied AI Techniques in the Process Industry\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eNumerous 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. \u003c\/p\u003e\u003cp\u003eEdited by two highly qualified academics and contributed to by a number of leading experts in the field, \u003ci\u003eApplied AI Techniques in the Process Industry\u003c\/i\u003e 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 \u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplied AI Techniques in the Process Industry\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley-Vch","offers":[{"title":"Hardcover","offer_id":45201532649623,"sku":"9783527353392","price":11158.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783527353392.webp?v=1768573551","url":"https:\/\/atlanticbooks.com\/products\/applied-ai-techniques-in-the-process-industry-from-molecular-design-to-process-design-and-optimization-9783527353392","provider":"Atlantic Books","version":"1.0","type":"link"}