{"product_id":"9783527353392","title":"Applied AI Techniques in the Process Industry From Molecular Design to Process Design and Optimization","description":"\u003ch1\u003eApplied AI Techniques in the Process Industry\u003c\/h1\u003e\u003ch2\u003eFrom Molecular Design to Process Design and Optimization\u003c\/h2\u003e\u003ch3\u003eChang He | Jingzheng Ren\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eScience \/ Chemistry \/ Physical \u0026amp; Theoretical\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\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\n\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\n\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\n\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: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntegration of observed data and reaction mechanisms in deep learning for designing sustainable glycolic acid\u003c\/li\u003e\n\u003cli\u003eMachine learning-aided rational screening of task-specific ionic liquids and AI for property modeling and solvent tailoring\u003c\/li\u003e\n\u003cli\u003eIntegration of incomplete prior knowledge into data-driven inferential sensor models under the variational Bayesian framework\u003c\/li\u003e\n\u003cli\u003eAI-aided high-throughput screening, optimistic design of MOF materials for adsorptive gas separation, and reduced-order modeling and optimization of cooling tower systems\u003c\/li\u003e\n\u003cli\u003eSurrogate modeling for accelerating optimization of complex systems in chemical engineering\u003c\/li\u003e\n\u003c\/ul\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\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003ci\u003e\u003cb\u003eDr. Chang He\u003c\/b\u003e is an Associate Professor in the School of Chemical Engineering and Technology, Sun Yat-Sen University. His research focuses on the multi-scale integration, design, optimization, and sustainability of the advanced energy systems.\u003c\/i\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003e\u003cb\u003eDr. Jingzheng Ren\u003c\/b\u003e is currently an Associate Professor at The Hong Kong Polytechnic University. He received the 2022 Asia-Pacific Economic Cooperation (APEC) Science Prize for Innovation, Research and Education (ASPIRE Prize).\u003c\/i\u003e \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e17 March 2025\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-VCH\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783527353392\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e336\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e24.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":45369326731404,"sku":"9783527353392","price":132.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783527353392.jpg?v=1780617287","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783527353392","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}