{"product_id":"9783527353392-applied-ai-techniques-in-the-process-in","title":"Applied AI Techniques in the Process Industry","description":"\u003cmeta content=\"text\/html; charset=utf-8\" http-equiv=\"Content-Type\"\u003e\u003cp\u003e\u003cspan\u003eFrom Molecular Design to Process Design and Optimization\u003cbr\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\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\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Rarewaves","offers":[{"title":"Default Title","offer_id":57932007440758,"sku":"9783527353392","price":113.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0092\/7504\/8033\/files\/stand_41421594_8da0ca3c-5c5f-4c5b-87d2-f5fdbfadafdf.jpg?v=1779916809","url":"https:\/\/www.rarewaves.com\/products\/9783527353392-applied-ai-techniques-in-the-process-in","provider":"Rarewaves.com","version":"1.0","type":"link"}