LEADER 05040nam 22005533 450 001 9911020153603321 005 20241211080256.0 010 $a9783527845491 010 $a3527845496 010 $a9783527845477 010 $a352784547X 010 $a9783527845484 010 $a3527845488 035 $a(MiAaPQ)EBC31821991 035 $a(Au-PeEL)EBL31821991 035 $a(CKB)36947403000041 035 $a(Perlego)4710960 035 $a(Exl-AI)31821991 035 $a(OCoLC)1478697900 035 $a(EXLCZ)9936947403000041 100 $a20241211d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied AI Techniques in the Process Industry $eFrom Molecular Design to Process Design and Optimization 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2025. 215 $a1 online resource (336 pages) 311 08$a9783527353392 311 08$a3527353399 327 $aCover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 AI for Property Modeling, Solvent Tailoring, and Process Design -- 1.1 AI?Assisted Property Modeling -- 1.2 AI?assisted Solvent Tailoring -- 1.3 AI?Assisted Process Design -- 1.4 Conclusions -- References -- Chapter 2 Hunting for Better Aromatic Chemicals with AI Techniques -- 2.1 Introduction -- 2.2 Machine Learning?Based Odor Prediction Models -- 2.2.1 Odor Predictions for Pure Aromatic Chemicals Using Group?Based Machine Learning Method -- 2.2.1.1 Database Preparation -- 2.2.1.2 Molecular Representation -- 2.2.1.3 Model Architecture -- 2.2.1.4 Results and Discussions -- 2.2.2 Odor Prediction for Mixture Aromatic Chemicals Using ??Profiles?Based Machine Learning Method -- 2.2.2.1 Database Preparation -- 2.2.2.2 Molecular Representation -- 2.2.2.3 Model Architecture -- 2.2.2.4 Results and Discussions -- 2.3 Computer?Aided Aroma Design (CAAD) Framework -- 2.3.1 CAAD for Pure Aromatic Chemicals -- 2.3.1.1 Identify Product Attributes -- 2.3.1.2 Convert Product Attributes to Properties and Their Constraints -- 2.3.1.3 Choose Property Prediction Model for Estimating Properties -- 2.3.1.4 Formulate MILP/MINLP Model$7Generated by AI. 330 8 $aThorough discussion of data-driven and first principles models for energy-relevant systems and processes, approached through various in-depth case studies 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. 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. 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: * 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. 606 $aArtificial intelligence$xIndustrial applications$7Generated by AI 606 $aChemical engineering$xData processing$7Generated by AI 615 0$aArtificial intelligence$xIndustrial applications. 615 0$aChemical engineering$xData processing 676 $a670.28563 700 $aHe$b Chang$01842658 701 $aRen$b Jingzheng$0867490 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020153603321 996 $aApplied AI Techniques in the Process Industry$94422846 997 $aUNINA