Applied AI Techniques in the Process Industry

From Molecular Design to Process Design and Optimization
Herausgegeben:He, Chang; Ren, Jingzheng

• Gebundenes Buch

Produktbeschreibung

Data-driven and first principles models for energy-relevant systems and processes approached through various in-depth case studies.

Produktdetails

• Verlag: Wiley-VCH
• Artikelnr. des Verlages: 1135339 000
• 1. Auflage
• Seitenzahl: 336
• Erscheinungstermin: 15. Januar 2025
• Englisch
• Abmessung: 250mm x 179mm x 22mm
• Gewicht: 785g
• ISBN-13: 9783527353392
• ISBN-10: 3527353399
• Artikelnr.: 70928791

Autorenporträt

Dr. Chang He 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. Dr. Jingzheng Ren 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).

Inhaltsangabe

Chapter 1: Integrating Data-Driven Modeling with First-Principles Knowledge
Chapter 2: Advanced algorithms for Hybrid Data-driven Modelling
Chapter 3: A computational Framework for Model-based Design and Optimization of Dynamic and Cyclic Membrane Processes
Chapter 4: AI-Aided Optimization and Design of MOF Materials for Gas Separation
Chapter 5: Machine Learning Aided Materials and Process Integration Design for High-Efficiency Gas Separation
Chapter 6: Data-driven Screening of High-performance Ionic Liquids
Chapter 7: Hunting for Aromatic Chemicals with AI Techniques
Chapter 8: AI-assisted Drug Design and Production
Chapter 9: Designing a Heat Exchanger by Combining Physics-Informed Deep Learning and Transfer Learning
Chapter 10: Catalyst Design Based on Machine Learning
Chapter 11: Surrogate Models for Sustainability Optimization of Complex Industrial System
Chapter 12: Advanced Machine Learning and Deep Learning Models for Chemical Process Control and Process Data Analytics