Applied AI Techniques in the Process Industry
From Molecular Design to Process Design and Optimization
Applied AI Techniques in the Process Industry
From Molecular Design to Process Design and Optimization
Data-driven and first principles models for energy-relevant systems and processes approached through various in-depth case studies.
Chapter 1: Integrating Data-Driven Modeling with First-Principles Knowledge
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
| ISBN | 9783527353392 |
|---|---|
| Artikelnummer | 9783527353392 |
| Medientyp | Buch |
| Auflage | 1. Auflage |
| Copyrightjahr | 2025 |
| Verlag | Wiley-VCH |
| Umfang | 336 Seiten |
| Abbildungen | 33 Tabellen |
| Sprache | Englisch |
