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<br>Chapter 2: Advanced algorithms for Hybrid Data-driven Modelling<br>Chapter 3: A computational Framework for Model-based Design and Optimization of Dynamic and Cyclic Membrane Processes<br>Chapter 4: AI-Aided Optimization and Design of MOF Materials for Gas Separation<br>Chapter 5: Machine Learning Aided Materials and Process Integration Design for High-Efficiency Gas Separation<br>Chapter 6: Data-driven Screening of High-performance Ionic Liquids<br>Chapter 7: Hunting for Aromatic Chemicals with AI Techniques<br>Chapter 8: AI-assisted Drug Design and Production<br>Chapter 9: Designing a Heat Exchanger by Combining Physics-Informed Deep Learning and Transfer Learning<br>Chapter 10: Catalyst Design Based on Machine Learning<br>Chapter 11: Surrogate Models for Sustainability Optimization of Complex Industrial System<br>Chapter 12: Advanced Machine Learning and Deep Learning Models for Chemical Process Control and Process Data Analytics<br>
| ISBN | 9783527353392 |
|---|---|
| Artikelnummer | 9783527353392 |
| Medientyp | Buch |
| Auflage | 1. Auflage |
| Copyrightjahr | 2024 |
| Verlag | Wiley-VCH |
| Umfang | 336 Seiten |
| Abbildungen | 33 Tabellen |
| Sprache | Englisch |
