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.
Table of Contents
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
