Applications of Artificial Intelligence in Process Systems Engineering

Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong

• Publisher: Elsevier
• Year: 2021
• Print ISBN: 9780128210925
• eBook ISBN: 9780128217436
• Topic: Technology & Engineering
• Subtopic: Chemical & Biochemical Engineering
• Index: Technology & Engineering

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering

Table of contents

• Applications of Artificial Intelligence in Process Systems Engineering
• Cover
• Title Page
• Copyright
• Table of Contents
• Contributors
• Chapter 1 Artificial intelligence in process systems engineering
• Chapter 2 Deep learning in QSPR modeling for the prediction of critical properties
• Chapter 3 Predictive deep learning models for environmental properties
• Chapter 4 Automated extraction of molecular features in machine learning-based environmental property prediction
• Chapter 5 Intelligent approaches to forecast the chemical property: Case study in papermaking process
• Chapter 6 Machine learning-based energy consumption forecasting model for process industry—Hybrid PSO-LSSVM algorithm electricity consumption forecasting model for papermaking process
• Chapter 7 Artificial intelligence algorithm application in wastewater treatment plants: Case study for COD load prediction
• Chapter 8 Application of machine learning algorithms to predict the performance of coal gasification process
• Chapter 9 Artificial neural network and its applications: Unraveling the efficiency for hydrogen production
• Chapter 10 Fault diagnosis in industrial processes based on predictive and descriptive machine learning methods
• Chapter 11 Application of artificial intelligence in modeling, control, and fault diagnosis
• Chapter 12 Integrated machine learning framework for computer-aided chemical product design
• Chapter 13 Machine learning methods in drug delivery
• Chapter 14 On the robust and stable flowshop scheduling under stochastic and dynamic disruptions
• Chapter 15 Bi-level model reductions for multiscale stochastic optimization of cooling water system
• Chapter 16 Artificial intelligence algorithm-based multi-objective optimization model of flexible flow shop smart scheduling
• Chapter 17 Machine learning-based intermittent equipment scheduling model for flexible production process
• Chapter 18 Artificial intelligence algorithms for proactive dynamic vehicle routing problem