Applications of Artificial Intelligence in Process Systems Engineering 1st Edition

Applications of Artificial Intelligence in Process Systems Engineering 1st Edition- Ebook Instant Download/Delivery ISBN(s): 9780128210925,0128210923,9780128217436, 012821743X

Product details:

• ISBN-10 ‏ : ‎012821743X
• ISBN-13 ‏ : ‎ 9780128217436
• Author: Jingzheng Ren

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.

Table contents:

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

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