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

This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.

Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers.

This book highlights the analytics and optimization issues in industry, to propose new approaches, and to present applications of innovative approaches in real facilities. In the past few decades there has been an exponential rise in the application of artificial intelligence for solving complex and intricate problems arising in industrial domain. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The book is edited to serve a broad readership, including computer scientists, medical professionals, and mathematicians interested in studying computational intelligence and their applications. It will also be helpful for researchers, graduate and undergraduate students with an interest in the fields of Artificial Intelligence and Industrial problems. This book will be a useful resource for researchers, academicians as well as professionals interested in the highly interdisciplinary field of Artificial Intelligence.

Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. - Allows the reader to learn AI quickly using inexpensive personal computers - Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions - Includes a computer diskette for an illustrated case study - Demonstrates an expert system for separation synthesis (EXSEP) - Presents a detailed review of published literature on expert systems and neural networks in chemical engineering

A comprehensive overview of current developments and applications in biofuels production Process Systems Engineering for Biofuels Development brings together the latest and most cutting-edge research on the production of biofuels. As the first book specifically devoted to process systems engineering for the production of biofuels, Process Systems Engineering for Biofuels Development covers theoretical, computational and experimental issues in biofuels process engineering. Written for researchers and postgraduate students working on biomass conversion and sustainable process design, as well as industrial practitioners and engineers involved in process design, modeling and optimization, this book is an indispensable guide to the newest developments in areas including: Enzyme-catalyzed biodiesel production Process analysis of biodiesel production (including kinetic modeling, simulation and optimization) The use of ultrasonification in biodiesel production Thermochemical processes for biomass transformation to biofuels Production of alternative biofuels In addition to the comprehensive overview of the subject of biofuels found in the Introduction of the book, the authors of various chapters have provided extensive discussions of the production and separation of biofuels via novel applications and techniques.

This book provides an application-focused exposition of modern ML tools that have proven useful in process industry and hands-on illustrations on how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, inferential modeling, dimensionality reduction, and process control. This book considers unique characteristics of industrial process data and uses real data from industrial systems for illustrations. With the focus on practical implementation and minimal programming or ML prerequisites, the book covers the gap in available ML resources for industrial practitioners. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning. The readers will find all the resources they need to deal with high-dimensional, correlated, noisy, corrupted, multimode, and nonlinear process data. The book has been divided into four parts. Part 1 provides a perspective on the importance of ML in process systems engineering and lays down the basic foundations of ML. Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the various characteristics of industrial process systems. Part 3 is focused on artificial neural networks and deep learning. Part 4 covers the important topic of deploying ML solutions over web and shows how to build a production-ready process monitoring web application. Broadly, the book covers the following: Varied applications of ML in process industry Fundamentals of machine learning workflow Practical methodologies for pre-processing industrial data Classical ML methods and their application for process monitoring, fault diagnosis, and soft sensing Deep learning and its application for predictive maintenance Reinforcement learning and its application for process control Deployment of ML solution over web