Advanced Optimization For Process Systems Engineering
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Advanced Optimization for Process Systems Engineering
Author: Ignacio E. Grossmann
language: en
Publisher: Cambridge University Press
Release Date: 2021-03-25
A unique text covering basic and advanced concepts of optimization theory and methods for process systems engineers. With examples illustrating key concepts and algorithms, and exercises involving theoretical derivations, numerical problems and modeling systems, it is ideal for single-semester, graduate courses in process systems engineering.
Applications of Artificial Intelligence in Process Systems 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
Advanced Optimization for Motion Control Systems
Precision motion control is strongly required in many fields, such as precision engineering, micromanufacturing, biotechnology, and nanotechnology. Although great achievements have been made in control engineering, it is still challenging to fulfill the desired performance for precision motion control systems. Substantial works have been presented to reveal an increasing trend to apply optimization approaches in precision engineering to obtain the control system parameters. In this book, we present a result of several years of work in the area of advanced optimization for motion control systems. The book is organized into two parts: Part I focuses on the model-based approaches, and Part II presents the data-based approaches. To illustrate the practical appeal of the proposed optimization techniques, theoretical results are verified with practical examples in each chapter. Industrial problems explored in the book are formulated systematically with necessary analysis of the control system synthesis. By virtue of the design and implementation nature, this book can be used as a reference for engineers, researchers, and students who want to utilize control theories to solve the practical control problems. As the methodologies have extensive applicability in many control engineering problems, the research results in the field of optimization can be applied to full-fledged industrial processes, filling in the gap between research and application to achieve a technology frontier increment.