
Introduction to Process Control
Description
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Discusses smart manufacturing, new data preprocessing techniques, and machine learning and artificial intelligence concepts that are part of current smart manufacturing decisions
Includes extensive references to guide the reader to the resources needed to solve modeling, classification, and monitoring problems
Introduces the link between process optimization and process control (optimizing control), including the effect of disturbances on the optimal plant operation, the concepts of steady-state and dynamic back-off as ways to quantify the economic benefits of control, and how to determine an optimal transition policy during a planned production change
Incorporates an introduction to the modern architectures of industrial computer control systems with real case studies and applications to pilot-scale operations
Analyzes the expanded role of process control in modern manufacturing, including model-centric technologies and integrated control systems
Integrates data processing/reconciliation and intelligent monitoring in the overall control system architecture
Drawing on the authors' combined 60 years of teaching experiences, this classroom-tested text is designed for chemical engineering students but is also suitable for industrial practitioners who need to understand key concepts of process control and how to implement them. The text offers a comprehensive pedagogical approach to reinforce learning and presents a concept first followed by an example, allowing students to grasp theoretical concepts in a practical manner and uses the same problem in each chapter, culminating in a complete control design strategy. A vast number of exercises throughout ensure readers are supported in their learning and comprehension.
Downloadable MATLAB (R) toolboxes for process control education as well as the main simulation examples from the book offer a user-friendly software environment for interactively studying the examples in the text. These can be downloaded from the publisher's website. Solutions manual is available for qualifying professors from the publisher.
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Persons
Ahmet Palazoglu is a professor of chemical engineering and materials science at the University of California, Davis. He earned a PhD in chemical engineering from Rensselaer Polytechnic Institute. Dr. Palazoglu has authored more than 150 publications and has taught short courses to academic and industrial audiences on process monitoring applications. His research interests include process control, nonlinear dynamics, process monitoring, and statistical modeling.
Content
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