This book presents a comprehensive optimization-based theory and framework that exploits the synergistic interactions and tradeoffs between process design and operational decisions that span different time scales. Conventional methods in the process industry often isolate decision making mechanisms with a hierarchical information flow to achieve tractable problems, risking suboptimal, even infeasible operations. In this book, foundations of a systematic model-based strategy for simultaneous process design, scheduling, and control optimization is detailed to achieve reduced cost and improved energy consumption in process systems. The material covered in this book is well suited for the use of industrial practitioners, academics, and researchers.
In Chapter 1, a historical perspective on the milestones in model-based design optimization techniques is presented along with an overview of the state-of-the-art mathematical tools to solve the resulting complex problems. Chapters 2 and 3 discuss two fundamental concepts that are essential for the reader. These concepts are (i) mixed integer dynamic optimization problems and two algorithms to solve this class of optimization problems, and (ii) developing a model based multiparametric programming model predictive control. These tools are used to systematically evaluate the tradeoffs between different time-scale decisions based on a single high-fidelity model, as demonstrated on (i) design and control, (ii) scheduling and control, and (iii) design, scheduling, and control problems. We present illustrative examples on chemical processing units, including continuous stirred tank reactors, distillation columns, and combined heat and power regeneration units, along with discussions of other relevant work in the literature for each class of problems.
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Baris Burnak is a Ph.D. candidate in the Artie McFerrin Department of Chemical Engineering at Texas A&M University. He has worked under the supervision of Prof. Pistikopoulos for five years with a focus on developing a theoretical basis to simultaneously address design and operational receding horizon decisions in process systems. He earned his Bachelor’s and M.Sc. degrees from the Department of Chemical Engineering at Bogazici University, Turkey. Here, he started his research career studying the Fischer-Tropsch synthesis with data-driven modeling and optimization techniques. He has co-authored 11 peer reviewed journal articles and 6 conference proceedings.
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