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Integrated Process Design and Operational Optimization via Multiparametric Programming [electronic resource] / by Baris Burnak, Nikolaos A. Diangelakis, Efstratios N. Pistikopoulos.

By: Burnak, Baris [author.].
Contributor(s): Diangelakis, Nikolaos A [author.] | Pistikopoulos, Efstratios N [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Engineering, Science, and Technology: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XV, 242 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031020896.Subject(s): Engineering design | Materials | Professional education | Vocational education | Engineering Design | Materials Engineering | Professional and Vocational EducationAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 620.0042 Online resources: Click here to access online
Contents:
Acknowledgments -- An Introduction to the Grand Unification of Process Design and Operational Optimization -- Mixed-Integer Dynamic Optimization for Simultaneous Process Design and Control -- PAROC: PARametric Optimization and Control Framework -- Integrating Process Design Optimization and Advanced Model-Based Control Strategies -- Process Scheduling and Control via Multiparametric Programming -- Simultaneous Process Design, Scheduling, and Advanced Model-Based Control -- Bibliography -- Authors' Biographies.
In: Springer Nature eBookSummary: 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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Acknowledgments -- An Introduction to the Grand Unification of Process Design and Operational Optimization -- Mixed-Integer Dynamic Optimization for Simultaneous Process Design and Control -- PAROC: PARametric Optimization and Control Framework -- Integrating Process Design Optimization and Advanced Model-Based Control Strategies -- Process Scheduling and Control via Multiparametric Programming -- Simultaneous Process Design, Scheduling, and Advanced Model-Based Control -- Bibliography -- Authors' Biographies.

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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