Название: Integrated Process Design and Operational Optimization via Multi-parametric Programming, Second Edition
Автор: Baris Burnak, Nikolaos A. Diangelakis, Efstratios N. Pistikopoulos
Издательство: Springer
Год: 2026
Страниц: 233
Язык: английский
Формат: pdf (true), epub
Размер: 24.3 MB

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. The authors explain how 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. This book details the foundations of systematic model based strategies for simultaneous process design, scheduling, and control optimization to achieve reduced cost and improved energy consumption in process systems. The authors approach the following questions: When does the need for such an integration of scales arise? Which types of industrially relevant problems and applications can be considered for such an approach? Where do we stand regarding methodological developments and solution strategies as enablers and tools for such an integration? If desired, what would be the ideal framework and potentially the target software platform for achieving such a unification? This Second Edition includes developments in methodological approaches and software platform unification, as well as discussion on the need for integration.

In this book, we present a systematic, process agnostic theory and framework to simultaneously account for process design, scheduling, and optimal control problems based on a model based multi-parametric programming approach. This novel methodology has been developed over the last 20 years using the state-of-the-art techniques in operations research and chemical engineering disciplines. In this first chapter, we present a historical perspective on the milestones in model-based design optimization techniques and the developed tools to solve the resulting complex problems. We examine the progress spanning more than five decades, from the early flexibility analysis and optimal process design under uncertainty to more recent developments on the simultaneous consideration of process design, scheduling, and control. In Chap. 2, we present the concept of mixed-integer dynamic optimization (MIDO) along with a rigorous algorithm to solve this class of problems. MIDO is one of the two fundamental constituents of the framework to integrate process decisions that will be discussed throughout the book. Therefore here, we will explore how to incorporate process design and classical control problems, demonstrated on a well-established binary distillation column model. The second fundamental concept to have an in-depth understanding is model based advanced controller design and the theory of multi-parametric programming, which is discussed in Chap. 3. Model Predictive Control (MPC) has become increasingly more popular over the years due to its strength in imposing dynamic constraints on process variables and has been applied to a plethora of applications in numerous industries. However, due to the implicit nature of the MPC structure, it is not trivial to integrate in an MIDO as discussed in Chap. 2. Hence in Chap. 3, we will introduce the PARametric Optimisation and Control (PAROC) framework, which allows for deriving the exact explicit expressions for an MPC problem. In the following chapters, we will use these concepts in tandem to develop the complete framework to integrate process design, scheduling, and control decisions. In Chap. 4, we will first consider the design and control problems, where the control strategy is an MPC, as opposed to the classical PID control in Chap. 2. In Chap. 5, we will cover the scheduling and control problem, and finally in Chap. 6, we will put all pieces together for the complete framework.

This book is intended for readers with a foundational understanding of mathematical programming and differential-algebraic equations, as well as basic familiarity with industrial practices in process design and optimization. We invite the reader to explore the power of multi-parametric programming as a transformative approach to integrating process design and operational optimization, bridging theory and practice to drive more efficient and intelligent decision-making in complex systems.