Planning and Integration of Refinery and Petrochemical Operations E-Book

Khalid Y. Al-Qahtani

• Herausgeber: Wiley-VCH
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 253

Veröffentlichungsjahr: 2011

Beschreibung

Clearly divided into three main sections, this practical book familiarizes readers with the area of planning in petroleum refining and petrochemical industry, while introducing several planning and modeling strategies encompassing single site refinery plants, multiple refinery networks, petrochemical networks, and refinery and petrochemical planning systems. It equally provides an insight into possible research directions and recommendations for the area of refinery and petrochemical planning. Furthermore, several appendices are included to explain the general background necessary, including stochastic programming, chance constraint programming, and robust optimization. For engineers and managers working in the petroleum industry as well as academic researchers in production, logistics, and supply chain management.

Leseprobe

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The Authors

Prof. Khalid Y. Al-Qahtani

Saudi Aramco

Process & Control Systems Dept

R-E-2790, Engin. Bldg (728A)

31311 Dhahran

Saudi Arabien

Prof. Ali Elkamel

University of Waterloo

Dept. of Chemical Engineering

University Avenue West 200

Waterloo, ON N2L 3G1

Kanada

All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

Library of Congress Card No.: applied for

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

Bibliographic information published by the Deutsche Nationalbibliothek

The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de.

© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.

ISBN: 978-3-527-32694-5

Contents

Cover

Related Titles

Titlepage

Copyright

Preface

Part One: Background

Chapter 1: Petroleum Refining and Petrochemical Industry Overview

1.1 Refinery Overview

1.2 Mathematical Programming in Refining

1.3 Refinery Configuration

1.4 Petrochemical Industry Overview

1.5 Petrochemical Feedstock

1.6 Refinery and Petrochemical Synergy Benefits

References

Part Two: Deterministic Planning Models

Chapter 2: Petroleum Refinery Planning

2.1 Production Planning and Scheduling

2.2 Operations Practices in the Past

2.3 Types of Planning Models

2.4 Regression-Based Planning: Example of the Fluid Catalytic Cracker

2.5 Artificial-Neural-Network-Based Modeling: Example of Fluid Catalytic Cracker

2.6 Yield Based Planning: Example of a Single Refinery

2.7 General Remarks

References

Chapter 3: Multisite Refinery Network Integration and Coordination

3.1 Introduction

3.2 Literature Review

3.3 Problem Statement

3.4 Model Formulation

3.5 Illustrative Case Study

3.6 Conclusion

References

Chapter 4: Petrochemical Network Planning

4.1 Introduction

4.2 Literature Review

4.3 Model Formulation

4.4 Illustrative Case Study

4.5 Conclusion

References

Chapter 5: Multisite Refinery and Petrochemical Network Integration

5.1 Introduction

5.2 Problem Statement

5.3 Model Formulation

5.4 Illustrative Case Study

5.5 Conclusion

References

Part Three: Planning Under Uncertainty

Chapter 6: Planning Under Uncertainty for a Single Refinery Plant

6.1 Introduction

6.2 Problem Definition

6.3 Deterministic Model Formulation

6.4 Stochastic Model Formulation

6.5 Analysis Methodology

6.6 Illustrative Case Study

6.7 General Remarks

Nomenclature and Notation

References

Chapter 7: Robust Planning of Multisite Refinery Network

7.1 Introduction

7.2 Literature Review

7.3 Model Formulation

7.4 Sample Average Approximation (SAA)

7.5 Illustrative Case Study

7.6 Conclusion

References

Chapter 8: Robust Planning for Petrochemical Networks

8.1 Introduction

8.2 Model Formulation

8.3 Value to Information and Stochastic Solution

8.4 Illustrative Case Study

8.5 Conclusion

References

Chapter 9: Stochastic Multisite Refinery and Petrochemical Network Integration

9.1 Introduction

9.2 Model Formulation

9.3 Scenario Generation

9.4 Illustrative Case Study

9.5 Conclusion

References

Appendix A: Two-Stage Stochastic Programming

Appendix B: Chance Constrained Programming

Appendix C: SAA Optimal Solution Bounding

Index

Preface

Petroleum refining and the petrochemical industry account for a major share of the world energy and industrial market. In many situations, they represent the economic back-bone of industrial countries. Today, the volatile environment of the market and the continuous change in customer requirements lead to constant pressure to seek opportunities that properly align and coordinate the different components of the industry. In particular, petroleum refining and petrochemical industry coordination and integration is gaining a great deal of interest. Previous attempts in the field either studied the two systems in isolation or assumed limited interactions between them.

This book aims at providing the reader with a detailed understanding of the planning, integration and coordination of multisite refinery and petrochemical networks using proper deterministic and stochastic techniques. The book consists of three parts:

Part 1: BackgroundPart 2: Deterministic Planning ModelsPart 3: Planning under Uncertainty

Part 1, comprised of one chapter, introduces the reader to the configuration of petroleum refining and the petrochemical industry. It also discusses key classifications of petrochemical industry feedstock from petroleum products. The final part explains and proposes possible synergies between the petroleum refinery and the petrochemical industry.

Part 2, comprised of four chapters, focusses on the area of planning in petroleum refining and the petrochemical industry under deterministic conditions. Chapter 2 discusses the model classes used in process planning (i.e., empirical models, and first principle models) and provides a series of case studies to illustrate the concepts and impeding assumptions of the different modeling approaches. Chapter 3 tackles the integration and coordination of a multisite refinery network. It addresses the design and analysis of multisite integration and coordination strategies within a network of petroleum refineries through a mixed-integer linear programming (MILP) technique. Chapter 4 explains the general representation of a petrochemical planning model which selects the optimal network from the overall petrochemical superstructure. The system is modeled as a MILP problem and is illustrated via a numerical example. Chapter 5 addresses the integration between the multisite refinery system and the petrochemical industry. The chapter develops a framework for the design and analysis of possible integration and coordination strategies of multisite refinery and petrochemical networks to satisfy given petroleum and chemical product demand. The main feature of the proposed approach is the development of a methodology for the simultaneous analysis of process network integration within a multisite refinery and petrochemical system. Part 2 of this book serves as a foundation for the reader of Part 3.

Part 3, comprised of four chapters, tackles the area of planning in the petroleum refinery and the petrochemical industry under uncertainty. Chapter 6 explains the use of two-stage stochastic programming and the incorporation of risk management for a single site refinery plant. The example used in this chapter is simple enough for the reader to grasp the concept of two-stage stochastic programming and risk management and to be prepared for the larger scale systems in the remaining chapters. Chapter 7 extends the proposed model in Chapter 3 to account for model uncertainty by means of two-stage stochastic programming. Parameter uncertainty was considered and included coefficients of the objective function and right-hand-side parameters in the inequality constraints. Robustness is analyzed based on both model robustness and solution robustness, where each measure is assigned a scaling factor to analyze the sensitivity of the refinery plan and the integration network due to variations. The proposed technique makes use of the sample average approximation (SAA) method with statistical bounding techniques to give an insight on the sample size required to give adequate approximation of the problem. Chapter 8 addresses the planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous chapter, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty considered in this part includes process yield, raw material and product prices, and lower product market demand. The expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) are also investigated to illustrate numerically the value of including the randomness of the different model parameters. Chapter 9 extends the petroleum refinery and petrochemical industry integration problem, explained in Chapter 5, to consider different sources of uncertainties in model parameters. Parameter uncertainty considered includes imported crude oil price, refinery product price, petrochemical product price, refinery market demand, and petrochemical lower level product demand. The sample average approximation (SAA) method is within an iterative scheme to generate the required scenarios and provide solution quality by measuring the optimality gap of the final solution.

All chapters are equipped with clear figures and tables to help the reader understand the included topics. Furthermore, several appendices are included to explain the general background in the area of stochastic programming, chance constraint programming and robust optimization.

Part One

Background

Chapter 1

Petroleum Refining and Petrochemical Industry Overview

Petroleum refining and the petrochemical industry account for a major share in the world energy and industrial market. In many situations, they represent the economic back-bone of industrial countries. Today, the volatile environment of the market and the continuous change in customer requirements lead to constant pressure to seek opportunities that properly align and coordinate the different components of the industry. In particular, petroleum refining and petrochemical industry coordination and integration is gaining a great deal of interest.

In this chapter, we will give an overview of the process configurations of petroleum refining and the petrochemical industry. We will also discuss the key classifications of petrochemical industry feedstock from petroleum products and explain and propose possible synergies between the petroleum refinery and the petrochemical industry.

1.1 Refinery Overview

The first refinery was built in Titusville, Pennsylvania in 1860 at a cost of $15 000 (Nelson, 1958). This refinery and other refineries at that time only used batch distillation to separate kerosene and heating oil from other crude fractions. During the early years, refining separation was performed using batch processing. However, with the increase in demand for petroleum products, continuous refining became a necessity. The first widely recognized continuous refinery plants emerged around 1912 (Nelson, 1958). With the diversity and complexity of the demand for petroleum products, the refining industry has developed from a few simple processing units to very complex production systems. A simplified process flow diagram of a typical modern refinery is shown in . For a detailed history of the evolution of refining technologies, we refer the reader to Nelson (1958) and Wilson (1997).