Planning and integration of refinery and petrochemical operations

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.

Author Notes

Dr. Khaild Al-Qahtani is a senior process engineer at Saudi Aramco, Saudi Arabia. He holds a B.S. in Chemical Engineering from King Fahd University of Petroleum Minerals and a Ph.D. in Chemical Engineering from the University of Waterloo. He worked for more than 10 years in the industry as a process engineer spanning the area of oil treatment, gas processing and refining operations. Dr. Al-Qahtani is a member in different scientific societies and published his work in several refereed journals and international conferences.
Ali Elkamel is a professor of Chemical Engineering at the University of Waterloo, Canada. He holds a B.S. in Chemical and Petroleum Refining Engineering and a B.S. in Mathematics from Colorado School of Mines, an M.S. in Chemical Engineering from the University of Colorado-Boulder, and a PhD in Chemical Engineering from Purdue University. Prof. Elkamel's specific research interests are in computer-aided modeling, optimization, and simulation with applications to the petroleum and petrochemical industry. Prior to joining the University of Waterloo, he was at Purdue University, PG, Italy, Kuwait University, and the University of Wisconsin, Madison. He has also taught a number of short courses to industry, including optimization and cost awareness, quantitative decision making, computer-aided problem solving, refinery economics and planning, and practical process engineering. He has contributed more than 200 publications in refereed journals and international conference proceedings and serves on the editorial board of several journals, including the International Journal of Process Systems Engineering, Engineering Optimization, Int. J. Oil, Gas, Coal Technology, and the Open Fuels Energy Science Journal.

Preface	p. ix
Part 1 Background	p. 1
1 Petroleum Refining and Petrochemical Industry Overview	p. 3
1.1 Refinery Overview	p. 3
1.2 Mathematical Programming in Refining	p. 5
1.3 Refinery Configuration	p. 7
1.3.1 Distillation Processes	p. 7
1.3.2 Coking and Thermal Processes	p. 8
1.3.3 Catalytic Processes	p. 9
1.3.3.1 Cracking Processes	p. 9
1.3.3.2 Alteration Processes	p. 9
1.3.4 Treatment Processes	p. 10
1.3.5 Product Blending	p. 10
1.4 Petrochemical Industry Overview	p. 11
1.5 Petrochemical Feedstock	p. 12
1.5.1 Aromatics	p. 12
1.5.2 Olefins	p. 13
1.5.3 Normal Paraffins and Cyclo-Paraffins	p. 13
1.6 Refinery and Petrochemical Synergy Benefits	p. 14
1.6.1 Process Integration	p. 14
1.6.2 Utilities Integration	p. 15
1.6.3 Fuel Gas Upgrade	p. 16
References	p. 16
Part 2 Deterministic Planning Models	p. 19
2 Petroleum Refinery Planning	p. 21
2.1 Production Planning and Scheduling	p. 21
2.2 Operations Practices in the Past	p. 23
2.3 Types of Planning Models	p. 24
2.4 Regression Based Planning: Example of the Fluid Catalytic Cracker	p. 24
2.4.1 Fluid Catalytic Cracking Process	p. 25
2.4.2 Development of FCC Process Correlation	p. 27
2.4.3 Model Evaluation	p. 31
2.4.4 Integration within an LP for a Petroleum Refinery	p. 31
2.5 Artificial-Neural-Network-Based Modeling: Example of Fluid Catalytic Cracker	p. 36
2.5.1 Artificial Neural Networks	p. 36
2.5.2 Development of FCC Neural Network Model	p. 37
2.5.3 Comparison with Other Models	p. 39
2.6 Yield Based Planning: Example of a Single Refinery	p. 44
2.6.1 Model Formulation	p. 46
2.6.1.1 Limitations on Plant Capacity	p. 46
2.6.1.2 Material Balances	p. 46
2.6.1.3 Raw Material Limitation and Market Requirement	p. 47
2.6.1.4 Objective Function	p. 47
2.6.2 Model Solution	p. 48
2.6.3 Sensitivity Analysis	p. 49
2.7 General Remarks	p. 52
References	p. 53
3 Multisite Refinery Network Integration and Coordination	p. 55
3.1 Introduction	p. 55
3.2 Literature Review	p. 57
3.3 Problem Statement	p. 60
3.4 Model Formulation	p. 61
3.4.1 Material Balance	p. 62
3.4.2 Product Quality	p. 63
3.4.3 Capacity Limitation and Expansion	p. 64
3.4.4 Product Demand	p. 65
3.4.5 Import Constraint	p. 65
3.4.6 Objective Function	p. 65
3.5 Illustrative Case Study	p. 66
3.5.1 Single Refinery Planning	p. 66
3.5.2 Multisite Refinery Planning	p. 69
3.5.2.1 Scenario-1: Single Feedstock, Multiple Refineries with No Integration	p. 70
3.5.2.2 Scenario-2: Single Feedstock, Multiple Refineries with Integration	p. 71
3.5.2.3 Scenario-3: Multiple Feedstocks, Multiple Refineries with Integration	p. 72
3.5.2.4 Scenario-4: Multiple Feedstocks, Multiple Refineries with Integration and Increased Market Demand	p. 74
3.6 Conclusion	p. 75
References	p. 77
4 Petrochemical Network Planning	p. 82
4.1 Introduction	p. 81
4.2 Literature Review	p. 82
4.3 Model Formulation	p. 83
4.4 Illustrative Case Study	p. 84
4.5 Conclusion	p. 87
References	p. 88
5 Multisite Refinery and Petrochemical Network Integration	p. 91
5.1 Introduction	p. 91
5.2 Problem Statement	p. 93
5.3 Model Formulation	p. 95
5.4 Illustrative Case Study	p. 99
5.5 Conclusion	p. 105
References	p. 106
Part 3 Planning Under Uncertainty	p. 109
6 Planning Under Uncertainty for a Single Refinery Plant	p. 111
6.1 Introduction	p. 111
6.2 Problem Definition	p. 112
6.3 Deterministic Model Formulation	p. 112
6.4 Stochastic Model Formulation	p. 114
6.4.1 Appraoch 1: Risk Model I	p. 114
6.4.1.1 Sampling Methodolgy	p. 115
6.4.1.2 Objective Function Evaluation	p. 115
6.4.1.3 Variance Calculation	p. 116
6.4.2 Approach 2: Expectation Model I and II	p. 117
6.4.2.1 Demand Uncertainty	p. 117
6.4.2.2 Process Yield Uncertainty	p. 118
6.4.3 Approach 3: Risk Model II	p. 119
6.4.4 Approach 4: Risk Model III	p. 120
6.5 Analysis Methodology	p. 121
6.5.1 Model and Solution Robustness	p. 121
6.5.2 Variation Coefficient	p. 122
6.6 Illustrative Case Study	p. 123
6.6.1 Approach 1: Risk Model I	p. 124
6.6.2 Approach 2: Expectation Models I and II	p. 125
6.6.3 Approach 3: Risk Model II	p. 126
6.6.4 Approach 4: Risk Model III	p. 133
6.7 General Remarks	p. 133
References	p. 137
7 Robust Planning of Multisite Refinery Network	p. 139
7.1 Introduction	p. 139
7.2Literature Review p. 140
7.3 Model Formulation	p. 142
7.3.1 Stochastic Model	p. 142
7.3.2 Robust Model	p. 144
7.4 Sample Average Approximation (SAA)	p. 146
7.4.1 SAA Method	p. 146
7.4.2 SAA Procedure	p. 147
7.5 Illustrative Case Study	p. 148
7.5.1 Single Refinery Plarrning	p. 148
7.5.2 Multisite Refinery Planning	p. 153
7.6 Conclusion	p. 159
References	p. 159
8 Robust Planning for Petrochemical Networks	p. 161
8.1 Introduction	p. 161
8.2 Model Formulation	p. 162
8.2.1 Two Stage Stochastic Model	p. 162
8.2.2 Robust Optimization	p. 163
8.3 Value to Information and Stochastic Solution	p. 165
8.4 Illustrative Case Study	p. 166
8.4.1 Solution of Stochastic Model	p. 167
8.4.2 Solution of the Robust Model	p. 168
8.5 Conclusion	p. 170
References	p. 171
9 Stochastic Multisite Refinery and Petrochemical Network Integration	p. 173
9.1 Introduction	p. 173
9.2 Model Formulation	p. 174
9.3 Scenario Generation	p. 177
9.4 Illustrative Case Study	p. 177
9.5 Conclusion	p. 181
References	p. 181
Appendix A Two Stage Stochastic Programming	p. 183
Appendix B Chance Constrained Programming	p. 185
Appendix C SAA Optimal Solution Bounding	p. 187
Index	p. 189

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