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100 1 _aKhor, Cheng Seong,
_eauthor.
245 1 0 _aModel-based optimization for petroleum refinery configuration design /
_cCheng Seong Khor.
264 1 _aWeinheim, Germany :
_bWiley-VCH,
_c2023.
300 _a1 online resource (256 pages)
336 _atext
_btxt
_2rdacontent.
337 _acomputer
_bc
_2rdamedia.
338 _aonline resource
_bcr
_2rdacarrier.
340 _2rdacc
_0http://rdaregistry.info/termList/RDAColourContent/1003.
505 0 _81.1\x
_a1 Introduction to Optimization Modeling for Petroleum Refineries -- 1.1 Background -- 1.2 Overview of Refining Processes -- 1.2.1 Atmospheric Crude Oil Distillation -- 1.2.2 Hydroprocessing -- 1.2.3 Sulfur Recovery -- 1.2.4 Reforming -- 1.2.5 Isomerization -- 1.2.6 Blending -- 1.3 Overview of Refinery Optimization Modeling -- 1.3.1 Refinery Optimization Systems, Techniques, and Tools -- 1.3.2 Modeling for Advanced Process Control -- 1.3.3 Modeling for Real-Time Optimization -- 1.3.4 Modeling for Process Simulation -- 1.3.4.1 Modeling for Dynamic Simulation -- 1.3.4.2 Modeling for Operator Training Simulation -- 1.3.5 Modeling for Planning and Scheduling -- 1.3.5.1 Systems Implementation -- 1.3.5.2 Optimization of Crude Oil Scheduling -- 1.3.5.3 Refinery Management -- 1.4 Concluding Remarks -- References -- 2 Basic Petroleum Refinery Economics -- 2.1 Refinery Economics Overview -- 2.1.1 Refinery Profitability -- 2.1.2 Refinery Margins -- 2.1.3 Refinery Margin Calculations -- 2.1.4 Refinery Margin Trends -- 2.1.5 Refinery Margin Improvement -- 2.2 Marginal Economics for Incremental Optimization -- 2.3 Refinery Economic Analysis -- 2.3.1 Refinery Value Determination -- 2.3.2 Refinery Economic Evaluation -- 2.3.2.1 Simple Example -- 2.3.2.2 Advanced Example -- 2.3.2.3 Further Example -- 2.3.3 Refinery Contracts -- 2.4 Concluding Remarks -- References -- 3 Superstructure Representation -- 3.1 Introduction to Superstructures -- 3.2 Types of Superstructure Representation -- 3.3 State-Task Network Superstructure Representation -- 3.4 State-Equipment Network Superstructure Representation -- 3.5 Resource-Task Network Superstructure Representation -- 3.6 Superstructure Generation -- 3.7 Other Superstructure Representations -- 3.7.1 State-Space Network Superstructure Representation -- 3.7.2 Unit Operation-Port-State Superstructure Representation -- 3.7.3 Bond Graph Superstructure Representation -- 3.8 Superstructure Representation Example for Naphtha Processing -- 3.9 Chapter Summary -- References -- 4 Modeling Framework -- 4.1 Modeling of Mixed Continuous and Integer Decision Variables -- 4.2 Superstructure Optimization Modeling -- 4.3 Constructing Superstructures -- 4.4 Modeling of Superstructure Representations -- 4.5 Modeling of Discrete Decisions and Logical Relations -- 4.5.1 Propositional Logics for Superstructure Optimization Modeling -- 4.5.2 Logical Binary Variables -- 4.5.3 Yes/No Type Binary Variables -- 4.5.4 Disjunctive Optimization Modeling -- 4.6 Modeling of Process Units and Operations -- 4.6.1 Process Design Procedure -- 4.6.2 Selecting Modeling Variables -- 4.6.3 Formulating Simple Models -- 4.6.4 Basic Unit Models -- 4.6.4.1 Mixer -- 4.6.4.2 Splitter -- 4.6.4.3 Separator -- 4.6.4.4 Valve -- 4.6.4.5 Multicomponent Splitter -- 4.6.5 Unit Operation Models -- 4.6.5.1 Compressor -- 4.6.5.2 Furnace -- 4.6.5.3 Conversion Reactor -- 4.6.5.4 Heat Exchanger -- 4.6.6 Information Flow Modeling -- 4.6.6.1 Information Flow Diagram -- 4.6.6.2 Choice of Design Variables -- 4.6.6.3 Equation Ordering -- 4.7 Modeling for Numerical Studies -- 4.8 Chapter Summary -- References -- 5 Model Formulation and Implementation -- 5.1 Mathematical Formulation -- 5.2 Generic Optimization Model Formulation for Refinery Planning -- 5.2.1 Objective Function -- 5.2.2 Production Capacity and Expansion Constraints -- 5.2.3 Mass Balances -- 5.2.4 Demand Constraints -- 5.2.5 Availability Constraints -- 5.2.6 Non-Negativity Constraints -- 5.3 Generic Optimization Model Formulation for Refinery Design -- 5.3.1 Material Balances -- 5.3.2 Mixed-Integer Logical Constraints -- 5.3.3 Logical Constraints on Design and Structural Specifications -- 5.3.4 Logic Propositional Constraints on Design Specifications -- 5.3.4.1 Example 1 -- 5.3.4.2 Example 2 -- 5.3.5 Logic Propositional Constraints on Structural Specifications -- 5.3.6 Generalized Disjunctive Programming -- 5.4 Numerical Implementation for Computational Experiments -- 5.5 Computational Experiment Examples -- 5.5.1 MILP Model Results -- 5.5.2 GDP Model Results -- 5.6 Chapter Summary -- References -- 6 Solution Strategies -- 6.1 Convex Relaxation -- 6.2 Lagrangean Decomposition -- 6.3 Global Optimization Techniques -- 6.3.1 Branch and Reduce -- 6.3.2 Spatial Branch and Bound -- 6.3.3 Hybrid Branch and Bound -- 6.3.4 Interval Analysis -- 6.3.5 Extended Cutting Plane -- 6.4 Advancements in Commercial Integer Optimization Solvers -- 6.4.1 Overview -- 6.4.2 Computational Performance of Commercial Integer Optimization Solvers -- 6.4.3 A Commercial Success Story: CPLEX Integer Optimization Solver -- 6.4.4 Solution Methods and Algorithms -- 6.4.4.1 Integer Optimization Algorithms -- 6.4.4.2 Branch and Bound -- 6.4.4.3 Presolve and Cutting Planes -- 6.4.4.4 Heuristics -- 6.4.4.5 Combined Local Search and Heuristics -- 6.4.4.6 Parallelization -- 6.4.4.7 Solution Pools -- 6.4.4.8 Tuning Tools -- 6.4.5 Application Examples -- 6.4.5.1 Example 1: Energy Optimization -- 6.4.5.2 Example 2: Financial Optimization -- 6.4.5.3 Example 3: Manufacturing Optimization -- 6.4.5.4 Concluding Remarks -- 6.5 Chapter Summary -- References -- 7 Industrial Case Studies with Business-Centric Techno-Commercial Considerations -- 7.1 Industrial Case Study 1: Refinery Configuration for Heavy Oil Processing -- 7.1.1 Background -- 7.1.2 Problem Statement -- 7.1.3 Model Formulation -- 7.1.4 Numerical Example -- 7.1.5 Concluding Remarks -- 7.2 Industrial Case Study 2: Refinery Configuration for Whole Complex Processing -- 7.2.1 Model Formulation -- 7.2.1.1 Superstructure Representation -- 7.2.1.2 Logic Propositions -- 7.2.1.3 Objective Function -- 7.2.2 Computational Results -- 7.2.2.1 Computational Results and Discussion -- 7.2.2.2 Model Validation -- 7.2.2.3 Application Extension to Refinery Upgrade Studies -- 7.2.2.4 Sensitivity Analysis -- 7.2.3 Concluding Remarks -- 7.3 Industrial Case Study 3: Refinery Configuration for Naphtha Upgrading -- 7.3.1 Problem Statement -- 7.3.2 Propositional Logics and Logic Cuts in Process Synthesis Problems -- 7.3.3 Logical Constraints -- 7.3.3.1 General Formulation -- 7.3.3.2 Logical Constraints on Processing Alternatives of Naphtha for Petroleum Refineries -- 7.3.4 Computational Experience -- 7.3.5 Concluding Remarks -- 7.4 Chapter Summary -- References -- 8 Industrial Case Studies with Environmental-Centric Techno-Commercial Considerations -- 8.1 Industrial Case Study 1: Refinery Configuration with Environmental Considerations -- 8.1.1 Background -- 8.1.2 Problem Statement -- 8.1.3 Model Formulation -- 8.1.3.1 Superstructure Representation -- 8.1.3.2 Material Balance Constraints -- 8.1.3.3 Logical Constraints -- 8.1.3.4 Logic Propositions -- 8.1.3.5 Environmental Performance Assessment for Risk Evaluation of Flowsheets -- 8.1.3.6 Objective Function -- 8.1.4 Numerical Example -- 8.1.5 Concluding Remarks -- 8.2 Industrial Case Study 2: Refinery Configuration with Heat Integration -- 8.2.1 Problem Statement -- 8.2.2 Superstructure Representation -- 8.2.3 Modeling and Computational Strategy -- 8.2.4 Model Formulation -- 8.2.4.1 Flowsheet Optimization -- 8.2.4.2 Heat Integration Constraints -- 8.2.4.3 Objective Function -- 8.2.5 Computational Results -- 8.2.6 Concluding Remarks -- 8.3 Chapter Summary -- References -- 9 Industrial Case Studies with Engineering-Centric Techno-Commercial Considerations -- 9.1 Industrial Case Study 1: Refinery Configuration for High-Octane Fuel Production -- 9.1.1 Catalytic Reforming Process -- 9.1.2 Data Reconciliation Method -- 9.1.3 Problem Statement -- 9.1.4 Model Formulation -- 9.1.4.1 Data Reconciliation Model -- 9.1.4.2 Feed Characterization -- 9.1.4.3 Reactor Representation -- 9.1.4.4 Reactor Pressure Balance -- 9.1.4.5 Reaction Kinetic Tuning -- 9.1.4.6 Reactor Switch in Cyclic Reformer -- 9.1.4.7 Measurement Models -- 9.1.5 Results and Discussion -- 9.1.5.1 Key Process Variables -- 9.1.5.2 Tuning Strategies -- 9.1.5.3 Reformate Yields -- 9.1.5.4 Reactor Total Endotherms -- 9.1.6 Concluding Remarks -- 9.2 Industrial Case Study 2: Refinery Configuration for Low-Benzene Fuel Production -- 9.2.1 Problem Statement -- 9.2.2 Superstructure Representation -- 9.2.3 Model Formulation -- 9.2.4 Preliminary Computational Results -- 9.3 Chapter Summary -- References -- Summary and Conclusions.
520 _aModel-Based Optimization for Petroleum Refinery Configuration Design An accessible, easy-to-read introduction to the methods of mixed-integer optimization, with practical applications, real-world operational data, and case studies Interest in model-based approaches for optimizing the design of petroleum refineries has increased throughout the industry in recent years. Mathematical optimization based on mixed-integer programming has brought about the superstructure optimization method for synthesizing petroleum refinery configurations from multiple topological alternatives. Model-Based Optimization for Petroleum Refinery Configuration Design presents a detailed introduction to the use of mathematical optimization to solve both linear and nonlinear problems in the refining industry. The book opens with an overview of petroleum refining processes, basic concepts in mathematical programming, and applications of mathematical programming for refinery optimization. Subsequent chapters address superstructure representations of topological alternatives, mathematical formulation, solution strategies, and various modeling frameworks. Practical case studies demonstrate refinery configuration design, refinery retrofitting, and real-world issues and considerations. Presents linear, nonlinear, and mixed-integer programming approaches applicable to both new and existing petroleum refineries Highlights the benefits of model-based solutions to refinery configuration design problems Features detailed case studies of the development and implementation of optimization models Discusses economic considerations of heavy oil processing, including cash flow analysis of refinery costs and return on capital Includes numerical examples based on real-world operational data and various commercial technologies Model-Based Optimization for Petroleum Refinery Configuration Design is an invaluable resource for researchers, chemical engineers, process and energy engineers, other refining professionals, and advanced chemical engineering students.
545 0 _aAbout the Author Cheng Seong Khor, PhD, is formerly a senior lecturer at Chemical Engineering Department, Universiti Teknologi PETRONAS (UTP), Malaysia and now a data scientist at PETRONAS.
650 0 _aPetroleum refineries
_0https://id.loc.gov/authorities/subjects/sh85100456
_xDesign and construction.
_0https://id.loc.gov/authorities/subjects/sh2002006372.
650 0 _aMathematical optimization.
_0https://id.loc.gov/authorities/subjects/sh85082127.
655 4 _aElectronic books.
856 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9783527826100
_yFull text is available at Wiley Online Library Click here to view
942 _2ddc
_cER