Optimization of Sustainable Process Systems

Nyhet

Multiscale Models and Uncertainties

Inbunden, Engelska, 2026

2 349 kr

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Presents a systematic review of optimizing sustainable process systems through multiscale modeling and uncertainty analysis The global pursuit of net-zero carbon emissions has created an urgent need for chemical engineers and energy researchers to design systems that are both sustainable and resilient. While renewable energy sources such as solar and wind offer great potential, their variability introduces significant challenges that must be addressed through advanced optimization techniques. Optimization of Sustainable Process Systems: Multiscale Models and Uncertainties connects optimization fundamentals with their applications in sustainable energy systems with a particular emphasis on the challenges posed by uncertainty. Divided into two parts, the book first introduces the core mathematical frameworks and methods needed to model and optimize uncertain systems, including stochastic programming, robust optimization, reinforcement learning, and multiscale algorithms. The authors clearly explain these state-of-the-art tools with attention to both theory and computational practice. The second part shifts to applications, demonstrating how these techniques are applied in real-world contexts such as renewable-based hydrogen, methanol, and ammonia production; carbon capture; shale gas systems; biomass integration; and power system optimization. Throughout the text, the authors emphasize the integration of renewables with chemical industries while highlighting strategies to manage variability, strengthen supply chains, and improve system-wide efficiency. Combining rigorous fundamentals with cutting-edge applications through a tutorial-style approach, Optimization of Sustainable Process Systems: Multiscale Models and Uncertainties: Provides the foundation and tools needed to design resilient, optimized, and sustainable energy systemsAddresses optimization methods under uncertainty tailored to energy and process systemsPresents a unified treatment of stochastic programming, robust optimization, and reinforcement learning techniquesIntegrates renewable-based systems with chemical industry supply chain design and operationAddresses computational challenges in large-scale optimization of energy systemsBoth a theoretical resource and a practical guide for applied problem-solving, Optimization of Sustainable Process Systems: Multiscale Models and Uncertainties is ideal for graduate-level courses in chemical engineering, process systems engineering, energy systems optimization, and operations research. It is also a valuable reference for industrial researchers, system modelers, and developers working on sustainable process design and energy transition strategies.

Produktinformation

Utgivningsdatum2026-04-13
Mått152 x 229 x 22 mm
Vikt705 g
FormatInbunden
SpråkEngelska
Antal sidor400
Upplaga26001
FörlagJohn Wiley & Sons Inc
ISBN9781394205578

Tillhör följande kategorier

Energiindustri inom Ekonomi och Ledarskap
Energiteknik inom Naturvetenskap och teknik

List of Contributors xiiiPreface xvii1 An Introduction to Bilevel Optimization and Its Application to Sustainable Systems Engineering 1Rishabh Gupta, Jnana S. Jagana, Tushar Rathi, and Qi Zhang1.1 Introduction 11.2 Fundamentals of Bilevel Optimization 21.2.1 Mathematical Formulation 21.2.1.1 Optimistic Versus Pessimistic Bilevel Optimization 31.2.1.2 High-Point Relaxation 41.2.1.3 When to Not Use Bilevel Optimization 41.2.2 KKT Reformulation 61.2.2.1 “Naive” KKT Reformulation 61.2.2.2 Mixed-Integer Programming Reformulation 61.2.2.3 Branching on Complementarity Constraints 71.2.2.4 Penalty-Based Reformulation 81.2.3 Value-Function Reformulation 81.2.3.1 Reformulation Using the Optimal-Value Function 91.2.3.2 Kth-Best Algorithm 91.2.3.3 Cutting-Plane Approach 111.3 Some Applications in Sustainable Systems Engineering 121.4 Bilevel Optimization for Machine Learning 141.4.1 Data-Driven Inverse Optimization 141.4.2 Hyperparameter Tuning 171.4.3 Algorithms for Large-Scale Bilevel Optimization 201.4.3.1 Implicit Estimation 211.4.3.2 Explicit Estimation 221.5 Robust Optimization 251.5.1 Mathematical Formulation 261.5.1.1 Reformulation 271.5.1.2 Cutting-Plane Approach 281.5.2 Adjustable Robust Optimization 281.5.3 Applications 291.5.4 Case Study 301.6 Conclusions 33References 342 Exploiting the Multiscale Structure of Sustainable Engineering Problems via Network-Based Decomposition 43Ilias Mitrai and Prodromos Daoutidis2.1 Introduction 432.2 Learning the Structure of Optimization Problems 452.2.1 Optimization Problems as Graphs 452.2.2 Learning the Structure via Stochastic Blockmodeling 462.3 Network-Based Decomposition of Optimization Problems 482.3.1 Benders Decomposition Based on the Variable Graph 482.3.2 Lagrangean Decomposition Based on the Structure of the Constraint Graph 502.4 Case Study: Transition to Green Ammonia Supply Chain Networks 522.4.1 Two-Stage Stochastic Programming Problem Formulation 522.4.2 Structure of the Optimization Problem 532.4.3 Numerical Results 572.5 Conclusions 57References 583 Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs 63Akshay Kudva, Wei-Ting Tang, and Joel A. Paulson3.1 Introduction 633.2 Problem Formulation 663.3 Multi-Objective Bayesian Optimization Over Network Systems 683.3.1 Statistical Surrogate Model 693.3.2 Multi-Objective Thompson Sampling for Function Networks 703.3.3 Practical Considerations in MOBONS 713.3.3.1 GP Kernel Selection and Tuning 713.3.3.2 Thompson Sampling 733.3.3.3 Approximating the Pareto Optimal Set 733.3.3.4 Selection Function 743.3.4 Handling Parallel Evaluations and Constrained Problems 743.4 Case Studies 753.4.1 Baseline Methods for Comparison 753.4.2 Synthetic Test Problem: ZDT4 Benchmark 763.4.3 Design of Sustainable Bioethanol Process 793.4.3.1 Process Description and Implementation 793.4.3.2 Problem Formulation and Function Network Representation 793.4.3.3 Optimization Performance and Hypervolume Analysis 813.4.3.4 Local Sensitivity Analysis 823.5 Conclusion 84References 854 A Tutorial on Multi-time Scale Optimization Models and Algorithms 91Asha Ramanujam and Can li4.1 Introduction 914.2 Multi-time Scale Optimization Models 924.3 Value of the Multi-scale Model (VMM) 944.4 Algorithms to Solve Multi-time Scale Optimization Models 964.4.1 Full-Space Methods 964.4.2 Decomposition Algorithms 974.4.2.1 Bi-level Decomposition 984.4.2.2 Dual-Based Decomposition Algorithms 1004.4.2.3 Limitations of Decomposition Algorithms 1134.4.3 Metaheuristic Algorithms 1134.4.4 Matheuristic Algorithms 1144.4.5 Data-Driven Methods 1154.4.6 Pamso 1174.5 Illustrative Example 1194.5.1 Problem Statement 1194.5.2 Integrated Model 1194.5.2.1 Indices and Sets 1194.5.2.2 Variables 1194.5.2.3 Parameters 1204.5.2.4 Constraints 1204.5.2.5 Objective 1204.5.2.6 Optimization Model 1204.5.3 Solving the Problem 1214.5.3.1 Using Full-Space Method 1214.5.3.2 Using Benders Decomposition 1214.5.3.3 Using Lagrangian Decomposition 1224.5.3.4 Using Dantzig–Wolfe Decomposition 1244.5.3.5 Using PAMSO 1264.5.4 Vmm 1284.6 Conclusion 129References 1295 Many Objective Optimization Tools for Sustainable Decision-Making 135Andrew Allman and Hongxuan Wang5.1 Introduction 1355.2 Sustainability Objectives 1365.3 MOP Solution Methods 1385.4 Objective Dimensionality Reduction for MaOPs 1415.5 Case Study: Cost Versus Emissions-Driven Demand Response 1445.6 Case Study: Analysis of Planetary Boundary Objectives 1475.7 Conclusion and Future Perspectives 150References 1516 Optimization Models and Algorithms for Design and Planning of Sustainable Processes and Energy Systems 155Seolhee Cho and Ignacio E. Grossmann6.1 Introduction 1556.2 Optimization Models 1566.2.1 Continuous Optimization 1576.2.2 Discrete Optimization 1576.2.3 Logic-Based Optimization 1586.2.4 Optimization Under Uncertainty 1596.3 Solution Strategies 1606.3.1 Benders Decomposition 1606.3.2 Lagrangean Decomposition 1616.3.3 Bilevel Decomposition 1626.4 Algebraic Modeling Languages 1626.5 Applications in Sustainable Process and Energy Systems 1646.5.1 Hydrogen 1646.5.2 Biomass 1656.5.3 Methanol 1666.5.4 Power Systems 1676.6 Conclusions 169References 1697 Multiscale Modeling and Optimization of Carbon Capture Processes 179Kyeongjun Seo, Mark A. Stadtherr, and Michael Baldea7.1 Introduction 1797.2 Modeling of Carbon Capture Processes 1807.2.1 Process Structure and Operation 1807.2.2 Mathematical Modeling 1837.2.3 Multiscale Optimization 1857.3 Multiscale Modeling and Optimization Results 1877.4 Conclusions 193Acknowledgments 194Disclaimer 194References 1958 Integrated Design and Operability Optimization of Sustainable Process Intensification Systems 199Yuhe Tian, Rahul Bindlish, and Efstratios N. Pistikopoulos8.1 Introduction 1998.2 Methodology Framework 2028.2.1 Prelude: Phenomena-Based Process Synthesis 2028.2.2 Generalized Modular Representation Framework 2038.2.3 Integrated Synthesis and Operability Optimization 2058.2.3.1 Safety Considerations via Risk Analysis 2058.2.3.2 Design Under Uncertainty via Flexibility Analysis 2088.3 Case Studies 2108.3.1 MMA Purification 2108.3.1.1 Process Description 2108.3.1.2 GMF Simulation of Base Case Design 2118.3.1.3 GMF Synthesis for Grassroots Design 2128.3.2 MTBE Production 2148.3.2.1 Process Description 2148.3.2.2 Integrated GMF Synthesis and Operability Optimization 2158.4 Concluding Remarks 218Acknowledgment 218References 2189 Circular Economy Assessment Tools for Process Systems 223Paola Munoz-Briones, Kenneth Martinez, Javiera Vergara-Zambrano, and Styliani Avraamidou9.1 Introduction 2239.2 Circular Economy Assessment in the Food Sector 2269.2.1 Example: CE Assessment for Food Packaging Waste Management Technologies 2309.3 Circular Economy Assessment in the Chemical Industry 2329.3.1 Example: Circular Economy Assessment of Viable for Fuels for Mobility 2359.4 Circular Economy Metrics for Energy Systems 237References 24010 Decarbonization of Steam Cracking for Clean Olefins Production: Optimal Microgrid Scheduling 251Saba Ghasemi Naraghi, Tylee Kareck, Lingyun Xiao, Richard Reed, Paritosh Ramanan, and Zheyu Jiang10.1 Introduction 25110.2 Dynamic Optimization of Steam Cracking Process 25410.3 Scenario-Based Optimal Microgrid Scheduling Problem 25810.4 Illustrative Case Studies 26510.4.1 Problem Setting 26510.4.2 Grid-Connected Mode 26710.4.3 Islanded Mode 27210.5 Conclusion 275Acknowledgments 275References 27611 Multiscale Strategies for the Use of Chemicals as Energy Storage Systems 279Diego Santamaría, Antonio Sánchez, and Mariano Martín11.1 Introduction 27911.2 Methodology 27911.2.1 Process Design 28011.2.2 Process Scale Up/Down 28211.2.3 Enterprise-Wide Level 28311.3 Cases of Study 28511.3.1 Hydrogen 28511.3.2 Methane 29111.3.3 Methanol 29511.3.4 Ammonia 29811.4 Conclusions 304Acknowledgment 304References 30412 Repurposing a Conventional Oil Refinery for Biomass Processing to Aviation Fuel: Process Design and Techno-Environmental Evaluation for a Real Operating Plant 317Valeria González, Alejandro Pedezert, Soledad Gutiérrez, Roberto Kreimerman, Lucia Pittaluga, and Ana I. Torres12.1 Introduction 31712.2 Overview of Feed Options, Processing Pathways and Current Infrastructure 31912.3 Sustainable Aviation Fuel Process Design 32112.3.1 Base Process Overview 32212.3.2 Modeling 32312.3.2.1 Feedstock 32312.3.2.2 Hydrotreating Reactor (R-100) 32312.3.2.3 Area 200: Separation 32912.3.2.4 Area 300: Hydrocracking and Hydroisomerization 33112.3.2.5 Area 400: Products Separation 33112.3.3 Simulation Results 33112.4 Sustainable Aviation Fuel Process Design: Adjustments in Design to Match Current Operations in the Refinery 33312.4.1 Simulation Results 33712.5 Environmental Assessment Using GREENSCOPE 33712.5.1 Overview of Selected Indicators 33812.5.1.1 Dangerous Materials 33812.5.1.2 Chemical Exposure Index (CEI) 33912.5.1.3 Health Hazards in the Workplace 33912.5.1.4 Safety Hazards 33912.5.1.5 Substance Toxicity 34012.5.1.6 Enviromental Hazard 34012.5.1.7 Indicators from Potency Factors: Global Warming, Smog, Acidification, Ozone Depletion, and Cancer 34112.5.1.8 Liquid Emissions 34112.5.2 Results 34112.6 Summary and Final Remarks 343Acknowledgments 344References 34413 Uncertainty Quantification of Solid Sorbent-Based CO 2 Capture Processes 349Ana Flávia Monteiro and Debangsu Bhattacharyya13.1 Introduction 34913.2 Methodology 35213.2.1 UQ of Model Parameters 35213.2.2 UQ of Model Form Discrepancy 35413.3 Example-UQ of a Solid-Based CO 2 Capture System in a Fixed Bed 35613.3.1 UQ of the Isotherm Model 35613.3.2 Uncertainty Propagation 35913.3.2.1 Lab-Scale Axial Flow-Fixed Bed 36013.3.2.2 Commercial-Scale Radial Flow-Fixed Bed 36113.4 Concluding Remarks 363References 366Index 371