{"product_id":"optimization-of-sustainable-process-systems-multiscale-models-and-uncertainties-hardback-9781394205578","title":"Optimization of Sustainable Process Systems; Multiscale Models and Uncertainties (Hardback) 9781394205578","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimization of Sustainable Process Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eMultiscale Models and Uncertainties\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eCan Li (Edited by), Can Li (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394205578, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 April 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e400 pages\u003cbr\u003e22.9 x 16 x 2.5 cm, 0.635 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePresents a systematic review of optimizing sustainable process systems through multiscale modeling and uncertainty analysis\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003ci\u003eOptimization of Sustainable Process Systems: Multiscale Models and Uncertainties\u003c\/i\u003e connects optimization fundamentals with their applications in sustainable energy systems with a particular emphasis on the challenges posed by uncertainty. \u003c\/p\u003e\n\u003cp\u003eDivided 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.  \u003c\/p\u003e\n\u003cp\u003eCombining rigorous fundamentals with cutting-edge applications through a tutorial-style approach, \u003ci\u003eOptimization of Sustainable Process Systems: Multiscale Models and Uncertainties:\u003c\/i\u003e \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e Provides the foundation and tools needed to design resilient, optimized, and sustainable energy systems\u003c\/li\u003e\n\u003cli\u003e Addresses optimization methods under uncertainty tailored to energy and process systems\u003c\/li\u003e\n\u003cli\u003e Presents a unified treatment of stochastic programming, robust optimization, and reinforcement learning techniques\u003c\/li\u003e\n\u003cli\u003e Integrates renewable-based systems with chemical industry supply chain design and operation\u003c\/li\u003e\n\u003cli\u003e Addresses computational challenges in large-scale optimization of energy systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eBoth a theoretical resource and a practical guide for applied problem-solving, \u003ci\u003eOptimization of Sustainable Process Systems: Multiscale Models and Uncertainties\u003c\/i\u003e 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Contributors xiii\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An Introduction to Bilevel Optimization and Its Application to Sustainable Systems Engineering 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRishabh Gupta, Jnana S. Jagana, Tushar Rathi, and Qi Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Fundamentals of Bilevel Optimization 2\u003c\/p\u003e \u003cp\u003e1.2.1 Mathematical Formulation 2\u003c\/p\u003e \u003cp\u003e1.2.1.1 Optimistic Versus Pessimistic Bilevel Optimization 3\u003c\/p\u003e \u003cp\u003e1.2.1.2 High-Point Relaxation 4\u003c\/p\u003e \u003cp\u003e1.2.1.3 When to Not Use Bilevel Optimization 4\u003c\/p\u003e \u003cp\u003e1.2.2 KKT Reformulation 6\u003c\/p\u003e \u003cp\u003e1.2.2.1 “Naive” KKT Reformulation 6\u003c\/p\u003e \u003cp\u003e1.2.2.2 Mixed-Integer Programming Reformulation 6\u003c\/p\u003e \u003cp\u003e1.2.2.3 Branching on Complementarity Constraints 7\u003c\/p\u003e \u003cp\u003e1.2.2.4 Penalty-Based Reformulation 8\u003c\/p\u003e \u003cp\u003e1.2.3 Value-Function Reformulation 8\u003c\/p\u003e \u003cp\u003e1.2.3.1 Reformulation Using the Optimal-Value Function 9\u003c\/p\u003e \u003cp\u003e1.2.3.2 Kth-Best Algorithm 9\u003c\/p\u003e \u003cp\u003e1.2.3.3 Cutting-Plane Approach 11\u003c\/p\u003e \u003cp\u003e1.3 Some Applications in Sustainable Systems Engineering 12\u003c\/p\u003e \u003cp\u003e1.4 Bilevel Optimization for Machine Learning 14\u003c\/p\u003e \u003cp\u003e1.4.1 Data-Driven Inverse Optimization 14\u003c\/p\u003e \u003cp\u003e1.4.2 Hyperparameter Tuning 17\u003c\/p\u003e \u003cp\u003e1.4.3 Algorithms for Large-Scale Bilevel Optimization 20\u003c\/p\u003e \u003cp\u003e1.4.3.1 Implicit Estimation 21\u003c\/p\u003e \u003cp\u003e1.4.3.2 Explicit Estimation 22\u003c\/p\u003e \u003cp\u003e1.5 Robust Optimization 25\u003c\/p\u003e \u003cp\u003e1.5.1 Mathematical Formulation 26\u003c\/p\u003e \u003cp\u003e1.5.1.1 Reformulation 27\u003c\/p\u003e \u003cp\u003e1.5.1.2 Cutting-Plane Approach 28\u003c\/p\u003e \u003cp\u003e1.5.2 Adjustable Robust Optimization 28\u003c\/p\u003e \u003cp\u003e1.5.3 Applications 29\u003c\/p\u003e \u003cp\u003e1.5.4 Case Study 30\u003c\/p\u003e \u003cp\u003e1.6 Conclusions 33\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Exploiting the Multiscale Structure of Sustainable Engineering Problems via Network-Based Decomposition 43\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIlias Mitrai and Prodromos Daoutidis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 43\u003c\/p\u003e \u003cp\u003e2.2 Learning the Structure of Optimization Problems 45\u003c\/p\u003e \u003cp\u003e2.2.1 Optimization Problems as Graphs 45\u003c\/p\u003e \u003cp\u003e2.2.2 Learning the Structure via Stochastic Blockmodeling 46\u003c\/p\u003e \u003cp\u003e2.3 Network-Based Decomposition of Optimization Problems 48\u003c\/p\u003e \u003cp\u003e2.3.1 Benders Decomposition Based on the Variable Graph 48\u003c\/p\u003e \u003cp\u003e2.3.2 Lagrangean Decomposition Based on the Structure of the Constraint Graph 50\u003c\/p\u003e \u003cp\u003e2.4 Case Study: Transition to Green Ammonia Supply Chain Networks 52\u003c\/p\u003e \u003cp\u003e2.4.1 Two-Stage Stochastic Programming Problem Formulation 52\u003c\/p\u003e \u003cp\u003e2.4.2 Structure of the Optimization Problem 53\u003c\/p\u003e \u003cp\u003e2.4.3 Numerical Results 57\u003c\/p\u003e \u003cp\u003e2.5 Conclusions 57\u003c\/p\u003e \u003cp\u003eReferences 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs 63\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAkshay Kudva, Wei-Ting Tang, and Joel A. Paulson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 63\u003c\/p\u003e \u003cp\u003e3.2 Problem Formulation 66\u003c\/p\u003e \u003cp\u003e3.3 Multi-Objective Bayesian Optimization Over Network Systems 68\u003c\/p\u003e \u003cp\u003e3.3.1 Statistical Surrogate Model 69\u003c\/p\u003e \u003cp\u003e3.3.2 Multi-Objective Thompson Sampling for Function Networks 70\u003c\/p\u003e \u003cp\u003e3.3.3 Practical Considerations in MOBONS 71\u003c\/p\u003e \u003cp\u003e3.3.3.1 GP Kernel Selection and Tuning 71\u003c\/p\u003e \u003cp\u003e3.3.3.2 Thompson Sampling 73\u003c\/p\u003e \u003cp\u003e3.3.3.3 Approximating the Pareto Optimal Set 73\u003c\/p\u003e \u003cp\u003e3.3.3.4 Selection Function 74\u003c\/p\u003e \u003cp\u003e3.3.4 Handling Parallel Evaluations and Constrained Problems 74\u003c\/p\u003e \u003cp\u003e3.4 Case Studies 75\u003c\/p\u003e \u003cp\u003e3.4.1 Baseline Methods for Comparison 75\u003c\/p\u003e \u003cp\u003e3.4.2 Synthetic Test Problem: ZDT4 Benchmark 76\u003c\/p\u003e \u003cp\u003e3.4.3 Design of Sustainable Bioethanol Process 79\u003c\/p\u003e \u003cp\u003e3.4.3.1 Process Description and Implementation 79\u003c\/p\u003e \u003cp\u003e3.4.3.2 Problem Formulation and Function Network Representation 79\u003c\/p\u003e \u003cp\u003e3.4.3.3 Optimization Performance and Hypervolume Analysis 81\u003c\/p\u003e \u003cp\u003e3.4.3.4 Local Sensitivity Analysis 82\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 84\u003c\/p\u003e \u003cp\u003eReferences 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A Tutorial on Multi-time Scale Optimization Models and Algorithms 91\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAsha Ramanujam and Can li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 91\u003c\/p\u003e \u003cp\u003e4.2 Multi-time Scale Optimization Models 92\u003c\/p\u003e \u003cp\u003e4.3 Value of the Multi-scale Model (VMM) 94\u003c\/p\u003e \u003cp\u003e4.4 Algorithms to Solve Multi-time Scale Optimization Models 96\u003c\/p\u003e \u003cp\u003e4.4.1 Full-Space Methods 96\u003c\/p\u003e \u003cp\u003e4.4.2 Decomposition Algorithms 97\u003c\/p\u003e \u003cp\u003e4.4.2.1 Bi-level Decomposition 98\u003c\/p\u003e \u003cp\u003e4.4.2.2 Dual-Based Decomposition Algorithms 100\u003c\/p\u003e \u003cp\u003e4.4.2.3 Limitations of Decomposition Algorithms 113\u003c\/p\u003e \u003cp\u003e4.4.3 Metaheuristic Algorithms 113\u003c\/p\u003e \u003cp\u003e4.4.4 Matheuristic Algorithms 114\u003c\/p\u003e \u003cp\u003e4.4.5 Data-Driven Methods 115\u003c\/p\u003e \u003cp\u003e4.4.6 Pamso 117\u003c\/p\u003e \u003cp\u003e4.5 Illustrative Example 119\u003c\/p\u003e \u003cp\u003e4.5.1 Problem Statement 119\u003c\/p\u003e \u003cp\u003e4.5.2 Integrated Model 119\u003c\/p\u003e \u003cp\u003e4.5.2.1 Indices and Sets 119\u003c\/p\u003e \u003cp\u003e4.5.2.2 Variables 119\u003c\/p\u003e \u003cp\u003e4.5.2.3 Parameters 120\u003c\/p\u003e \u003cp\u003e4.5.2.4 Constraints 120\u003c\/p\u003e \u003cp\u003e4.5.2.5 Objective 120\u003c\/p\u003e \u003cp\u003e4.5.2.6 Optimization Model 120\u003c\/p\u003e \u003cp\u003e4.5.3 Solving the Problem 121\u003c\/p\u003e \u003cp\u003e4.5.3.1 Using Full-Space Method 121\u003c\/p\u003e \u003cp\u003e4.5.3.2 Using Benders Decomposition 121\u003c\/p\u003e \u003cp\u003e4.5.3.3 Using Lagrangian Decomposition 122\u003c\/p\u003e \u003cp\u003e4.5.3.4 Using Dantzig–Wolfe Decomposition 124\u003c\/p\u003e \u003cp\u003e4.5.3.5 Using PAMSO 126\u003c\/p\u003e \u003cp\u003e4.5.4 Vmm 128\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 129\u003c\/p\u003e \u003cp\u003eReferences 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Many Objective Optimization Tools for Sustainable Decision-Making 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAndrew Allman and Hongxuan Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 135\u003c\/p\u003e \u003cp\u003e5.2 Sustainability Objectives 136\u003c\/p\u003e \u003cp\u003e5.3 MOP Solution Methods 138\u003c\/p\u003e \u003cp\u003e5.4 Objective Dimensionality Reduction for MaOPs 141\u003c\/p\u003e \u003cp\u003e5.5 Case Study: Cost Versus Emissions-Driven Demand Response 144\u003c\/p\u003e \u003cp\u003e5.6 Case Study: Analysis of Planetary Boundary Objectives 147\u003c\/p\u003e \u003cp\u003e5.7 Conclusion and Future Perspectives 150\u003c\/p\u003e \u003cp\u003eReferences 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optimization Models and Algorithms for Design and Planning of Sustainable Processes and Energy Systems 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSeolhee Cho and Ignacio E. Grossmann\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 155\u003c\/p\u003e \u003cp\u003e6.2 Optimization Models 156\u003c\/p\u003e \u003cp\u003e6.2.1 Continuous Optimization 157\u003c\/p\u003e \u003cp\u003e6.2.2 Discrete Optimization 157\u003c\/p\u003e \u003cp\u003e6.2.3 Logic-Based Optimization 158\u003c\/p\u003e \u003cp\u003e6.2.4 Optimization Under Uncertainty 159\u003c\/p\u003e \u003cp\u003e6.3 Solution Strategies 160\u003c\/p\u003e \u003cp\u003e6.3.1 Benders Decomposition 160\u003c\/p\u003e \u003cp\u003e6.3.2 Lagrangean Decomposition 161\u003c\/p\u003e \u003cp\u003e6.3.3 Bilevel Decomposition 162\u003c\/p\u003e \u003cp\u003e6.4 Algebraic Modeling Languages 162\u003c\/p\u003e \u003cp\u003e6.5 Applications in Sustainable Process and Energy Systems 164\u003c\/p\u003e \u003cp\u003e6.5.1 Hydrogen 164\u003c\/p\u003e \u003cp\u003e6.5.2 Biomass 165\u003c\/p\u003e \u003cp\u003e6.5.3 Methanol 166\u003c\/p\u003e \u003cp\u003e6.5.4 Power Systems 167\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 169\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multiscale Modeling and Optimization of Carbon Capture Processes 179\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKyeongjun Seo, Mark A. Stadtherr, and Michael Baldea\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 179\u003c\/p\u003e \u003cp\u003e7.2 Modeling of Carbon Capture Processes 180\u003c\/p\u003e \u003cp\u003e7.2.1 Process Structure and Operation 180\u003c\/p\u003e \u003cp\u003e7.2.2 Mathematical Modeling 183\u003c\/p\u003e \u003cp\u003e7.2.3 Multiscale Optimization 185\u003c\/p\u003e \u003cp\u003e7.3 Multiscale Modeling and Optimization Results 187\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 193\u003c\/p\u003e \u003cp\u003eAcknowledgments 194\u003c\/p\u003e \u003cp\u003eDisclaimer 194\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Integrated Design and Operability Optimization of Sustainable Process Intensification Systems 199\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYuhe Tian, Rahul Bindlish, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 199\u003c\/p\u003e \u003cp\u003e8.2 Methodology Framework 202\u003c\/p\u003e \u003cp\u003e8.2.1 Prelude: Phenomena-Based Process Synthesis 202\u003c\/p\u003e \u003cp\u003e8.2.2 Generalized Modular Representation Framework 203\u003c\/p\u003e \u003cp\u003e8.2.3 Integrated Synthesis and Operability Optimization 205\u003c\/p\u003e \u003cp\u003e8.2.3.1 Safety Considerations via Risk Analysis 205\u003c\/p\u003e \u003cp\u003e8.2.3.2 Design Under Uncertainty via Flexibility Analysis 208\u003c\/p\u003e \u003cp\u003e8.3 Case Studies 210\u003c\/p\u003e \u003cp\u003e8.3.1 MMA Purification 210\u003c\/p\u003e \u003cp\u003e8.3.1.1 Process Description 210\u003c\/p\u003e \u003cp\u003e8.3.1.2 GMF Simulation of Base Case Design 211\u003c\/p\u003e \u003cp\u003e8.3.1.3 GMF Synthesis for Grassroots Design 212\u003c\/p\u003e \u003cp\u003e8.3.2 MTBE Production 214\u003c\/p\u003e \u003cp\u003e8.3.2.1 Process Description 214\u003c\/p\u003e \u003cp\u003e8.3.2.2 Integrated GMF Synthesis and Operability Optimization 215\u003c\/p\u003e \u003cp\u003e8.4 Concluding Remarks 218\u003c\/p\u003e \u003cp\u003eAcknowledgment 218\u003c\/p\u003e \u003cp\u003eReferences 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Circular Economy Assessment Tools for Process Systems 223\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePaola Munoz-Briones, Kenneth Martinez, Javiera Vergara-Zambrano, and Styliani Avraamidou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 223\u003c\/p\u003e \u003cp\u003e9.2 Circular Economy Assessment in the Food Sector 226\u003c\/p\u003e \u003cp\u003e9.2.1 Example: CE Assessment for Food Packaging Waste Management Technologies 230\u003c\/p\u003e \u003cp\u003e9.3 Circular Economy Assessment in the Chemical Industry 232\u003c\/p\u003e \u003cp\u003e9.3.1 Example: Circular Economy Assessment of Viable for Fuels for Mobility 235\u003c\/p\u003e \u003cp\u003e9.4 Circular Economy Metrics for Energy Systems 237\u003c\/p\u003e \u003cp\u003eReferences 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Decarbonization of Steam Cracking for Clean Olefins Production: Optimal Microgrid Scheduling 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSaba Ghasemi Naraghi, Tylee Kareck, Lingyun Xiao, Richard Reed, Paritosh Ramanan, and Zheyu Jiang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 251\u003c\/p\u003e \u003cp\u003e10.2 Dynamic Optimization of Steam Cracking Process 254\u003c\/p\u003e \u003cp\u003e10.3 Scenario-Based Optimal Microgrid Scheduling Problem 258\u003c\/p\u003e \u003cp\u003e10.4 Illustrative Case Studies 265\u003c\/p\u003e \u003cp\u003e10.4.1 Problem Setting 265\u003c\/p\u003e \u003cp\u003e10.4.2 Grid-Connected Mode 267\u003c\/p\u003e \u003cp\u003e10.4.3 Islanded Mode 272\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 275\u003c\/p\u003e \u003cp\u003eAcknowledgments 275\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multiscale Strategies for the Use of Chemicals as Energy Storage Systems 279\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDiego Santamaría, Antonio Sánchez, and Mariano Martín\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 279\u003c\/p\u003e \u003cp\u003e11.2 Methodology 279\u003c\/p\u003e \u003cp\u003e11.2.1 Process Design 280\u003c\/p\u003e \u003cp\u003e11.2.2 Process Scale Up\/Down 282\u003c\/p\u003e \u003cp\u003e11.2.3 Enterprise-Wide Level 283\u003c\/p\u003e \u003cp\u003e11.3 Cases of Study 285\u003c\/p\u003e \u003cp\u003e11.3.1 Hydrogen 285\u003c\/p\u003e \u003cp\u003e11.3.2 Methane 291\u003c\/p\u003e \u003cp\u003e11.3.3 Methanol 295\u003c\/p\u003e \u003cp\u003e11.3.4 Ammonia 298\u003c\/p\u003e \u003cp\u003e11.4 Conclusions 304\u003c\/p\u003e \u003cp\u003eAcknowledgment 304\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Repurposing a Conventional Oil Refinery for Biomass Processing to Aviation Fuel: Process Design and Techno-Environmental Evaluation for a Real Operating Plant 317\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eValeria González, Alejandro Pedezert, Soledad Gutiérrez, Roberto Kreimerman, Lucia Pittaluga, and Ana I. Torres\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 317\u003c\/p\u003e \u003cp\u003e12.2 Overview of Feed Options, Processing Pathways and Current Infrastructure 319\u003c\/p\u003e \u003cp\u003e12.3 Sustainable Aviation Fuel Process Design 321\u003c\/p\u003e \u003cp\u003e12.3.1 Base Process Overview 322\u003c\/p\u003e \u003cp\u003e12.3.2 Modeling 323\u003c\/p\u003e \u003cp\u003e12.3.2.1 Feedstock 323\u003c\/p\u003e \u003cp\u003e12.3.2.2 Hydrotreating Reactor (R-100) 323\u003c\/p\u003e \u003cp\u003e12.3.2.3 Area 200: Separation 329\u003c\/p\u003e \u003cp\u003e12.3.2.4 Area 300: Hydrocracking and Hydroisomerization 331\u003c\/p\u003e \u003cp\u003e12.3.2.5 Area 400: Products Separation 331\u003c\/p\u003e \u003cp\u003e12.3.3 Simulation Results 331\u003c\/p\u003e \u003cp\u003e12.4 Sustainable Aviation Fuel Process Design: Adjustments in Design to Match Current Operations in the Refinery 333\u003c\/p\u003e \u003cp\u003e12.4.1 Simulation Results 337\u003c\/p\u003e \u003cp\u003e12.5 Environmental Assessment Using GREENSCOPE 337\u003c\/p\u003e \u003cp\u003e12.5.1 Overview of Selected Indicators 338\u003c\/p\u003e \u003cp\u003e12.5.1.1 Dangerous Materials 338\u003c\/p\u003e \u003cp\u003e12.5.1.2 Chemical Exposure Index (CEI) 339\u003c\/p\u003e \u003cp\u003e12.5.1.3 Health Hazards in the Workplace 339\u003c\/p\u003e \u003cp\u003e12.5.1.4 Safety Hazards 339\u003c\/p\u003e \u003cp\u003e12.5.1.5 Substance Toxicity 340\u003c\/p\u003e \u003cp\u003e12.5.1.6 Enviromental Hazard 340\u003c\/p\u003e \u003cp\u003e12.5.1.7 Indicators from Potency Factors: Global Warming, Smog, Acidification, Ozone Depletion, and Cancer 341\u003c\/p\u003e \u003cp\u003e12.5.1.8 Liquid Emissions 341\u003c\/p\u003e \u003cp\u003e12.5.2 Results 341\u003c\/p\u003e \u003cp\u003e12.6 Summary and Final Remarks 343\u003c\/p\u003e \u003cp\u003eAcknowledgments 344\u003c\/p\u003e \u003cp\u003eReferences 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Uncertainty Quantification of Solid Sorbent-Based CO 2 Capture Processes 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAna Flávia Monteiro and Debangsu Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 349\u003c\/p\u003e \u003cp\u003e13.2 Methodology 352\u003c\/p\u003e \u003cp\u003e13.2.1 UQ of Model Parameters 352\u003c\/p\u003e \u003cp\u003e13.2.2 UQ of Model Form Discrepancy 354\u003c\/p\u003e \u003cp\u003e13.3 Example-UQ of a Solid-Based CO 2 Capture System in a Fixed Bed 356\u003c\/p\u003e \u003cp\u003e13.3.1 UQ of the Isotherm Model 356\u003c\/p\u003e \u003cp\u003e13.3.2 Uncertainty Propagation 359\u003c\/p\u003e \u003cp\u003e13.3.2.1 Lab-Scale Axial Flow-Fixed Bed 360\u003c\/p\u003e \u003cp\u003e13.3.2.2 Commercial-Scale Radial Flow-Fixed Bed 361\u003c\/p\u003e \u003cp\u003e13.4 Concluding Remarks 363\u003c\/p\u003e \u003cp\u003eReferences 366\u003c\/p\u003e \u003cp\u003eIndex 371\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Chemistry [\u003ca title=\"See our other books on Chemistry\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Chemistry%20%5BPN%5D%22\"\u003ePN\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52433203003672,"sku":"9781394205578","price":117.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394205578.jpg?v=1784851593","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/optimization-of-sustainable-process-systems-multiscale-models-and-uncertainties-hardback-9781394205578","provider":"Freshly Printed Books","version":"1.0","type":"link"}