Strategic Planning for the Sustainable Production of Biofuels - 1st Edition - ISBN: 9780128181782, 9780128181799

Strategic Planning for the Sustainable Production of Biofuels

1st Edition

Authors: José Maria Ponce-Ortega José Ezequiel Santibañez-Aguilar
Paperback ISBN: 9780128181782
eBook ISBN: 9780128181799
Imprint: Elsevier
Published Date: 8th March 2019
Page Count: 530
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Strategic Planning for the Sustainable Production of Biofuels presents several optimization models for the design and planning of sustainable biorefinery supply chains, including issues surrounding the potential of biomass feedstocks in multiple harvesting sites, availability and seasonality of biomass resources, different potential geographical locations for processing plants that produce multiple products using diverse production technologies, economies of scale for production technologies, demands and prices of multiple products, locations of storage facilities, and a number of transportation modes. Sustainability considerations are incorporated into the proposed models by including simultaneous economic, environmental and social performance in the evaluation of the supply chain designs.

Key Features

  • Covers different optimization models for the strategic planning of biorefining systems
  • Includes the GAMS and MATLAB codes for solving various problems
  • Considers sustainability criteria in the presented models
  • Presents different approaches for obtained trade-off solutions
  • Provides general software that can be used for solving different problems


Gradueate and undergraduate students, researches, teachers, and different dessicion makers in the area of bioenergy. Also, this book can be used as a textbook for courses about supply chains, optimization and bioenergy in different engineering courses

Table of Contents

Chapter 1. Introduction
1.1 Importance of biofuels and biorefineries
1.2 Strategic planning
1.3 Optimization
1.4 Sustainability
1.5 Description of the book
1.6 References

Chapter 2. Involving environmental aspect in the strategic planning of a biomass conversion system
2.1 Introduction
2.2 Outline of the optimization model
2.3 Mathematical model
2.3.1 Mass balances
2.3.2 Maximum availability for feedstocks
2.3.3 Maximum product demand
2.3.4 Maximum processing limits
2.3.5 Objective functions
2.3.6 Economic objective
2.3.7 Environmental objective
2.4 Solution strategy
2.5 Case study
2.6 Sensitivity analysis
2.7 Concluding remarks
2.8 Nomenclature for Chapter 2
2.8.1 Parameters
2.8.2 Variables
2.8.3 Indexes
2.9 References

Chapter 3. Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives
3.1 Introduction 3.2 Problem statement
3.3 Model formulation
3.3.1 Mass balances for harvesting sites
3.3.2 Mass balance for processing hubs (secondary plants)
3.3.3 Raw material in hubs
3.3.4 Products in hubs
3.3.5 Mass balance for the main plant
3.3.6 Raw material in the main plant
3.3.7 Product in the main plant
3.3.8 Mass balances for the markets
3.3.9 Constraints for total product sales
3.3.10 Storage constraint
3.3.11 Transportation constraints
3.3.12 Processing constraints
3.3.13 Availability constraints
3.3.14 Star and end storage constraints
3.3.15 Objective functions Economic objective function Environmental objective function Social objective function
3.3.16 Remarks for the model
3.4 Case study
3.5 Discussion
3.6 Concluding remarks
3.7 Nomenclature
3.7.1 Sets
3.7.2 Indexes
3.7.3 Parameters
3.7.4 Variables
3.7.5 Binary variables
3.7.6 Boolean variables
3.8 References

Chapter 4. Distributed biorefining networks for the valued-added processing of water hyacinth
4.1 Introduction
4.2 Outline of the model formulation
4.3 Model formulation for Chapter
4 4.3.1 Mass balance for the harvesting of water hyacinth
4.3.2 Availability for the harvested water hyacinth
4.3.3 Mass balance for the splitters before the processing plants
4.3.4 Balances for mixers before the processing facilities
4.3.5 Balances for the technologies used in the processing facilities
4.3.6 Balances for the mixers before the central processing facilities
4.3.7 Balances for the technologies of central processing facilities
4.3.8 Balances for the splitters after the central processing facilities
4.3.9 Balances for the splitters after the central processing facilities
4.3.10 Balances for the markets
4.3.11 Demands for the consumers
4.3.12 Balances for the water treatment in each source
4.3.13 Water treatment technologies in each source
4.3.14 Mass balance for the splitters after the water treatment
4.3.15 Mass balance for the mixers before each water consumer
4.3.16 Component balance for the mixers before each water consumer
4.3.17 Demand for water consumers
4.3.18 Constraints for the water quality for each consumer
4.3.19 Operational cost for the processing facilities
4.3.20 Capital cost for the processing facilities
4.3.21 Operational cost for the central processing facilities
4.3.22 Capital cost for the water treatment units
4.3.23 Operational cost for the water treatment units
4.3.24 Capital cost for the water treatment units
4.3.25 Harvesting cost
4.3.26 Water transportation cost
4.3.27 Biomass transportation cost
4.3.28 Product transportation cost
4.3.29 Total operational cost
4.3.30 Total capital cost
4.3.31 Total sales
4.3.32 Total annual cost (negative of total net profit)
4.3.33 Percentage of eliminated water hyacinth
4.4 Remarks for the model of Chapter 4
4.5 Results
4.6 Concluding remarks 4.7 Nomenclature
4.7.1 Parameters
4.7.2 Variables
4.7.3 Binary variables
4.8 References

Chapter 5. Optimization of the supply chain associated to the production of bioethanol from residues of the agave from the tequila process in Mexico
5.1 Introduction
5.2 Problem statement
5.3 Model formulation
5.3.1 Mass balances in agave cultivating areas
5.3.2 Maximum available agave
5.3.3 Mass balance in the tequila industry
5.3.4 Residues of agave bagasse from the tequila industry
5.3.5 Mass balance in distributed processing plants for bioethanol production
5.3.6 Distribution of products from processing plants to markets
5.3.7 Product demands
5.3.8 Cost of the distributed bioethanol processing plants
5.3.9 Transportation cost for stalks to distributed and central plants
5.3.10 Transportation cost from the tequila industries to distributed and central bioethanol processing plants
5.3.11 Transportation cost for products
5.3.12 Objective function
5.4 Case study
5.4.1 Scenario A (Economic Solution with a constraint of 1% for the bioethanol demand in each consumption site)
5.4.2 Scenario B. (Solution without constraint for the demand of bioethanol in the markets)
5.4.3 Scenario C (Increasing the cultivation area)
5.5 Concluding remarks
5.6 Nomenclature
5.6.1 Indexes
5.6.2 Sets
5.6.3 Parameters
5.6.3 Variables
5.6 References

Chapter 6. Financial risk assessment and optimal planning of biofuels supply chains under uncertainty
6.1 Introduction
6.2 Problem statement
6.3 Mathematical model formulation for Chapter 6
6.4 Objective 1: Expected profit
6.5 Objective 2: Worst case for the net annual profit
6.6 Results and discussion
6.6.1 Distribution of raw material price without correlation
6.6.2 Case with correlated values
6.7 Concluding remarks
6.8 Nomenclature
6.8.1 Variables
6.8.2 Binary variables
6.8.3 Parameters

Chapter 7. Stochastic design of biorefinery supply chains considering economic and environmental objectives
7.1 Introduction
7.2 Problem statement
7.3 Mathematical formulation
7.3.1 Availability of raw material
7.3.2 Mass balances in the suppliers
7.3.3 Mass balances in the processing facilities
7.3.4 Mass balances in the markets
7.3.5 Demand constraint
7.3.6 Relationships for the input-output of the distributed material
7.3.7 Transportation limits and transportation costs
7.3.8 Processing stages in the processing facilities
7.3.9 Processing constraints for the first stage
7.3.10 Processing constraints for the second stage
7.3.11 Storage modelling
7.3.12 Revenue for selling of products
7.3.13 Raw material production cost
7.3.14 Economic objective function
7.3.15 Environmental objective
7.4. Solution approach
7.4.1 Definition of the superstructure
7.4.2 Identification of the parameters under uncertainty
7.4.3 Sampling for uncertain parameters
7.4.4 Solving of the associated deterministic optimization problem
7.4.5 Comparison between different supply chain topologies
7.4.6 Changing of the upper limit for the environmental impact
7.4.7 Standardized regression coefficients
7.5. Case study
7.6. Computer-Aided Tools
7.7. Results and discussion
7.8. Concluding remarks for Chapter
7 7.10. Nomenclature
7.10.1 Indexes
7.10.2 Variables
7.10.3 Parameters
7.11 References

Chapter 8. Mixed-integer dynamic optimization or planning distributed biorefineries
8.1 Introduction
8.2 Problem statement
8.3 Mixed-integer dynamic mathematical optimization model
8.3.1 Raw material inventory in suppliers
8.3.2 Raw material inventory in processing facilities
8.3.3 Raw material inventory in main processing facility
8.3.4 Product inventory in processing facilities
8.3.5 Product inventory in main processing facility
8.3.6 Product inventory in distribution centers
8.3.7 Continuity of the inventories at the beginning and at the end of the time horizon
8.3.8 Raw material orders from facilities to suppliers
8.3.9 Raw material orders from main facility to suppliers
8.3.10 Product orders from distribution centers to facilities
8.3.11 Product orders from distribution centers to main facility
8.3.12 Product orders from consumers to distribution centers
8.3.13 Continuity of the inventories at the beginning and at the end of the horizon
8.3.14 Availability of raw material
8.3.15 Constraints for the demand
8.3.16 Constraints to control the orders from consumers to distribution centers
8.3.17 Constraints for transported flow rate at the outlet and inlet locations
8.3.18 Transportation limits
8.3.19 Processing
8.3.20 Economies of scale for processing facilities
8.3.21 Storage modeling
8.3.22 Operating cost
8.3.23 Total capital cost
8.3.24 Transportation cost
8.3.25 Storage cost 8.3.26 Net annual Profit
8.3.27 Control product demand
8.4 Nonlinear model predictive control approach
8.5 Solution approach for the MIDO problem
8.6 Results
8.7 Conclusions
8.8 Nomenclature
8.8.1 Parameters
8.8.2 Binary variables
8.8.3 Variables
8.9 References

Appendix A - GAMS code for the model of Chapter 2
Appendix B – GAMS code for the model of Chapter 3
Appendix C – GAMS code for the model of Chapter 4
Appendix D – GAMS code for the model of Chapter 6
Appendix E – GAMS code for the model of Chapter 7
Appendix F – GAMS code for the model of Chapter 8


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© Elsevier 2019
8th March 2019
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About the Author

José Maria Ponce-Ortega

Dr. José María Ponce-Ortega got his Ph.D. and Master degrees in Chemical Engineering in the Institute of Technology of Celaya in Mexico in 2009 and 2003, respectively. He stayed as a postdoctoral researcher at Texas A&M University and as visiting scholar in Carnegie Mellon University. Dr. Ponce-Ortega is full professor at the Universidad Michoacana de San Nicolás de Hidalgo, and he is member of the National Research System of Mexico. The research interest of Dr. Ponce-Ortega is in the areas of optimization of chemical processes, sustainable design, energy, mass, water and property integration and supply chain optimization. Dr. Ponce-Ortega has published more than 180 papers, 1 book and 46 chapter books. He also has supervised 12 Ph.D. and 24 Master students. He also has 15 funded research projects for about $US 1,000,000.00. Dr. Ponce-Ortega is member of the editorial board of the Clean Technologies and Environmental Policy Journal, and in the Process Integration and Optimization for Sustainability Journal.

Affiliations and Expertise

Professor, Department of Chemical Engineering, Michoacan University of Saint Nicholas of Hidalgo - Michoacán, Mexico

José Ezequiel Santibañez-Aguilar

Dr. José Ezequiel Santibañez- Aguilar got his Ph.D. and Master degrees in the Chemical Engineering Department in the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2016 and 2013, respectively. He stayed as scholar researcher at Texas A&M University and Northwestern University. Currently, he is professor at the Tecnologico de Monterrey in Mexico and he is member of the National Research System of Mexico. His research interest is in the area of optimization of supply chains for biorefineries accounting for the involved sustainability. He has published more than 20 papers and 11 book chapters.

Affiliations and Expertise

Professor, School of Engineering and Science, Tecnologico de Monterrey - Nuevo León, Mexico

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