Strategic Planning for the Sustainable Production of Biofuels

By José Maria Ponce-Ortega and José Ezequiel Santibañez-Aguilar

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.

• 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

• Language: English
• Publisher: Elsevier Science
• Release date: Mar 5, 2019
• ISBN: 9780128181799

José Maria Ponce-Ortega

José María Ponce-Ortega is a Professor at the Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He obtained his Ph.D. and Master degrees in Chemical Engineering from the Institute of Technology of Celaya in Mexico in 2009 and 2003, respectively. He stayed as a postdoctoral researcher at Texas A&M University, USA and as visiting scholar at Carnegie Mellon University, USA. Dr. Ponce-Ortega is a full professor at the Universidad Michoacana de San Nicolás de Hidalgo, and he is a 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 280 papers, 3 books and 50 book chapters. He also has supervised 25 Ph.D. and 40 Master students. He also has 15 funded research projects for about $US 1,000,000.00. Dr. Ponce-Ortega is a member of the editorial board of the Clean Technologies and Environmental Policy Journal, and of the Process Integration and Optimization for Sustainability Journal. He is also a subject editor in the journal Sustainable Production and Consumption.

Book preview

Strategic Planning for the Sustainable Production of Biofuels

José María Ponce-Ortega

Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Mexico

José Ezequiel Santibañez-Aguilar

Tecnologico de Monterrey, Monterrey, Mexico

Table of Contents

Cover image

Title page

Copyright

Preface

Chapter 1. Introduction

Abstract

1.1 Importance of Biofuels and Biorefineries

1.2 Strategic Planning

1.3 Optimization

1.4 Sustainability

1.5 Description of the Book

References

Further Reading

Chapter 2. Environmental Aspects in the Strategic Planning of a Biomass Conversion System

Abstract

2.1 Introduction

2.2 Outline of the Optimization Model

2.3 Mathematical Model

2.4 Solution Strategy

2.5 Case Study

2.6 Sensitivity Analysis

2.7 Concluding Remarks

2.8 Nomenclature for Chapter 2

References

Chapter 3. Optimal Planning and Site Selection for Distributed Multiproduct Biorefineries Involving Economic, Environmental, and Social Objectives

Abstract

3.1 Introduction

3.2 Problem Statement

3.3 Model Formulation

3.4 Case Study

3.5 Discussion

3.6 Concluding Remarks

3.7 Nomenclature

References

Further Reading

Chapter 4. Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth

Abstract

4.1 Introduction

4.2 Outline of the Model Formulation

4.3 Model Formulation

4.4 Remarks on the Model

4.5 Results

4.6 Concluding Remarks

4.7 Nomenclature

References

Chapter 5. Optimization of the Supply Chain Associated to the Production of Bioethanol From Residues of Agave From the Tequila Process in Mexico

Abstract

5.1 Introduction

5.2 Problem Statement

5.3 Model Formulation

5.4 Case Study

5.5 Concluding Remarks

5.6 Nomenclature

References

Chapter 6. Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains Under Uncertainty

Abstract

6.1 Introduction

6.2 Problem Statement

6.3 Mathematical Model Formulation

6.4 Objective 1: Expected Profit

6.5 Objective 2: Worst Case for the Net Annual Profit

6.6 Results and Discussion

6.7 Concluding Remarks

6.8 Nomenclature

Chapter 7. Stochastic Design of Biorefinery Supply Chains Considering Economic and Environmental Objectives

Abstract

7.1 Introduction

7.2 Problem Statement

7.3 Mathematical Formulation

7.4 Solution Approach

7.5 Case Study

7.6 Computer-Aided Tools

7.7 Results and Discussion

7.8 Concluding Remarks

7.9 Nomenclature

References

Chapter 8. Mixed-Integer Dynamic Optimization for Planning Distributed Biorefineries

Abstract

8.1 Introduction

8.2 Problem Statement

8.3 Mixed-Integer Dynamic Mathematical Optimization Model

8.4 Nonlinear Model Predictive Control Approach

8.5 Solution Approach for the MIDO Problem

8.6 Results

8.7 Conclusions

8.8 Nomenclature

References

Appendices. Code Used in the Book

Appendix A GAMS Code for Model of Chapter 2, Environmental Aspects in the Strategic Planning of a Biomass Conversion System

Appendix B GAMS Code for Model of Chapter 3, Optimal Planning and Site Selection for Distributed Multiproduct Biorefineries Involving Economic, Environmental, and Social Objectives

Appendix C GAMS Code for Model of Chapter 4, Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth

Appendix D GAMS Code for Model of Chapter 5, Optimization of the Supply Chain Associated to the Production of Bioethanol from Residues of Agave from the Tequila Process in Mexico

Appendix E GAMS Code for Model of Chapter 6, Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains under Uncertainty

Appendix F GAMS and MATLAB Code for Chapter 7, Stochastic Design of Biorefinery Supply Chains Considering Economic and Environmental Objectives

Appendix G GAMS Code for Model of Chapter 8, Mixed-Integer Dynamic Optimization for Planning Distributed Biorefineries

Index

Copyright

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Preface

Biorefinery systems are a well-suited solution for replacing fossil fuels. Several aspects need to be taken into consideration when implementing such a system, including the selection of feedstocks, processing routes, products, sites for harvesting, processing and markets, in addition to different sustainability criteria. Optimal planning of biorefinery systems is an important task. But it is a complicated task that involves making decisions about the aforementioned aspects. To this end, this book presents general optimization models that can be used by researchers, students and decision makers for the design and planning of sustainable biorefinery supply chains. The mathematical formulations provided here take into account relevant issues such as biomass feedstocks available in multiple harvesting sites, availability and seasonality of biomass resources, different geographical locations for processing plants that produce multiple products using diverse production technologies, economies of scale for the production technologies, demands and prices of multiple products in different markets, locations of storage facilities and a number of transportation modes between the supply chain components. Sustainability is incorporated into the proposed models by including simultaneous economic, environmental and social performance in the evaluation of the supply chain designs.

This text uses GAMS and MATLAB software to code and solve the different supply chain planning problems, something not found in similar books. GAMS makes it easy to implement optimization formulations, and MATLAB helps with the subroutines.

Several case studies for the strategic planning of biorefinery systems are also presented in this book, and the corresponding data are incorporated in the optimization code given. It is thus easy to modify the GAMS code for different case studies by incorporating the proper data.

The content of this book is described as follows.

Chapter 1, Introduction, offers an introduction to supply chain management in biorefining systems and introduces the basic concepts used in the strategic planning of such along with a literature review.

Chapter 2, Environmental Aspects in the Strategic Planning of a Biomass Conversion System, presents a multiobjective optimization model based on a mathematical programming formulation for the optimal planning of a biorefinery, considering the optimal selection of feedstock, processing technology and multiple products. The multiobjective optimization problem simultaneously considers profit maximization and environmental impact minimization. The economic objective function takes into account the availability of bioresources, processing limits and demand of products, as well as the costs of feedstocks, products and processing routes, while the environmental assessment includes the overall environmental impact measured through the ecoindicator-99 based on lifecycle analysis methodology.

Chapter 3, Optimal Planning and Site Selection for Distributed Multiproduct Biorefineries Involving Economic, Environmental, and Social Objectives, presents a multiobjective, multiperiod mixed-integer linear program that seeks to maximize the profit of the supply chain, to minimize its environmental impact and to maximize the number of jobs generated by its implementation. The environmental impact is measured by the ecoindicator-99 according to the lifecycle assessment technique, and the social objective is quantified by the number of jobs generated. The Pareto-optimal solutions are obtained using the ε-constraint method. To illustrate the capabilities of the proposed multisite system model, a case study addressing the optimal design and planning of a biorefinery supply chain to fulfill the expected ethanol and biodiesel demands in Mexico is presented.

Chapter 4, Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth, presents a general superstructure and a mathematical programming model for the sustainable elimination of water hyacinth through a distributed biorefining network. The proposed model optimizes the selection of the products, the siting and sizing for the processing facilities and the selection of the markets while accounting for technical and economic constraints. A case study for the central part of Mexico, where water hyacinth is a serious problem, is used to show the applicability of the proposed holistic approach. The results show that an optimally synthesized distributed biorefining network is capable of the sustainable and economic elimination of water hyacinth from contaminated water bodies while generating value. Additionally, the results shown through Pareto curves allow the identification of a set of optimal solutions featuring tradeoffs between the economic and the environmental objectives.

Chapter 5, Optimization of the Supply Chain Associated to the Production of Bioethanol From Residues of Agave from the Tequila Process in Mexico, presents an optimization framework for designing a supply chain for the bioethanol production from residues of agave bagasse obtained in the tequila processing in Mexico, where central and distributed bioethanol processing plants are considered. The bioethanol production process in the central and distributed plants is modeled according to conversion factors for the different processing steps obtained from experimental data. The proposed optimization formulation also considers the total available agave and the bioethanol demand in Mexico. Several scenarios are analyzed for the bioethanol production from agave bagasse in Mexico, where positive results are obtained from the reuse of residues of agave bagasse for the bioethanol production with considerable profits and satisfying a significant demand of the gasoline required in the zone.

Chapter 6, Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains Under Uncertainty, presents a mathematical programming model for the optimal planning of a distributed system of biorefineries that considers explicitly the uncertainty associated with the supply chain operation as well as the associated risk. The capabilities of the approach proposed are demonstrated through its application to the production of biofuels in Mexico considering multiple raw materials and products.

Chapter 7, Stochastic Design of Biorefinery Supply Chains Considering Economic and Environmental Objectives, presents an approach to optimal planning with an uncertain price of feedstock for a biomass conversion system involving both economic and environmental issues. The environmental impact is measured using ecoindicator-99 and the economic aspect is considered through the net annual profit. On the other hand, the uncertain raw material price was considered by the stochastic generation of scenarios using the Latin Hypercube method followed by the implementation of the Monte-Carlo method, where a deterministic optimization problem was solved for each of the scenarios to select the structure of the more robust supply chain relying on statistical data. The proposed approach was applied to a case study of a distributed biorefinery system in Mexico.

Chapter 8, Mixed-Integer Dynamic Optimization for Planning Distributed Biorefineries, presents a dynamic optimization model for the optimal planning of a distributed biorefinery system taking into account the time dependence of the involved variables and parameters. In addition, this chapter incorporates a model predictive control methodology to obtain the behavior of the storages and orders of the supply chain, where the objective function is the difference between the required and satisfied demands in the markets. Thus, this study considers relevant issues including multiple available biomass feedstocks at various harvesting sites, the availability and seasonality of biomass resources, potential geographical locations for processing plants that produce multiple products using diverse production technologies, economies of scale for the production technologies, demands and prices of multiple products in each consumer, locations of storage facilities and a number of transportation modes between the supply chain components. The model was applied to a case study for a distributed biorefinery system in Mexico.

Finally, the authors wish to acknowledge the Universidad Michoacana de San Nicolás de Hidalgo and the Tecnologico de Monterrey for giving them the opportunity to work on this important project as well as the needed support to finish it.

Chapter 1

Introduction

Abstract

Biorefineries appear to be a way to replace fossil fuels. In the implementation of a biorefinery several options need to be explored, including the selection of feedstocks, processing routes, products, sites for harvesting, processing, and markets, in addition to different sustainability criteria. The optimal solution to this problem is not obvious. Thus, this book presents several optimization models for the design and planning of sustainable biorefinery supply chains (SCs) taking into account relevant issues such as several potential biomass feedstocks available 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 the production technologies, demands and prices of multiple products in each market, locations of storage facilities, and a number of transportation modes between the SC components. Sustainability considerations are incorporated into the proposed models by including simultaneous economic, environmental, and social performance in the evaluation of the SC designs.

One important future of this book is that it includes the optimization models coded in the software GAMS and MatLab, which can be useful to understand the codes and can be used for solving different problems for different locations and with different data.

Keywords

Biorefineries; biofuels; biomass; optimization; environmental impact; sustainable process; optimization; strategic planning; optimization; mathematical programming

1.1 Importance of Biofuels and Biorefineries

Currently, the increasing demand for energy around the world has led to several challenges associated with the use of fossil fuels. In addition to the continuous depletion of fossil fuel reserves, there are substantial problems related to the climate change caused by greenhouse gas emissions (GHGE) from burning fossil fuels. These challenges have spurred research to develop new sources of energy and modern low-carbon technologies that can reduce the negative environmental impact by fossil fuels and improve the economic and social aspects of sustainability. In this context, biomass has gained considerable attention as a feedstock for energy production because of its attractive characteristics, including its availability as a renewable resource, reduction in the GHGE life cycle, creation of new infrastructure, and associated jobs and flexibility to produce a wide variety of products. The inherent flexibility of biomass feedstocks to produce several products (biofuels, polymers, specialty chemicals, etc.) has encouraged an increasing body of research to investigate the synthesis of processing pathways or technologies for designing better biorefineries.

Recently, Bao, Ng, Tay, Jiménez-Gutiérrez, and El-Halwagi (2011) developed a shortcut method to synthesize and screen integrated biorefineries. In this approach, a structural representation is used to track individual chemicals while allowing the processing of multiple chemicals in production technologies. Ng (2010) presented an optimization-based automated targeting procedure to determine the maximum biofuel production and revenue levels in an integrated biorefinery. A novel and systematic two-stage approach to synthesize and optimize a biorefinery configuration given available feedstocks and desired products was proposed by Pham and El-Halwagi (2012). In addition, Ponce-Ortega, Pham, El-Halwagi, and El-Baz (2012) proposed a new general systematic approach to selecting optimal pathways for a biorefinery design. Furthermore, Aksoy, Cullinan, Sammons, and Eden (2008) proposed a method to design integrated biorefineries. However, previous approaches have not considered optimizing the supply chain (SC) for biorefineries.

1.2 Strategic Planning

With respect to the SC optimization of biorefineries, Sammons, Eden, Yuan, Cullinan, and Aksoy (2007) proposed a systematic framework to optimize the product portfolio and process configuration in integrated biorefineries. Subsequently, Sammons et al. (2008) developed a methodology to assist the bioprocessing industry in evaluating the profitability of different possible production routes and product portfolios while maximizing stakeholder value through the mixed-integer linear programming (MILP) model presented by Van Dyken, Bakken, and Skjelbred (2010). This model was developed to design biomass-based SCs. Elms and El-Halwagi (2009) presented a procedure to schedule and operate biodiesel plants considering various feedstock options. Bowling, Ponce-Ortega, and El-Halwagi (2011) included the effect of economies of scale on the selection, sizing, location, and planning of a multisite biorefinery system. Akgul, Zamboni, Bezzo, Shah, and Papageorgiou (2011) presented MILP models to optimally design the bioethanol SC. Aksoy et al. (2011) studied four biorefinery technologies for feedstock allocation, optimum facility location, economic feasibility, and their economic impacts on Alabama. A mixed-integer nonlinear programming optimization model for a sustainable design and behavior analysis of the sugar and ethanol SC was proposed by Corsano, Vecchietti, and Montagna (2011). Furthermore, Kim, Realff, Lee, Whittaker, and Furtner (2011) presented a general optimization model that enables the selection of fuel-conversion technologies, capacities, biomass locations, and the logistics of transportation from forestry resource locations to the conversion sites and final markets. Mansoornejad, Chambost, and Stuart (2010) presented a methodology that links product/process portfolio design for making optimal long-term decisions for forest biorefineries. It is worth noting that previous approaches have only included different economic objectives for optimizing biorefinery SCs.

To assess the environmental impact, Cherubini et al. (2009) evaluated different technologies for biofuel production based on the energetic efficiency while considering the environmental impact using a single optimization approach. This approach does not include trade-offs between economic and environmental objectives. Herva, Franco, Carrasco, and Roca (2011) classified a series of environmental indicators that can be applied to evaluate production processes and products. Hugo and Pistikopoulos (2005) presented a multiobjective optimization approach to consider economic and environmental objectives in the SC optimization problem. Zamboni, Shah, and Bezzo (2009) proposed a general modeling framework to drive the decision-making process to strategically design biofuel SC networks, where the design task was formulated as an MILP problem that considers the simultaneous minimization of the SC operating costs and the environmental impact (measured in terms of GHGE). An MILP optimization approach to designing sugar-based SC biorefineries that involves economic and environmental concerns was reported by Mele, Guillén-Gosálbez, and Jiménez (2009). You and Wang (2011) presented an optimization model to design and plan biomass and liquid SCs based on economic and environmental criteria; this approach was illustrated through a case study for the state of Iowa. Elia, Baliban, Xiao, and Floudas (2011) developed an MILP formulation to analyze the US energy SC network for hybrid coal, biomass, and natural gas-to-liquids facilities. A multiobjective optimization model to optimize a biorefinery was reported by Santibañez-Aguilar, González-Campos, Ponce-Ortega, Serna-González, and El-Halwagi (2011), an approach that simultaneously maximized profit while minimizing environmental impact. Recently, You, Tao, Graciano, and Snyder (2012) proposed a new approach to optimally plan biofuel SCs considering economic, environmental, and social objectives. However, these previously reported methodologies to optimize biorefinery SCs have not simultaneously considered the sustainability criteria.

1.3 Optimization

Optimization refers to finding the best solution of a given problem, accounting for specific limitations. Mathematically, optimization is used to maximize or minimize a given objective function subject to a set of equality and/or inequality constraints, where there are a set of degrees of freedom involved. Optimization problems can be classified as linear and nonlinear and continuous or discrete. Furthermore, when more than one objective is considered, the optimization problems are classified as mono- or multiobjective depending on the number of objective functions. It should be noted that software such as GAMS (Brooke, Kendrick, Meeruas, & Raman, 2011) can be used to solve all these problems, and in this book such software was used to implement the presented optimization model.

1.4 Sustainability

During the last three decades, several authors have developed diverse assessment frameworks that integrate a number of dimensions required for sustainable development, which is defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs (WCED, 1987). In this context, Jansen (2003) identified three relevant dimensions for sustainable development: the interactions between culture, structure, and technology; the optimization/improvement/renewal approaches; and the parts involved. Vachon and Mao (2008) assessed the linkage between SC characteristics and three suggested dimensions of sustainability, namely, environmental performance, corporate environmental practices, and social sustainability. An overview of the environmental, social, and economic footprints indicators that can be used to measure sustainability was presented by Cucek, Klemes, and Kravanja (2012). Recently, Lozano (2008) proposed the concept of two-tiered sustainability equilibria for depicting sustainability. This concept centers on the interaction between economic, environmental, and social, as well as other aspects over time. After analyzing sustainability reports from three companies using a holistic perspective, Lozano and Huisingh (2011) identified the connections between the economic, ecological, and social dimensions and, in certain cases, were able to relate these connections to time. These authors proposed a new category, interlinked issues and dimensions, as the final stage in sustainability reporting analysis. The interlinked category could help companies address the short- and long-term ecological/societal horizons along with their economic scope. In an additional study, Lozano (2012) provided an analysis of 16 of the most widely used initiatives that have been developed to contribute to sustainability factors (economic, environmental, social, and time). This researcher proposed the Corporate Integration of Voluntary Initiatives for Sustainability framework to understand and apply the corporate sustainability initiatives better.

1.5 Description of the Book

This book presents several optimization strategies for the optimal planning of biorefining systems. For all the presented optimization strategies, the corresponding GAMS code has been included Chapter 2, Involving Environmental Aspect in the Strategic Planning of a Biomass Conversion System, presents an optimization formulation for the optimal planning of distributed biorefining systems that consider economic and environmental aspects. In Chapter 3, Optimal Planning and Site Selection for Distributed Multiproduct Biorefineries Involving Economic, Environmental, and Social Objectives, the social dimension of sustainability is included as a third objective function. In Chapter 4, Distributed Biorefining Networks for the Value-Added Processing of Water Hyacinth, an optimization formulation for the optimal planning of distributed biorefining systems for the proper use of water hyacinth is given, whereas Chapter 5, Optimization of the Supply Chain Associated to the Production of Bioethanol From Residues of Agave From the Tequila Process in Mexico, considers agave residues. Chapter 6, Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains Under Uncertainty, looks at the financial risk involved in the planning of a biorefining system, and