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Development of mathematical programming models for producing and supplying biodiesel using microalgae biomass

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Many studies have developed mathematical programming models for optimal design of supply chains for agricultural or lingo-cellulosic biomass-derived bioethanol production. However, because of the shortcomings of using agricultural (food supply problems) and lingo-cellulosic biomass (low biomass avai...
Many studies have developed mathematical programming models for optimal design of supply chains for agricultural or lingo-cellulosic biomass-derived bioethanol production. However, because of the shortcomings of using agricultural (food supply problems) and lingo-cellulosic biomass (low biomass availability and processing yield) as feedstock, use of micro-algal biomass has been considered for use as a feedstock for biodiesel (biofuel). Thus, in this study we developed a deterministic mathematical programming model for strategic planning design of a microalgae biomass-to-biodiesel supply chain network (MBBSCN) from feedstock fields to end users that simultaneously satisfies resource constraints, demand constraints, and technology over a long-term planning horizon. The proposed deterministic model can help to determine where and how much feedstock to be transported, and where and how many refineries to be constructed to minimize the expected total cost including the co-product (naphtha and power) benefit. To demonstrate the feasibility of the proposed model, we conducted a case study based on the Korea biodiesel market data. In this case study, the optimized (i.e., most cost-effective) supply chain design can be gained at a reliable cost of ~$US 5.91/gal ($US 1.56/l). In particular, this study can help to identify the technological bottlenecks and major cost drivers for the microalgae-to-diesel strategy, and can be also a guideline for development of various mathematical programming models for optimal design of microalgae biomass-derived biofuel supply chain like lingo-cellulosic biomass-based optimization studies.
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CONTENTS

Abstract i
...
CONTENTS

Abstract i
List of Figures v
List of Tables vi
1. Introduction 1
2. Problem statement 4
2.1. Technologies overview 4
2.1.1 Refinery 4
2.1.2 Carbon capture and storage system 5
2.1.3 Wastewater treatment system 5
2.1.4 Transportation 6
2.2. Network configuration 6
3. Mathematical model formulation 7
3.1. Objective function 8
3.2. Constraints 9
3.2.1 Constraints on refineries 9
3.2.2 Constraints on demand cities 11
3.2.3 Integrality and non-negativity constraints 11
3.2.4 Carbon capture and storage system constraints 12
3.2.5 Parameter constraints for carbon capture and storage system 13
3.2.5 Parameter constraints for pipeline construction with wastewater treatment system 16
4. Case Studies 18
3.1. Potential feedstock resource locations 19
3.2. Potential refinery locations and costs 20
3.1. Transportations capacities and costs 20
5. Results and discussion 20
5.1. Optimal costs 20
5.2. Optimal design 21
5.3. Feedstock supply 22
5.4. Single-period and multi-period 23
5.5. Sensitivity analysis 24
6. Conclusions 25
Nomenclature 28
Summary in Korean 51
References 53
감사의 글 62