Biodiesel Supply Research Articles

Novel Methodology to Assess Advanced Biofuel Production at Regional Level: Case Study for Cereal Straw Supply Chains

Nowadays, there is an urgent necessity for breakthrough technologies able to reduce process complexity and to lower costs to make the biomass-based biodiesel supply chain competitive. This paper presents an innovative methodology for the identification of secondary biomass potentials for biofuel production. The methodology, divided into four steps, defines the criteria that allow identification of promising scenarios of biomass supply chains for commercial application of innovative technologies. The model set up a scoring procedure based on carbon emission avoidance, efficient resource exploitation and sustainability indexes. Applying the four levels of analysis (biomass availability, suitability analysis, regionalization and multi-criteria analysis), the cereal straw supply chains in four European districts (Scandinavian, Hanseatic, Central European and Mediterranean) have been identified and grouped in 18 biomass supply regional clusters (BSRCs). The clusters represent the most promising hotspots where biomass availability, logistics and feedstock requirements for a novel conversion technology meet. Central European and Mediterranean districts are those with the highest cereal straw bioeconomic potentials which allow mono-feedstock supply chains and relatively high capacity of a conversion technology at 200 MWth and beyond. Central Europe has an overall bioeconomic potential of 16.249 kt (232.359 TJ) distributed over 36 regions and organized in 12 BSRCs. The Mediterranean district has an overall bioeconomic potential of 3541 kt (50.630 TJ) distributed over nine regions and organized in four BSRCs.

A MILP approach of optimal design of a sustainable combined dairy and biodiesel supply chain using dairy waste scum generated from dairy production

The present study proposes a mixed integer linear programming (MILP) approach for optimal design of a sustainable combined supply chain for the production of biodiesel using as a feedstock dairy waste scum generated from the implementation of optimal product portfolio of dairy supply chain. The latter includes models of economic and environmental impacts of the combined supply, constraints and an optimization criterion in terms of capital and operation costs. Тhe environmental impact is assessed through greenhouse gas emissions obtained through whole life cycle of the products. The proposed approach is implemented on a real case study from Bulgaria. As a result, optimal operating conditions of the considered supply chain, which includes: dairy farms portfolios, dairy plants portfolios, production capacity and location of biorefineries that should be built, performance of biorefineries and transportation routes are obtained. It can be used as a tool for decision-making.

A Grey-Fuzzy Programming Approach towards Socio-Economic Optimization of Second-Generation Biodiesel Supply Chains

This study aims to develop a multi-objective second-generation-based socially responsible supply chain (SGB-SRSC) network design model that considers all dimensions of sustainability: economy, environment, and social. The dynamic nature of the biodiesel supply chain (SC) impairs the SGB-SRSC model decisions; thus, a grey-fuzzy solution approach is developed. Biodiesel is a promising renewable energy resource produced from a variety of easily accessible domestic wastes. For a swift transition towards commercially feasible biodiesel production, integrated optimization of the biodiesel SC system is critical. Using the latest social impact assessment tools, this study provides a decision-support system for developing a biodiesel SC network. A comprehensive computational analysis is performed on a case study to validate the proposed model. The results show that significant investment is required to achieve social well-being goals and secure decisions against uncertainty associated with SGB-SRSC model parameters. Further, it is observed that the expenses of biodiesel production and biodiesel plant installation accounted for a large portion of the overall SC cost. As a result, finding more cost-effective biodiesel production methods is critical to the industry’s economic viability. Regulators and policymakers involved in biodiesel production projects may find the framework useful in obtaining a compromise solution for socio-economic goals based on their preferences.

A robust optimization approach for an integrated hybrid biodiesel and biomethane supply chain network design under uncertainty: case study

A robust optimization approach for an integrated hybrid biodiesel and biomethane supply chain network design under uncertainty: case study

A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network

Increasing energy demand and the fast depletion of fossil fuels have prompted the quest for sustainable energy sources. Biodiesel is a potential fossil fuel replacement that can be used in engines without modification. However, the commercial feasibility of biodiesel production is a major challenge. A resilient and cost-efficient biodiesel supply chain network is essential for commercialization. In addition, disruption risks arising from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with these requirements, an animal fat-based biodiesel supply chain model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production–distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. A novel interactive solution technique, robust possibilistic flexible programming, which enables decision-makers to incorporate flexibility into model constraints, has been introduced. Furthermore, a p-measure constraint that ensures the lowest cost under disruption scenarios is used to control network reliability. A real-world case study is used to assess the suggested model and solution technique's applicability. The findings demonstrate a tradeoff between system reliability and nominal cost, showing that with a marginal increase in overall cost, the decisions can be secured against an uncertain environment. Biodiesel producers and distributors, as well as investors and regulators, may potentially benefit from the proposed model.

Biodiesel Economic Evaluation and Biomass Planting Allocation Optimization in Global Supply Chain

During the past decade, the demand for biofuel has rapidly increased because of fossil fuel scarcity and pollution, which has resulted in the accelerating commercialization of the global biofuel markets with a potential 500 exajoules per year by 2050. This paper evaluates the economic benefits of five biomass types and proposes a stochastic programming model for the optimal design of the global biodiesel supply chain under the demand and price uncertainty. The biomass is produced in Southeast Asia and oil is extracted. Then, the vegetable oil is shipped to Europe and North America for biodiesel conversion and fulfilling the local demand. The results show that palm tree is a cost-effective clean energy with high benefits and low-carbon emissions. Based on the economic evaluation of biomass, the optimal capital investment, allocation of cultivated area, and transportation paths are identified to maximize the expected global profit in this supply chain in 2008–2012 by the proposed supply chain optimization model.

LINEAR PROGRAMMING MODEL FOR THE OPTIMIZATION OF THE BIODIESEL SUPPLY CHAIN IN THE MINDANAO ISLAND OF THE PHILIPPINES

Biofuels are known to have several advantages over fossil fuels including, but not limited to, high abundance of resources, negligible SOx emissions, lower NOx emissions, and more environment-friendly processes. In the Philippines, the biofuels industry is anchored onto the Biofuels Act of 2006 which mandates the use of biofuels made from indigenous sources such as coconut. Despite this, biodiesel is still less preferred by consumers over conventional fuel due to its high cost. This can be attributed to high production costs of biodiesel, wherein 18-28% is credited to transportation of products. This work proposes a linear programming model to reduce the overall cost of biodiesel by minimizing the transportation cost in the Philippine biodiesel supply chain, using the Mindanao cluster as case study. Multiple scenarios were done to gauge the impact of varying the supply allocation and biodiesel blend on the supply chain. The optimal supply allocation, transportation cost, and carbon footprint of each scenario were determined. A new facility locator feature was also added as an extension of the program. Results also showed high reproducibility using easily accessible programming tools such as Microsoft Excel and Python.

Integrated Strategic and Tactical Planning of non-Edible Biomass-to-Biofuel Supply Chains

Iran has high potential of renewable resources and biomass is one of the most important of them. While the biomass is fairly cheap, the logistic costs are major obstacles in utilizing these sources. In this paper, a mathematical programming model (a MILP model) is presented to design the biodiesel supply chain network (BSCN) in Iran. A supply chain model is presented that is in accordance with the real conditions and geographical features of the case study. Due to, importing most of edible oils in Iran and their high production costs, non-edible biomass including Jatropha curcas L. (JCL) and waste cooking oil (WCO) are considered as sources of producing biodiesel. In addition, in this study, the possibility of capacity expansion of facilities in different periods is considered. The presented approach is used to design a three-echelon, multi-product, multi-period BSCN. This model determines the strategic and tactical decisions including location, number, capacity of the facilities, production, inventory and material flow throughout the network. The result of this paper shows the effectiveness of the presented approach for designing the BSCN.

SUPPLY CHAIN OF JATROPHA CURCAS BIODIESEL INDUSTRY BASED ON LOCAL PRODUCTION AND CONSUMPTION IN CUBA

The Cuban efforts for the founding of a local biodiesel industry are totally focused on Jatropha based, as the main source of bioenergy for transportation in rural areas. There is no a national policy in this respect and the producers and users are dealing with the use of a blend of 15% of biodiesel of Jatropha curcas and 85% diesel fuel. This is a major challenge for the farmers and municipalities, due to risk and uncertainty in its production, profitability and land disposal, among others. The present study investigates the supply chain network and related problems of the biodiesel industry in Cuba. The Jatropha based biodiesel supply chain includes stages like seeds production, feedstock logistics such as harvesting, storage, and transportation, biodiesel production, distribution and final use in diesel engines. The critical factor is the seed production at the level needed for a stable supply of oil to the biodiesel plant. Consumables that are imported are another risk factor in the Jatropha value chain in Cuban local production. The settling of a local bioenergy market is another task for the Jatropha industry but also for the by-products generated (glycerol, Jatropha cake and husk) are also crucial for the sustainability of the industry.

Optimal Design of Sustainable Biodiesel Supply Chain Using Dairy Waste Scum as a Feedstock Generated from Dairy Supply Chain

The rapid depletion of fossil fuels and the increased environmental impact of their combustion make it necessary to find cleaner and more sustainable energy resources. Over the last decades, biodiesel has been introduced as an alternative because of its advantages over fossil fuels. However, the high production cost of biodiesel is one of the main obstacles to achieving its commercial viability. One way to improve the efficiency and sustainability of this process is to use Dairy Waste Scum (DWS), which is a waste product from the dairy industry as a feedstock. Selection of the feedstock used is only a part of the strategy to increase the sustainability of this type of production. The most effective way to achieve this is by optimising the activities across the supply chains (SCs). In addition, the sustainability of the biodiesel production process using DWS may be influenced by design of optimal product portfolio of the considered dairy SC. This study proposes an approach for optimal design of a sustainable combined dairy and biodiesel/diesel SC using dairy waste scum as a feedstock, generated from dairy SC. It is based on defining mixed integer linear programming (MILP) model of the optimal design of the considered combined SC. The latter includes economic and environmental assessments. The first one is defined as an optimization criterion while the second one is defined as a constraint. The model takes into account key SC activities such as infrastructure compatibility; the production of the dairy products; milk, dairy products and DWS transportation between the regions, carbon taxes, related with all SC activities. The environmental and economic performance of the combined dairy and biodiesel/diesel SC is assessed by the annual operating costs for the combined SC design and greenhouse gas (GHG) emissions of pollutants associated with its operation. The developed approach was implemented on a real case study from Bulgaria. The analysis of the environmental results shown that the total GHG emissions generated by the operation of the SC for all time intervals are mainly due to the production of the products in dairies and utilization of unused DWS for production of biodiesel.

Incorporating Environmental Perspective in Integrated Strategic-Tactical Economic Optimization Model of Biomass-to-Biofuel Supply Chain—A Real Case Study in Ethiopia

Several optimization models, which consider economic and environmental perspectives, have been developed recently to support the sustainable biomass-to-biofuel supply chain (BBSC) design. All of the economic-environmental optimization models rely on solving long-term planning problems with a conventional hierarchical approach, where tactical decisions are made based on the optimal strategic decisions from the strategic-level model, despite it arousing non-optimal solutions. Moreover, almost all of them have used non-monetary-based environmental indicators, which result in difficulties with clarity when comparing with economic objectives. Therefore, in this work, an effort is made to develop a more reliable planning strategy that offers optimal strategic and tactical decisions simultaneously and maximizes the economic and environmental benefits. Furthermore, the environmental performance of the BBSC has been assessed in terms of monetary value by adopting an ecocost approach after performing an LCA on the system. The integrated model is applied in the real biofuel sector of Ethiopia to optimize the country’s bioethanol and biodiesel supply chain over a 20-year horizon. Despite the abrupt rise in the model size, with it being a real countrywide case with many variables and large quantities of data, an alternative semi-heuristic method that offers a feasible solution to the multi-objective problem is provided.

Organic waste derived biodiesel supply chain network: Deterministic multi-period planning model

In this study, a multi-period model of an organic waste-to-biodiesel phased supply chain network is developed to strategically consider variations in the biodiesel demand and the organic waste usage over a long-term planning interval. The proposed model can facilitate planning to determine the biorefinery location and the amount of biodiesel to produce, transport, or utilize to reduce the total cost of the organic waste-to-biodiesel supply chain network design while meeting the biodiesel demand during each period of the planning interval. The features and capabilities of the model are validated by application of a future organic waste-to-biodiesel supply chain network design in South Korea. The optimization results illustrate that the average total annual cost of the organic waste of biodiesel based on the multi-period model will be $US 4.2 billion per year during the period 2020–2030. The minimum selling prices for the optimal network design were determined ranged from $US 3.154–3.156/gallon of biodiesel containing 3% butanol derived from organic waste combined with diesel. A small portion (4.9–6.6%) of the total biodiesel demand was produced from organic waste because of low organic waste availability in the existing anaerobic digestion facilities, leading to increasing the outsourcing costs of butanol. The proposed approach will facilitate the strategic development of a sustainable supply chain network for organic waste recycling for material and energy valorization.

Economic design framework of microalga-based biodiesel supply chains under uncertainties in CO2 emission and diesel demand

To determine the optimal configuration of supply chain network for microalga-based biodiesel (MADSCN) considering uncertain values which influence the optimal strategies of microalga-based biodiesel production, we develop a two-stage stochastic model that considers the CO2 supply and biodiesel demand as uncertain parameters, which simultaneously influence the potential locations for the construction of carbon-capture-and-storage systems to supply CO2 as feedstock, and of biorefineries to produce and distribute the biodiesel as to regions in which it is in demand. The proposed framework is formulated using mixed-integer linear programming to identify the optimal MADSCN configuration that minimizes the overall cost of microalga-based biodiesel production. Variation of both uncertain factors in the stochastic model affect the configurations and operating strategies of MADSCN compared to the deterministic model that fixes the factors. The optimal superstructure of MADSCN was affected more significantly by uncertain biodiesel demand than by uncertain CO2 emission.

Stochastic Programming of Sustainable Waste Cooking Oil for Biodiesel Supply Chain under Uncertainty

As an important emission reduction source for the transportation industry, biofuel has received strong support from the Chinese government. However, the development of the biofuel industry is still struggling. The high degree of uncertainty makes the development of the industry face huge challenges. Kitchen waste, as a biodiesel raw material with a large yield, has good development prospects. Reuse of kitchen waste can solve public health and safety problems. This paper proposes a two-stage stochastic programming model under supply disturbance to optimize the supply chain from the perspective of contract. Then current three main flow directions of kitchen waste are analysed and the reasonable price for biodiesel operators to purchase is determined. By signing contracts with the biodiesel operators, restaurant is guaranteed and encouraged to provide a certain percentage of kitchen waste to meet the demand for biodiesel production. Using actual case in the Yangtze River Delta region, the performance of the stochastic programming model under disturbance was compared. Through the sensitivity analysis of different parameters, this paper determines the influence of its supply chain network design and expected total system cost. Through the optimization of the waste cooking oil (WCO) for biodiesel supply chain, this paper can effectively improve the efficiency of the supply chain, reduce system costs, increase the profits of biofuel operators, and promote the sustainable development of the biofuel industry.

A two-stage multi-objective second generation biodiesel supply chain design considering social sustainability: A case study

Undesirable environmental impacts caused by fossil fuel consumption have motivated governments to focus on biofuels production. Therefore, in our paper, we design a multi-objective biodiesel supply chain from Jatropha, which includes biomass cultivation center, pretreatment, and oil extraction center, biorefinery, and demand zone. In our study, a two-stage approach presented, which first specifies candidate locations for biomass cultivation with a common weight data envelopment analysis (CWDEA) method and then strategic and tactical decisions were made by a mathematical model. The presented multi-objective model focuses on cost minimization, besides social benefits and environmental impacts maximization while a life cycle assessment approach is used to model social indicators. The capability of the model is validated by proposing a case study of Iran and the augmented ε-constraint method is employed to reach a trade-off between three pillars of sustainability while managerial insights are provided by employing a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for the optimal solution selection. Model's results and sensitivity analysis on key parameters, specify the best locations for Jatropha Curcas L. (JCL) cultivation and facility construction as well as an optimal value of supply chain variables, which finally proves that Iran has a high potential for biodiesel production from JCL that leads improving balanced socio-economic development in this country.

Organic-waste-derived butyric acid-to-biodiesel supply-chain network: Strategic planning design using a deterministic snapshot model

An integrated optimization model for an organic-waste-derived butyric acid-to-butanol supply-chain network (BABSCN) is proposed to minimize the total network cost by simultaneously optimizing both strategic biodiesel production and waste management planning decisions. This model is useful for ensuring effective organic-waste provision for large-scale biodiesel production and waste management. The proposed mixed-integer linear-programming model optimizes the activities ranging from organic-waste preprocessing to butyric acid (BA), transportation of BA to biorefinery, butanol (BuOH) production and mixing with diesel to the distribution of biodiesel. This model is useful for forecasting organic-waste management biodiesel supply chains in South Korea in 2030. The case study results show that a total network cost of $US 3.16/gallon of B3 contains 3% BuOH from organic waste products combined with diesel. The biorefinery-related cost accounts for 98.3% of the total network cost, followed by the organic waste procurement cost (1.1%) and biodiesel distribution cost (0.6%). A scenario-based analysis shows that a 7%-BuOH increase in biodiesel increases the total network cost by 18.8%.

A facility location problem for palm-oil-based biodiesel plant: case study in North Sumatera Province

As a country that supports the development of renewable energy, Indonesia has policies to direct the use of biodiesel as fuel energy source. In 2020, the Government of Indonesia (GoI) implements mandate of biodiesel from palm oil, one of Indonesia’s best plantation products. The increase in the mandate up to B30 makes the national biodiesel plant’s capacity to operate up to 80% so that a new plant is needed to meet biodiesel for national and export demand. Taking North Sumatra province as the location of study, this research expects to determine the biodiesel plant’s location that will be built to meet the needs of petrodiesel fuel in the North Sumatra area to minimize transportation costs at each stage. Mathematical modelling is conducted to reach the objective. The model aims to determine the most appropriate location and size of the biodiesel plant to be made, by taking into account the distance between several two-transportation points, the availability of feedstocks, demand, and supply chain costs. Mixed-integer linear programming (MILP) is employed to simulate the model. Result suggests that two regions are suitable locations for establishing biodiesel factories to meet the needs of the people of North Sumatra, namely Langkat Regency and Serdang Bedagai Regency. The result can serve as recommendations for policymakers to minimize the biodiesel supply chain cost in North Sumatra.

Optimization Design of a Supply Chain for Jatropha-Based Biofuel from a Sustainable Development Perspective Considering International Resources and Demand: A Case Study

In this paper, a multiperiod, multiobjective, and multiproduct second-generation biodiesel supply chain network is innovated, taking into account sustainable development and international resources and demand. The proposed model maximizes the total profit and positive social issues and minimizes the harmful environmental consequences of the network. Furthermore, as trade is nowadays considered to be an important issue on an international scale, economic globalization is also considered in the present study by expanding the operational activities of supplying feedstocks and selling final products worldwide. The model is validated through real data collected within Iran and some Asian countries and solved by the interactive fuzzy programming method. The experimental results verify the efficiency of the model for countries that have the capacity to produce inedible-based biofuel. As a result, the highest share of the revenues is earmarked for international biodiesel sales (80%), which indicates that globalization can increase the profit to a great extent.

Multiobjective Optimization of Sustainable WCO for Biodiesel Supply Chain Network Design

Bioenergy is attracting more attention worldwide due to its environmental and economic benefits. The design of a feasible biodiesel supply chain network can effectively improve the production and use of biodiesel and then further promote the development of the biodiesel industry. As an easy recyclable material with high yield, kitchen waste has a good prospect and can solve public health and safety problems. This paper takes the kitchen waste producing biodiesel as the object to design and optimize the biodiesel supply chain in order to improve the sustainable development of biodiesel industry and the operational efficiency of the biodiesel supply chain. By designing a sustainable biodiesel supply chain model under defined conditions, it proposes strategic and tactical decisions related to location, production, inventory, and distribution within multiple planning cycles. In order to effectively solve the model, a Pareto optimal NSGAII heuristic algorithm is proposed and applied to a practical case study of restaurants in Jiangsu Province. The efficiency of the method and the optimal solution are verified by a case study. The overall optimization of biodiesel supply can effectively improve the efficiency of supply chain, reduce system cost, improve the profit of biodiesel operators, and promote the sustainable development of biodiesel industry, which has important guiding significance and reference value for the practice of biodiesel supply chain network planning.

Microalgae to biodiesel - Review of recent progress

Microalgae with potentially high lipid yields of certain strains and relatively small environmental footprints offer a medium to long term biodiesel supply. Substantial challenges need to be resolved before they can be developed commercially on a large scale. There have been some significant technological breakthroughs made in recent years that offer the potential to achieve this. The ability to genetically engineer algal strains more efficiently and precisely to stabilize their growth and improve lipid yield is one approach. Others include one-step harvesting and lipid extraction from wet biomass, filtering incident light to focus on red photons, combining solar photovoltaics with high-rate algal ponds, and using wastewater and effluent to provide the ideal nutrient mix to promote algal growth and lipid yield. Additionally, microalgal extracts offer some value-added, high-margin co-products, such as poly-unsaturated fatty acids and nutrient-rich biofertilizers. Multi-product integrated biorefineries embracing offer the best route to large-scale, eco-friendly biodiesel commercialization.