Biofuel Supply Research Articles

Analysing the impact of stakeholder relationships in the optimisation of biomass supply chains

Many policies have been rolled out to help biomass supply chains reach their greatest contribution to combat climate issues. However, policies are often undermined by the failure to capture the impact of policy on interactions among players in the supply chain. When players in the supply chain have different perspectives toward a given policy, it impacts the way they interact with one another. This was not considered in the existing literature. A conflict analysis approach is applied to evaluate the impact of the supply chain player's relationship on supply chain decisions. Conflict analysis can be incorporated to mathematically model the behaviours of each supply chain player towards specific proposals or policies. To illustrate the effectiveness of the proposed conflict analysis-embedded model, a case study on a biofuel supply chain based in Malaysia is analysed. In this study, players' reactions in the supply chain toward the three proposed government policies (transport subsidy, workforce reduction and production variation) are systematically modelled and used as an input for the supply chain optimisation. A comparison study is shown to benchmark the results obtained with and without conflict analysis. Based on the optimised results, the conflict analysis model called for a higher amount of empty fruit bunch (i.e., 2.7 times higher between empty fruit brunch sources and first layer storage) than the model without conflict analysis. The total cost associated with the supply chain also increased by 77% after relationships among players were considered (USD 4.5 million to USD 8.0 million). The higher cost and material from the model suggested that conflict reduces the effectiveness of material transfer by reducing the chances of successful trade within the supply chain. By foreseeing and considering the inefficiency of a supply chain in a model, policymakers can, therefore, possible to make more sound decisions. The proposed approach with these findings is crucial in notifying the policymakers of the true potential of their policy toward the supply chain.

An inclusive trend study of techno-economic analysis of biofuel supply chains

Biofuels have gained much attention as a potentially sustainable alternative to fossil fuels to tackle climate change and energy scarcity. Hence, the increasing global interest in contributing to the biofuel supply chain (BSC), from biomass feedstock to biofuel production, has led to a huge amount of scientific production in recent years. In this vein, techno-economic analysis (TEA) of biofuel production to estimate total costs and revenues is highly important for transitioning towards a bioeconomy. This research aims to provide a comprehensive image of the body of knowledge in TEA evolution within the BSC domain. To this end, a systematic science mapping analysis, supported by a bibliometric analysis, is carried out on 1104 articles from 1986 to 2021. As a result, performance indicators of the scientific production within the target literature are presented to explain how this literature has evolved. Besides, thematic trends and conceptual structures of TEA of biofuel production are discovered. The results show that (i) biofuel production and consumption need promotion through tax measures and price subsidies, (ii) the development of cost-competitive algal biofuels has faced many challenges over recent years, and (iii) TEA of algal biofuels to identify commercial improvements and increase the economic feasibility is still lacking, which calls for more in-depth investigations. Consequently, current challenges and future perspectives of TEA in the BSC domain are rendered. The provided insights enable researchers and decision-makers involved in BSCs to (i) capture the most influential contributors to the field and (ii) identify major research hotspots and potential directions for further development.

A game-theoretic approach for power pricing in a resilient supply chain considering a dual channel biorefining structure and the hybrid power plant

This study investigates power pricing decisions in a resilient supply chain, including the main biomass supplier, biorefinery, the hybrid power plant, and the backup supplier. The power plant's main fuel is considered biofuel derived from Jatropha Curcas L. (JCL). However, due to unavoidable uncertainties in biofuel supply links, the shortage in meeting demand is inevitable. Therefore, for increasing the supply chain's resiliency, the fossil fuel backup supplier is taken into account to make up the shortage with anticipated fossil fuel. Due to environmental concerns, the penalty cost is considered for excessive use of fossil fuel. The model is studied under two cases by considering a dual-channel biorefining. Different game-theoretic models are established for varied power configurations and interactions of supply chain members. It is observed from the numerical study that, depending on the fractional part of the power plant's requirements of biofuel and JCL supplied by the biorefinery (Case 1) and the main supplier (Case 2), respectively the performance of the supply chain increases in the presence of the backup supplier. Additionally, the sensitivity analysis shows that Case 1 is more profitable than Case 2 for chain members. Moreover, in contrast to the fossil fuel replacement coefficient, the greater the convertibility degree of the JCL, the larger the profits for the members.

Optimization of Sustainable-Robust Biofuel Supply Chain under Uncertainty

Optimization of Sustainable-Robust Biofuel Supply Chain under Uncertainty

Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties

The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply–demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply–demand, policy requirement constraints, and the financial minimization objective. The results indicate that biofuel allocation and expansion schemes are sensitive to the multiple and compound uncertainty inputs, and the corresponding biofuel-deficit change trends of three heat sources are obviously distinct with the system’s condition, varying due to the complicated interactions of the system’s components. Beyond that, a potential trade-off relationship between the heating cost and the constraint-violation risk can be obtained by observing system responses with thermalization coefficient varying.

Marine Biofuels Costs and Emissions Study for the European Supply Chain Till 2030

The design and preliminary estimations of biomass supply chains are essential in matching energy supply to energy demand. This is especially true of novel/future fuels and technologies in large industries. In this paper, a Mixed Integer Linear Programming (MILP) model was formulated to represent biofuel supply chains across Europe for the production of three novel marine fuels and to allow the selection of fuel conversion technologies, biomass supply locations, and the logistics of transportation from resources to conversion and from conversion to final markets. On top of this, the total production costs and emissions were calculated and compared to current marine fuels to assess the implementation potential and feasibility of these fuels. The MILP model was used to design and analyze optimal distribution and conversion systems, using a realistic data-set covering the European member states and 15 of the largest bunkering ports in the EU. The results showed that on average, the fuels obtained a 72% greenhouse gas (GHG) reduction compared to a fossil fuel comparator and ranged from 22–36 €/GJ in total production costs. It was also discovered that forestry residues were the best-suited biomass for the production of these fuels and that Poland had the highest supply potential of all considered states. The available supply of biomass was sufficient for the demand in the foreseeable future, the largest impediment to the adoption of these fuels is the available refining potential in Europe.

A biofuel supply chain design considering sustainability, uncertainty, and international suppliers and markets

A biofuel supply chain design considering sustainability, uncertainty, and international suppliers and markets

Optimizing the Design of a Biomass-to-Biofuel Supply Chain Network Using a Decentralized Processing Approach

When designing biomass-to-biofuel supply chains, the biomass uncertainty, seasonality and geographical dispersion that affect economic viability need to be considered. This work presents a novel methodology that can optimize the design of biofuel supply chains by adopting a decentralized network structure consisting of a mix of fixed and mobile processing facilities. The model considers a variable biomass yield profile and the mobile fast pyrolysis technology. The mixed-integer linear programming model developed identifies the optimal biofuel production and biomass harvesting schedule schemes under the objective of profit maximization. It was applied in the case study of marginal lands in Scotland, which are assumed to be planted with Miscanthus. The trade-offs observed between economies of scale against the transportation costs, the effect of the relocation costs and the contribution of storage capacity were investigated. The results showed that, in most cases, harvesting is most concentrated during the month of the highest biomass yield, provided that storage facilities are available. Storage capacity plays an important role to widen the operational time window of processing facilities since scenarios with restricted or costly storage resulted in facilities of higher capacity operating within a narrower time window, leading to higher investment costs. Relocation costs proved to have a minor share in the total transportation costs.

Life cycle evaluation of microalgae based biorefinery supply network: A sustainable multi-criteria approach

Due to enjoying such features as ability to produce high volumes of oil and reducing and eliminating environment carbon dioxide, microalgae is a promising solution for producing biofuels. In order to help the commercialization of this emerging industry, the present study offers a sustainable mixed integer linear programming model (MILP) for designing and planning microalgae biofuel production and distribution network. As objective functions, this model considers minimizing the total cost of the network and maximizing greenhouse gas emission saving simultaneously. In order to design microalgae biofuel sustainable production and distribution network, a two-stage approach is developed. In order to reduce computational complexity, at first place, data envelopment analysis approach (DEA) and Combinative Distance-Based Assessment (CODAS) approach as well as best worst method (BWM) are developed respectively for determining the location of suitable candidates for establishing microalgae production facilities and constructing biorefinery based on economic, social and environmental criteria. In the second stage, the environmental effects of microalgae biofuel supply chain network are measured using one of the newest and most effective methods that is based upon environmental life cycle assessment as well as OpenLCA software and then followed by formulizing a bi-objective linear programming model. For solving the offered model, augmented epsilon constraint method is used. Finally, a realistic case study conducted at national level concerning microalgae biofuel supply chain. The results indicate that based on the offered model by replacing fossil fuels with biofuel we can achieve greenhouse gases emission saving by 99 %.

The imbalance of food and biofuel markets amid Ukraine-Russia crisis: A systems thinking perspective

The Ukraine war has immensely affected both food and energy systems due to the significant role of Russia in supplying natural gas and fertilizers globally and the extensive contribution of both Russia and Ukraine in exporting grains and oilseeds to the international markets. Hence, the Ukraine-Russia conflict has resulted in a shortage of crops and grains in the food market, especially in Europe, causing speculations if these resources should still be used for biofuel production (1st Generation). However, the International Energy Agency has warned that lowering biofuel mandates could result in rising petroleum demand and supply concerns. In light of these unfolding events, a systems thinking approach is required to monitor and analyze the implications of this crisis for food and biofuel markets as a whole to alleviate the concerns faced and plan sustainably. In this vein, based on the trade-offs between food system elements and the biofuel supply chain, as well as the potential effects of the war on the food and energy systems worldwide, a causal loop diagram is developed in the present work. According to the insights provided, the key to preventing food insecurity and keeping biofuel mandates on an increasing trend simultaneously amid the Ukraine war is to switch from the 1st Generation biofuels to higher generations. This transition would reduce not only the pressure on the food market to move toward zero hunger (SDG 2) but also pave the way to move towards a circular economy and clean and affordable energy (SDG 7) during the post-war era.

Bi-objective stochastic model for the design of biofuel supply chains incorporating risk

The design of a biofuel supply chain network involves multiple uncertainty sources such as the biomass supply and quality. Most of the previous works took a risk-neutral approach by modeling this problem as two-stage stochastic formulation. However, most decision makers are not risk-neutral and a better understanding of the risk involved is germane. This study proposes a bi-objective two-stage stochastic optimization model that considers the expected total cost and the CVaR as objective functions. The first stage determines the location of biorefineries, and the second stage decides the flow between suppliers (counties) and biorefineries. We assume as non deterministic two features of switchgrass (biomass), the ash and moisture contents. To solve this problem, a hybrid method based on a Simulated Annealing algorithm and the augmented ε-constraint method is developed and tested using a realistic large-scale case study in the state of Texas. Based on the experimental results, we observe that the CVaR is not a suitable risk measure when the problem involves a third party because the cost of third-party does not imply risk for the decision makers (biorefinery owner). Therefore, the PVaR model is more suitable for the nature of this problem since PVaR includes the risk inherent to the third party in case this entity is not able to fulfill the demand of biofuel not covered by the supply chain network. As a result, we reformulate the original model by replacing the CVaR by PVaR to create a tailor-made formulation for the bi-objective two-stage stochastic programming problem including third party biofuel suppliers. Compared to existing solution procedures, the proposed hybrid method has merits in terms of computational burden and solution quality. Computational experiments show that the lower the confidence level in the third party (that is, a less reliable supplier needed to meet the bioethanol demand), the higher the expected costs and the PVaR (risk) for the supply chain design.

Economic optimization for a dual‐feedstock lignocellulosic‐based sustainable biofuel supply chain considering greenhouse gas emission and soil carbon stock

AbstractEnvironmental factors, including greenhouse gas (GHG) emissions and soil organic carbon (SOC), should be considered when building a sustainable biofuel supply chain. This work developed a three‐step optimization approach integrating a geographical information system‐based mixed‐integer linear programming model to economically optimize the biofuel supply chain on the premise of meeting certain GHG emission criteria. The biomass supply grid cell was considered first, based on a maximum level of GHG emissions, prior to economic optimization. The optimization simultaneously considered dual‐feedstock sourcing, selection between distributed and centralized configurations, and the impact of maintaining SOC balance in agricultural soil on biomass availability. The applicability of the modeling approach was demonstrated through a case study that optimized a dual‐feedstock renewable jet fuel supply chain via a gasification‐Fischer–Tropsch (gasification‐FT) conversion pathway in 2050 under three biomass availability scenarios. The case study results show that the differences in procurement costs and GHG emissions between energy crops and agricultural residues have a large impact on the layout of the supply chain. The supply‐chain configuration tends to be more centralized with large‐scale biorefineries when a supply region has an intensive and centralized distribution of biomass resources. The cost‐supply curves demonstrated the technical potential of biofuels that could be obtained at a certain level of cost. Additionally, sensitivity analysis shows that the GHG emission credit from producing extra electricity during the gasification‐FT process will be significantly reduced with a rising share of renewable electricity generation in the future. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

Bi-criteria optimization of cleaner biofuel supply chain model by novel fuzzy goal programming technique

In engineering, the biofuel supply chain (BSC) is a significant supply chain optimization challenge. Here, the multi-objective optimization problem for biofuel distribution is based on the life cycle energy consumption (LCEC) and the emission of CO2 involve in supply network of a biofuel. It includes numerous feedstocks, means of transportation, and locations for biofuel plant construction, as well as multiple technologies, the number of feedstocks in the agricultural system, transportation capabilities, crop yields, and market needs. To provide a modelling framework for optimization issues with multi-fold uncertainty, different reduction approaches to turn a Type-2 Fuzzy variable into a Type-1 Fuzzy variable through the expected value method are described. The deterministic BSC model is then solved utilizing Goal Programming and Weighted Sum Method using Generalized Reduced Gradient Technique. The goal BSC models are (i) Minimization of life cycle energy consumption (ii) total emission of CO2 in uncertain environment. The numerical experiments demonstrated with different optimistic labels by which managers can control their objects by choosing optimistic values. Here, we have developed a systematic optimization approaches for the advancement of biofuel transportation, supply and process chain analysis, sustainability, and industrial optimization. Finally, a real-world case study is used to demonstrate the applicability of the suggested approach. The findings show that the proposed solutions are successful in reducing logistical expenses.

In vitro plant tissue culture as the fifth generation of bioenergy

Developing and applying a novel and sustainable energy crop is essential to reach an efficient and economically feasible technology for bioenergy production. In this study, plant tissue culture, also referred to as in vitro culture, is introduced as one of the most promising and environmentally friendly methods for the sustainable supply of biofuels. The current study investigates the potential of in vitro -grown industrial hemp calli obtained from leaf, root, and stem explants as a new generation of energy crop. For this purpose, the in vitro grown explants were first fully characterized in terms of elemental and chemical composition. Secondly, HTL experiments were designed by Design Expert 11 with a particular focus on biocrude. Finally, the chemical components, functional groups, and petroleum-like hydrocarbons present in the biocrude were identified by PY-GCMS. A 22.61 wt.% biocrude was produced for the sample grown through callogenesis of the leaf (CL). The obtained biocrude for CL consisted of 19.55% acids, 0.42% N compounds, 15.44% ketones, 16.03% aldehydes, 2.21% furans, 20.01% aromatics, 5.2% alcohols, and 19.88% hydrocarbons. To the best of the authors’ knowledge, this is the first report that in vitro -grown biomass is hydrothermally liquefied toward biocrude production; the current work paves the way for integrating plant tissue culture and thermochemical processes for the generation of biofuels and value-added chemicals.

RETRACTED ARTICLE: A multi-objective mixed integer linear programming model proposed to optimize a supply chain network for microalgae-based biofuels and co-products: a case study in Iran.

Recently, with efficient methods of cultivating microalgae at hand, researchers and investors have paid considerable attention to the production of different environmentally friendly biofuel products. In this study, a two-stage deterministic model is proposed to design a microalgae-based biofuel and co-product supply chain network (MBCSCN). In the first stage, appropriate locations to cultivate microalgae are identified through the analytical hierarchy process (AHP). In the second stage, a deterministic mathematical mixed integer linear programming (MILP) model is developed for a period of 5 years based on economic and environmental impacts as two criteria. The economic objective function maximizes the overall profit, while the objective function of the environmental impacts seeks to minimize fossil fuel consumption throughout the supply chain. Then, a multi-objective MILP optimization problem is solved using the ε-constraint method. The proposed model is evaluated through a case study in Iran. It has helped to identify appropriate locations for the cultivation of microalgae and to specify the required quantity of feedstock, the species of microalgae, the required technology, and the transportation modes in each step of the supply chain.

Public policies versus market factors: What drives ethanol expansion in Brazil?

Abstract Concerns about energy security, rural development, and the environment have driven biofuel expansion. Different policy and market drivers, such as designated areas for biofuel feedstock production and blend mandates, have played an important role in biofuel expansion in different countries. Understanding how policy and market drivers influence biofuel supply and demand can provide guidelines for countries seeking to expand biofuel production into new regions. The case of Brazil serves as an important example. In just over 10 years, the Cerrado region of Brazil, specifically the states of Goias and Mato Grosso do Sul, experienced a six-fold expansion in ethanol production, becoming the second largest production region in Brazil. The purpose of this study is to examine the influence of policies and market drivers on ethanol expansion in the Cerrado region using a market equilibrium modeling approach. Results elucidate the role that market and policy drivers, including areas designated for sugarcane production, blend mandates, and energy taxes, play in influencing ethanol expansion into an already intensive agricultural region of the country. The findings from this study have implications for sustainable biofuel production in other regions of the world.

Global evaluation of economics of microalgae-based biofuel supply chain using GIS-based framework

Global evaluation of economics of microalgae-based biofuel supply chain using GIS-based framework

Biofuel supply chain management in the circular economy transition: An inclusive knowledge map of the field

Investment in biofuels, as sustainable alternatives for fossil fuels, has gained momentum over the last decade due to the global environmental and health concerns regarding fossil fuel consumption. Hence, effective management of biofuel supply chain (BSC) components, including biomass feedstock production, biomass logistics, biofuel production in biorefineries, and biofuel distribution to consumers, is crucial in transitioning towards a low-carbon and circular economy (CE). The present study aims to render an inclusive knowledge map of the BSC-related scientific production. In this vein, a systematic review, supported by a keywords co-occurrence analysis and qualitative content analysis, was carried out on a total of 1,975 peer-reviewed journal articles in the target literature. The analysis revealed four major research hotspots in the BSC literature, namely (1) biomass-to-biofuel supply chain design and planning, (2) environmental impacts of biofuel production, (3) biomass to bioenergy, and (4) techno-economic analysis of biofuel production. Besides, the findings showed that the following subject areas of research in the BSC research community have recently attracted more attention: (i) global warming and climate change mitigation, (ii) development of the third-generation biofuels produced from algal biomass, which has recently gained momentum in the CE debate, and (iii) government incentives, pricing, and subsidizing policies. The provided insights shed light on the understanding of researchers, stakeholders, and policy-makers involved in the sustainable energy sector by outlining the main research backgrounds, developments, and tendencies within the BSC arena. Looking at the provided knowledge map, potential research directions in BSCs towards implementing the CE model, including (i) integrative policy convergence at macro, meso, and micro levels, and (ii) industrializing algae-based biofuel production towards the CE transition, were proposed.

Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty

This paper proposes an integrated stochastic mixed-integer linear programming model for biofuel supply chain and landscape design optimization that considers the interactions between uncertainty in biomass yield, spatially explicit feedstock availability, supply chain configuration, operational decisions, and the system's environmental impact. By modeling crop establishment and fertilization as strategic decisions made before the realization of uncertainty, model solutions identify integrated supply chain configurations better suited to mitigate uncertain biomass yields. Importantly, the paper presents an approach for gathering and processing the large amount of necessary real-world data, including an accurate accounting of soil carbon sequestration. A case study located in Michigan, USA, demonstrating the capabilities of the integrated model with realistic data, is presented. Results at a variety of harvesting site resolutions and number of uncertainty scenarios, show that large-scale instances, at fine spatial resolutions, identify attractive environmental solutions and that solving the stochastic problem leads to an economic benefit.

Insight into Nanoparticle-Number-Derived Characteristics of Precharged Biodiesel Exhaust Gas in Nonthermal Plasma State.

The utilization of biodiesel as an alternative partial replacement of diesel fuel was shown to improve exhaust emissions from diesel engines. Waste cooking oil biodiesel (WCO) has also gained more attention due to edible biofuel supply and the environment. In this study, a nonthermal plasma (NTP) technique was applied to be equipped into the after-treatment system of a four-cylinder diesel engine at medium- and high-load conditions. The exhaust gases in the NTP state from the combustion of WCO and diesel (D100) fuels were partially drawn by spectrometers and nanoparticle-number-derived characteristics were analyzed. The particle number, area, and mass concentrations were in log-normal distribution over equivalent diameters, and they were higher at high load. The concentration of the particulate matter (PM) was lower but was larger in size when the NTP charger was activated due to coagulation principally owing to WCO’s number and surface area. The total particle masses were lower for WCO at the two load conditions tested. During NTP charger activation, the mass mean diameters were increased by maximum values of 24.0% for D100 and 5.5% for WCO. The PM removal efficiencies were maximized by 10.8% for D100 and 16.7% for WCO when the NTP charger was in use, and the WCO exhaust was dominantly seen to simultaneously reduce NOx and PM emissions.