Bioenergy Supply Chain Research Articles

Modelling the productivity and economic feasibility for bioenergy production in a Mediterranean oak coppice

The possibility of further exploiting the national bioenergy supply chain in Italy is hindered by a lack of economically sustainable mechanization. Large enterprises can rely on brand-new and advanced machinery to improve productivity and maximize revenue. On the other hand, small enterprises often resort to custom-built machinery to accomplish the same tasks. The performance of an excavator equipped with forest shears was used to model its productivity and economic feasibility in an oak coppice in central Italy. The oak coppice harvesting operations required 29, 19, 22, 20 and 10% of total time respectively for clearing, moving, felling, bunching and delay. The linear model highlighted a strong influence of diameter at breast height (DBH) on the total productivity, with a p value lower than 0.001 and an adjusted R2 of 0.64. Felling and bunching operation costs ranged from 10.89 to 31.45 EUR t-1, the latter one corresponding to a DBH value of 9 cm. Hence, our findings indicate the minimum stand requirements necessary to ensure the economic sustainability of excavator-based harvesting in these specific conditions.

Game theoretic approach for the synthesis and optimization of economically optimal coalitions in integrated renewable energy and polygeneration networks

This study presents the generation of an integrated resource network that encompasses a bioenergy supply chain and a polygeneration hub. The resource network is designed using a superstructure that comprises 3 layers. The first layer is a supply chain network of seasonally available renewable and non-renewable energy sources and is connected to the second layer by a transport system comprised of rail, road, and pipeline. The second layer, which is the polygeneration hub, comprises a boiler to produce high-pressure steam, steam turbines for generating power, a multi-effect evaporation system for desalinating salt-water and an absorption refrigeration system to produce chilled water. The option of connecting the boiler to a solar-thermal and heat storage subnetwork for feedwater pre-heating is included in the second layer. Piping and electrical cables connect the polygeneration layer to the third layer, which comprises industrial heat demand, represented by heat exchanger network, and electrical power demand. The model is applied to a case study in which the subnetworks in the overall integrated superstructure layers are considered as individual players in an industrial symbioses network with the possibility to be in a cooperative game. The objective function of the model, which is represented as a mixed integer nonlinear programming model, involves the minimization of the summation of the players' marginal contribution, with equal weighting applied for each player, to the coalition's total annual cost. The result obtained involves preference for a coalition among participating subnetworks that include combined heat and power, multi-effect evaporation, and heat exchanger network.

Optimizing Bio-energy Supply Chain to Achieve Alternative Energy Targets

In response to global warming and the dwindling reservoirs of fossil fuels, Thailand has increasingly embraced alternative energy sources. Central to its energy development strategy is the Alternative Energy Development Plan (AEDP), which aims to reduce energy intensity, capitalize on residual resources, and mitigate greenhouse gas emissions. While significant strides have been made in meeting various consumption targets set forth by the AEDP, notable challenges persist, particularly in the realms of bio-mass-derived electricity generation, bio-gas utilization, and bio-ethanol production from bio-mass. Therefore, this study delves into the factors contributing to the shortfall in achieving AEDP targets and proposes strategies to enhance the efficiency of the bio-energy supply chain. Leveraging mathematical and linear programming techniques, our research optimizes the supply chain dynamics, accounting for monthly supplier profiles spanning 77 provinces, 17 distinct biomasses, and 427 bio-energy plants equipped with diverse energy conversion technologies. Our findings indicate that Thailand currently boasts adequate bio-mass resources to fulfill the electricity and bio-ethanol targets outlined in the AEDP—provided enhancements are made to supply chain efficiency. To fully realize the objectives of the AEDP, we recommend augmenting bio-mass cultivation efforts and implementing new power plant installations. Additionally, we advocate for the consideration of high-methane content fuels, such as solid waste, as a means to alleviate bio-mass demand. This study underscores the paramount importance of strategic planning and optimization in propelling Thailand towards its alternative energy ambitions while surmounting supply chain impediments.

A systematic evaluation of risk in bioenergy supply chains

We introduce a novel method of evaluating risks of disruption to (bio)fuel supply chains. The biofuel landscape is complex, with multiple options for feedstocks and processing routes, but the type, size, and scale of risks are currently not sufficiently appreciated. As a consequence, the uptake of biofuels remains below expectation suggesting the need for comprehensive risk assessment. Our method of risk discovery and evaluation is based on a transparent and robust sustainability assessment framework, and exploits the richness of data and expert analysis available in publications. In a UK case study, we show that biomass (solids) has a similar risk score to coal, biogas being slightly less risky, with bioliquids being less risky still though more risky than wind and solar power. The most important cause of risk, ‘changing policy or regulatory framework’, reflects this fledgling industry's need for policy support. The second most important cause of risk is ‘lack of access to capital’, reflecting the scale of the process engineering required to convert biomass to tightly specified products which could substitute fossil fuels. Levels of optimism bias in the biofuel industry are high, leading to unrealistic expectations from complex technologies and dubious claims about the quantity of resource available. This, together with the wide variety and variability of feedstocks complicates the business case for biofuel investment. Bioenergy policy would benefit from a more nuanced understanding of risks which impede the widespread large-scale deployment of biofuels.

Assessing the impact of seasonality on bioenergy production from energy crops in Germany, considering just‐in‐time philosophy

AbstractThe availability of biomass is strongly influenced by seasonality, which can affect the production of biofuels, biogas, and bio‐based products in the downstream bioenergy supply chain. Rapeseed, maize silage, sugar beet, wheat, and grass from grassland are the most popular energy crops; they play a significant role in the German bioenergy strategy and are being discussed extensively in the current gas shortage context. Most models in the literature assume yearly temporal resolution for these energy crops, which can negatively impact the accuracy of results. This problem is increasingly relevant under weather conditions that are varying increasingly due to climate change; in this study we therefore employ the extended bioenergy optimization model (BENOPTex) to explore the impact of seasonality on the optimal deployment of biomass from energy crops in bioenergy production in the German heat, power, and transport sectors, which typically show high dependency on fossil fuels. First, we increased the model's temporal resolution using available datasets and documents. Next, the varying availability factors were embedded in the optimization model, considering the no‐storage policy for energy crops in accordance with the just‐in‐time philosophy. Finally, the outcomes of the BENOPTex with annual resolution were contrasted with the results including the effects of seasonality, while considering various objective functions. We demonstrated a shift toward the consumption of woody biomass until 2045 due to its longer shelf life and improved storability. The energy demand stemming from summer leisure travel was also anticipated to exceed the bioenergy system's capacity. The insights provided here might be interesting for policymakers who design roadmaps for bioenergy development with a more resilient energy supply.

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Optimization of the Bioenergy Supply Chain Under Governmental Intervention Policies

Optimization of the Bioenergy Supply Chain Under Governmental Intervention Policies

Pricing in competitive energy supply chains considering government interventions to support CCS under cap-and-trade regulations: A game-theoretic approach

This paper investigates the impacts of governmental support strategies on rival bioenergy and conventional energy supply chains in the presence of cap-and-trade regulations using a game-theoretic approach. The role of government in reducing carbon emissions and increasing carbon capture and storage (CCS) is explored and compared in three scenarios: NO-Support (N), Subsidy on investment (C), and FIT subsidy (I). In the bioenergy supply chain, the bioenergy manufacturer determines the wholesale electricity price and the level of CCS efforts, and the 3 PL company specifies the level of carbon emission reduction efforts. The decision variable of the conventional energy supply chain is the wholesale electricity price. In addition to the carbon cap, the government must decide the rate of its contribution to CCS and 3 PL costs in scenario C and the Feed-In Tariff subsidy rate in the scenario I. The results show that in the presence of cap-and-trade regulations, all examined governmental support strategies, especially those in scenario C, can significantly improve the profits of bioenergy supply chain members while sustainable development goals. In addition, Scenario C is more effective in incentivizing the supply chain members to increase CCS and reduce carbon emission levels among the three scenarios.

An integrated sustainable bio-fuel and bio-energy supply chain: A novel approach based on DEMATEL and fuzzy-random robust flexible programming with [formula omitted] measure

The viability of the Indian government’s green initiative to blend 20% fuel grade ethanol with gasoline is investigated by modelling a sustainable integrated bio-fuel and bio-energy supply chain with multiple echelons in order to address the dual crisis of fossil fuels, i.e., environmental as well as gradually diminishing reserves. In order to find the best strategic and planning decisions, a multi-objective mixed integer programming model is developed that analyses the supply chain from a technological, economic, environmental, and social standpoints while handling various feedstocks, supply zones, products, and multiple periods. The drawbacks in addressing the uncertainty in the parameter as well as risk in decision making are overcome by introducing a novel fuzzy-random robust flexible programming approach based on Me measure and probabilistic programming. The presented model incorporates hybrid uncertainty, flexible constraints and a convex mixture of optimistic-pessimistic perspective of decision maker. A hybrid strategy combining the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method and the multi-choice reference point goal programming with S-shaped utility function is presented to solve the generated multi-objective model. A case study in West Bengal, India is then performed that envisages the economic, environmental and social aspects of a bio-fuel and bio-energy supply chain.

Importance of incorporating spatial and temporal variability of biomass yield and quality in bioenergy supply chain

Biofuels made from biomass and waste residues will largely contribute to United States’ 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds.

Forest biomass and bioenergy supply chain resilience: A conceptual decision-making model

Forest biomass and bioenergy supply chain resilience: A conceptual decision-making model

Forest biomass and bioenergy supply chain resilience: A conceptual decision-making model

In recent years, with the increasing concerns about the global warming, limited non-renewable energy sources, and uncertainty in renewable energy sources accessibility, how to continuously supply clean sources of energy in form of a resilient system structure has become a topic of intense scholarly interest. Based on the milestone literature on the research field of renewable and non-renewable energies, forest biomass and bioenergy have significant compatibility with both industry and nature. Through this, our proposal contributes to supply one of the most available clean energy sources and direct this type of energy needed in an optimal way. The essential resilience components are determined and classified according to the theoretical and practical concepts. Then, this research is made use of a conceptual decision-making model based on third order confirmatory factor analysis to explore the interactions among the components in form of a network. The results reveal that the proposed model has sufficient validity and reliability levels. Finally, key interactions among the elements of model that contributed to most of the impacts on the forest biomass and bioenergy supply chain resilience were found to be optimized constantly within the network.

Coordination of bioenergy supply chains under government incentive policies: a game-theoretic analysis

Coordination of bioenergy supply chains under government incentive policies: a game-theoretic analysis

A multi-period location-routing model for waste-to-energy supply chain: A case study

Due to the importance of energy in human life as well as the global environmental conditions, societies aim to lower the use of fossil fuels and supply their energy needs using renewable resources such as wind, solar, and biomass instead. In this research, we investigate a bioenergy supply chain in which energy is generated from municipal solid waste (MSW) considering waste separation and various technologies that turn wastes to biofuels. In addition, a mixed integer linear programming model is developed to minimize cost of waste collection and transportation from MSW generation areas, cost of transportation of generated biofuels and installation cost of consumption points. The presented model also considers the effect of utilizing cross-dock centers on designing the network for collecting and transporting wastes from MSW generation areas to conversion centers. In addition to show the application and efficiency of the developed model, Khorasan Razavi province is regarded as the case problem to which the model is applied and the results are thoroughly analyzed.

Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

ABSTRACT Biomass sources have the potential to mitigate carbon emissions as a renewable source while reducing waste and residues. Seasonality and disruption risks are some of the disadvantages of biomass resources requiring that biomass supply chains be managed such that to withstand disruptions. There has been very limited research on integrating predictions for smart management on supply or demand sides of biomass supply chains. In this study, a number of predictive models are investigated for building energy demand and biomass stock availability subject to forecasts of weather conditions. On that basis, an allocation algorithm is proposed for optimal collection and logistics of biomass from land to depots. Accordingly, Google Maps API will be used to identify the best distribution routes for delivering biomass from depots to end-users. A case study with real (supply and demand) data is considered. The proposed integrated data-driven approach aims at improving the accuracy of biomass supply and demand predictions and coordinating these predictions to enhance the resiliency of bioenergy supply chain routing decisions.

Assessing and prioritizing challenges facing bioenergy supply chain in norway: A Delphi-AHP method

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Integrating Analytical Hierarchical Process and Network Optimization Model to Support Decision-Making on Biomass Terminal Selection

Forest biomass is an appealing bioenergy feedstock due its renewability, availability and potential to stimulate local economies. It is, however, voluminous, with heterogenous fuel characteristics and uncertainties in its supply. The feasibility of a bioenergy facility is contingent on a secure supply of uniform feedstock; a terminal in the supply chain can be useful in this regard. Biomass can be treated in the terminal to meet quality specifications and stored to overcome seasonality and supply disruptions. Nonetheless, such terminals require a significant capital investment; thus, the decision to use a terminal needs to be made judiciously. The decision process must account for a diverse set of factors that influence the terminal’s effectiveness. These include both quantitative and qualitative factors. The objective of this study is to develop a multi-criteria decision-making framework that takes quantitative and qualitative factors into consideration while selecting a terminal. The framework consists of analytical hierarchy process to analyze qualitative information, and a mixed-integer programming model to evaluate quantitative information including fuel quality (moisture content and thermal value). This hybrid framework was implemented in a case study. It proved to be an effective tool for identifying terminals with the highest potential to generate value for the bioenergy supply chain.

Multiperiod optimal planning of biofuel refueling stations: A bi-level game-theoretic approach

Energy suppliers pay more and more attention to bioenergy which has great potential for responding to extreme climate challenges. Nevertheless, few studies concentrate on the optimal planning of the downstream bioenergy supply chain. This study proposes a multiperiod bilevel game-theoretic framework for the optimal planning of biofuel refueling stations, including an upper-level model for petroleum companies and a lower-level model for biofuel companies. Based on the characteristic of the game theory and the implementation of carbon taxes, four scenarios are established. Finally, the proposed method is validated by a real-world case in Beijing, China. Under the upper limit of the blending ratio, the results show that cooperation can achieve the biggest overall profit. Petroleum companies are more profitable for cooperating with biofuel companies no matter whether carbon tax exists or not. Biofuel companies can get more profit if they non-cooperate with petroleum companies when there is no carbon tax, but the existence of the carbon tax makes it more profitable for biofuel companies to cooperate with petroleum companies. Besides, the best choice for biofuel companies to enter the fuel retailing market is the early stage of energy transition when no carbon tax exists or carbon tax is low.

Sistem Pendukung Keputusan Cerdas Menggunakan Metode Ant Colont Optimization (ACO) untuk Pencarian Jalur Optimum Rantai Pasok Bioenergi Berbasis Kelapa Sawit

The concept of supply chain management in business activities is very necessary. This concept involves more than one type of company. One of the businesses that requires this concept is palm oil-based bioenergy management. The optimal path in the supply chain network is very difficult, because the product supply chain network is perishable. For that we need an intelligent decision support system model in the search for the optimal path. The purpose of this study is to determine the optimum value and to find the optimum path of the palm oil-based bioenergy supply chain. The method used in the development of the model is the ant colony optimization method, namely the shortest path method. The data processed is the distance and time of supply. The optimal path search result is 0.035. So that this research becomes a reference in determining the optimal path.

Decision Model for Determining the Feasibility of Rice-Based Bioenergy Supply Chain Development Area with Fuzzy Logic-AHP Approach

Decision on location determination for rice-based Bioenergy Agro-industrial area is one part of the efforts to develop the bioenergy supply chain. The development location is selected based on the criteria set by the government. This study aims to plan the model of determining the location of agro-industrial supply chain development area of Rice-based bioenergy as well as evaluate the important criteria that need to be considered in the selection of the location of a bioenergy agro-industry so that alternative locations can be obtained from several potential locations that are targeted for rice-based bioenergy agro-industry development areas in Banten province of Indonesia. By using the fuzzy logic-AHP method approach, it is expected to obtain a decision model based on multi-criteria with a hierarchical process. The result of this study is the most optimal district used as the location of development of rice-based bioenergy agro industry area in Banten Province is Lebak Regency with the highest weight value 0,4328. Based on the results of research proposals for further research this method can be applied to the development of other bioenergy agro-industry, taking into account the criteria of spatial aspects based on geographical factors as well as appropriate alignment in an industrial area.