Search Results (33)

Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article

This review examines how the integration of circular bioeconomy principles with digital technologies can drive climate change mitigation, improve resource efficiency, and facilitate sustainable biorefinery development. This highlights the urgent need to transition away from fossil fuels and introduces the bio-circular economy as a regenerative model focused on biomass valorization, reuse, recycling, and biodegradability. This study compares linear, circular, and bio-circular approaches and analyzes key policy frameworks in Europe, Latin America, and Asia linked to several UN Sustainable Development Goals. A central focus is the role of digitalization, particularly artificial intelligence (AI), the Internet of Things (IoT), and blockchain. Examples include AI-based biomass yield prediction and biorefinery optimization, IoT-enabled real-time monitoring of material and energy flows, and blockchain technology for supply chain traceability and transparency. Applications in agricultural waste valorization, bioplastics, bioenergy, and nutraceutical extraction are also discussed in this review. Sustainability tools, such as automated life-cycle assessment (LCA) and Industry 4.0 integration, are outlined. Finally, future perspectives emphasize autonomous smart biorefineries, biotechnology–nanotechnology convergence, and international collaboration supported by open data platforms. Full article

In the Republic of Moldova, orchard biomass represents an important resource for the production of densified solid biofuels, with peach having the highest sustainable energy potential (33.5 ± 6.54 GJ·ha⁻¹). However, the quality of solid biofuels derived from orchard biomass is often constrained by heterogeneity in moisture content, uneven particle size distribution, and inadequate drying or blending practices along the supply chain. Optimizing the solid biofuel supply chain is therefore essential to minimize feedstock variability, ensure consistent densification quality, and reduce production costs. The aim of this study was to improve the process of producing densified solid biofuels from orchard biomass. Specifically, the study investigated how raw material moisture and particle size influence briquette density and durability, and how ternary mixtures of peach biomass, wheat straw, and sunflower residues can be optimized for enhanced energy performance. All experimental determinations were performed using validated methods and calibrated equipment. The results showed that optimal performance is achieved by shredding the biomass with 4–8 mm sieves and maintaining the moisture content between 6 and 14%, resulting in briquettes with the density of 1.00–1.05 g·cm⁻³, ash content below 3–5%, and an energy yield of 18.4–19.2 MJ·kg⁻¹. Ternary diagrams confirmed the decisive role of peach lignocellulosic residues in achieving high density, low ash content, and increased energy yield, while wheat straw and sunflower residues can be used in controlled proportions to diversify resources and reduce costs. These findings provide quantitative insights into how mixture formulation and process parameters influence the briquette quality, contributing to the optimization of solid biofuel supply chains for orchard and agricultural residues. Overall, this study demonstrates that competitive solid biofuels can be produced through careful balancing of mixture composition and optimization of technological parameters, offering practical guidelines for sustainable bioenergy development in regions with abundant orchard residues. Full article

Renewable energy expansion requires cost-effective strategies to integrate underutilized biomass resources into energy systems. In Australia, forest residues represent a significant but largely untapped feedstock that could contribute to a more diversified energy portfolio. This study presents an integrated geospatial and optimization decision-support model designed to minimize the total cost of forest biomass-to-bioenergy supply chains through optimal facility selection and network design. The model combined geographic information systems with mixed-integer linear programming to identify the optimal candidate facility sites based on spatial constraints, biomass availability and infrastructure proximity. These inputs then informed an optimization framework that determined the number, size, and geographical distribution of bioenergy plants. The model was applied to a case study in Queensland, Australia, evaluating two strategic scenarios: (i) a biomass-driven approach that maximizes the use of forest residues; (ii) an energydriven approach that aligns facilities with regional energy consumption patterns. Results indicated that increasing the minimum facility size reduced overall costs by capitalizing on economies of scale. Biomass collection accounted for 81%–83% of total supply chain costs (excluding capital installation), emphasizing the need for logistically efficient sourcing strategies. Furthermore, the system exhibited high sensitivity to transportation distance and biomass availability; energy demands exceeding 400 MW resulted in sharply escalating transport expenses. This study provides a scalable, data-driven framework for the strategic planning of forest-based bioenergy systems. It offers actionable insights for policymakers and industry stakeholders to support the development of robust, cost-effective, and sustainable bioenergy supply chains in Australia and other regions with similar biomass resources. Full article

Future energy security and consumption trends for energy products have stimulated the consumption of products such as bioethanol, biodiesel, or biogas, generated from non-petroleum sources. Therefore, the production of these products aims to increase its viability progressively. The supply chain (SC) approach enables the evaluation of the structures used to produce these types of bioenergy. Consequently, the identification of tools to represent the production stages of the SC and their articulation with the objective functions, as well as the strategies and solution software implemented in the design of SC for bioenergy products are presented throughout this bibliographic analysis. Based on systematic and narrative literature analysis, current trends and future research issues are performed. The bibliographic analysis has evidenced that the production of bioenergy is a research topic that has evolved in the last decades. Strategic decisions such as factory capacity and the location of production facilities are the most frequently used decision variables in the design of bioenergy SC. Similarly, it was found that the bioenergy SC designs have focused on the implementation of several feedstocks simultaneously. Finally, due to these evaluation and design trends, the bioenergy SC designs that include environmental and social objectives aimed at sustainability are a future relevant research issue. Full article

Most nations are shifting towards renewable energy sources to reduce energy-related emissions and achieve their net zero emissions targets by mid-century. Consequently, many attempts have been made to invest in clean, accessible, inexpensive, sustainable and reliable renewable energy sources while reducing dependency on fossil fuels. Recently, the production of biogas and upgrading it to produce biomethane is considered a sustainable way to reduce emissions from natural gas consumption. However, uncertainties in the biomass supply chain and less attention to decarbonising the natural gas grid have led to fewer investors in biomethane injection projects. Thus, researchers have applied Geographic Information System (GIS) as the best decision-making tool with spatial analytical and optimisation capabilities to address this issue. This study aims to review GIS-based applications on planning and optimising the biomass supply chain. Accordingly, this review covers different GIS-based biomass assessment methods with the evaluation of feedstock types, GIS-based approaches on selecting and optimising bioenergy plant locations and GIS-based applications on facilitating biomethane injection projects. This review identified four major biomass assessment approaches: Administrative division-based, location-based, cluster-based and grid-based. Sustainability criteria involved in site selection were also discussed, along with suitability and optimality techniques. Most of the optimising studies investigated cost optimisation based on a single objective. However, optimising the whole supply chain, including all operational components of the biomass supply chain, is still seldom investigated. Furthermore, it was found that most studies focus on site selection and logistics, neglecting biomethane process optimisation. Full article

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions. Full article

One of the United Nations’ 17 Sustainable Development Goals is to “Ensure access to affordable, sustainable, and clean energy for all.” Additionally, the growing concerns about climate change and energy security have heightened the importance of exploring alternative energy sources to replace fossil fuels. The utilisation of agricultural waste for bioenergy production has acquired significant attention due to its potential to mitigate environmental impacts and provide renewable energy sources. However, the major obstacle to producing bioenergy is managing the supply chain while considering economic, environmental, and social factors in an optimal way. This paper presents a comprehensive overview of the literature on the management of agriculture waste supply chains, specifically related to the use of modelling and optimisation techniques for planning. The first section describes different stages of the supply chain and various technologies for converting biomass to bioenergy. This is followed by a synopsis of the literature reviewed based on decision levels, objective functions, modelling methodologies, and optimisation approaches. Finally, the review highlights limitations and gaps in current research and the areas with potential for further exploration. Full article

Sustainable energy projects in Africa are particularly vulnerable in terms of sourcing vital alternative fuels due to the complexity of sourcing processes, contract agreements and relationships between society managers or directors and supplier chain entities. These challenges can affect any phase of a sustainable project, and the losses can be as high as 3.2 EURO/GJ. In addition, there is reduced competition and fair trade, low profits and poor quality of the fuel purchased. Technology (mobile application) is one powerful tool that can solve the above challenges by controlling or managing the supply and demand of biomass-based fuels, agriculture residue, industrial waste and many more. Thus, the main objective of this study is to evaluate the feasibility of a developed digital platform to remove barriers in the trade of alternative fuels. Data collection began with the identification of the key production areas (sources) and quantities of three selected AFs. Secondly, data on the seasonal variations in alternative fuel (AF) quantities were obtained from the identified locations. Thirdly, the acquisition costs were calculated based on the quality and characteristics of the AFs. Results were then transferred into a mobile application where industries could assess, compare, and bargain for AF based on quality and price. Due to the introduction of competitive pricing, overall, the mobile application improved the savings on sourcing for AFs by industries by 2.89 EURO/GJ. In terms of profit optimization, the farmers have value for money and fair bargaining for their products, thus increasing their revenues for the planting season. It was also observed that the cost of the fuel was based on the proximity of the source to the demand industry. In conclusion, the mobile application facilitates a circular economy between the farmers, suppliers and industries where industries receive fair and competitive prices for their fuel whiles farmers receive extra income for farming businesses and agricultural waste is sustainably managed through a circular economy. Full article

An agent-based modeling framework is developed and employed to replicate the interactions among urban farms. The objectives are to efficiently manage an urban farm’s food, energy, and water resources, decrease food waste, and increase the food availability for the local community. A case study of eleven farms was investigated in Vancouver, Canada to study the linkages between the resources in the urban food, energy, and water nexus. Each urban farm in the simulation belonged to a community microgrid generating electricity from solar and wind. The local farms aimed to provide fresh produce for their respective local communities. However, at some points, they lacked supply, and at other points, there was excess supply, leading to food waste. Food waste can be converted into fertilizers or bioenergy. However, an alternative solution must be employed due to the natural resources required for production, efficiently managing resources, and adhering to sustainability guidelines. In this paper, an optimization framework was integrated within the agent-based model to create a micro supply chain. The supply chain directly linked the producers with the consumers by severing the links involved in a traditional food supply. Each urban farm in the study collaborated to reduce food wastage and meet consumer demands, establishing farmer-to-farmer exchange in transitional agriculture. The optimization-based micro supply chain aimed to minimize costs and meet the equilibrium between food supply and demand. Regular communication between the farms reduced food waste by 96.9% over 16 weeks. As a result, the fresh food availability increased for the local community, as exemplified by the consumer purchases over the same period. Moreover, the simulation results indicated that the renewable energy generation at the community microgrids aided in the generation of 22,774 Mwh from solar and 2568 Mwh from wind. This has the potential to significantly reduce CO₂ emissions in areas that heavily rely on non-renewable energy sources. Full article

Forest bioenergy value chains can offer attractive opportunities to promote economic development and mitigate climate change. However, implementing profitable and efficient forest biomass value chains requires overcoming barriers that continue to hinder the development of bioenergy systems in several jurisdictions. The objective of this study was to compare the economic, social, and environmental sustainability of various potential configurations of forest bioenergy value chains, including forest biomass supply and bioenergy production chains, in the Capitale-Nationale region of Quebec (Canada), which is a jurisdiction that has considerable forest resources but makes little use of bioenergy. We based our study on the ToSIA model parameterization and compared various policy measures, biomass supply, and logistics scenarios for 2008 and 2030. Our results showed that wood chip and pellet value chains in the Capitale-Nationale region would positively contribute to the regional economy in 2030, even in the absence of subsidies. Moreover, actions to increase biomass feedstock mobilization in 2030 would lead to an increase in gross value added, employment, and energy production in the region compared with 2008 and a greater increase than other considered policy or logistical measures. However, increased biomass feedstock mobilization would also mean higher relative GHG emissions and more fossil fuel energy input per unit of bioenergy than in the other scenarios. Conversely, optimizing biomass feedstock and combustion technologies could help minimize the fossil fuel energy input needed and GHG and some non-GHG pollutant emissions. Overall, our study suggested that implementing policy and logistical measures for forest biomass value chains could make the significant mobilization of forest bioenergy attainable and, in turn, Quebec’s 2030 bioenergy target of 17 petajoules realistic. Full article

Over the years, cities have undergone transformations that, invariably, overload and even compromise the functioning of an energy matrix dependent on increasingly scarce resources. The high demand for energy has challenged stakeholders to invest in more sustainable alternatives, such as bioenergy, which, in addition, helps to reduce the pressure for finite resources, enable the energy recovery of waste and contribute to the mitigation of carbon emissions. For these improvements to be successful, stakeholders need specific technological strategies, requiring tools, methods and solutions that support the decision-making process. In this perspective, the current work aimed to develop a framework optimizing the evaluation of waste bioenergy projects through the application of algorithms. Therefore, a literature review was carried out to select the algorithms and identify the sectors/areas and stages in which they are applied. These algorithms were then grouped into two sequential phases. The first targeted the evaluation of region, based on the type and supply of biomass, while the second sought to optimize aspects related to infrastructure and logistics. Both phases were concluded with the application of multi-criteria methods, thus, identifying the areas/regions with the greatest potential for implementing bioenergy projects. In general, it was observed that there are different algorithms and multi-criteria analysis methods that can be suitable in bioenergy projects. They were used to identify and select the regions with the greatest potential for bioenergy plant implementation, focusing on the type, quantity and perpetuity of biomass supply, to assess the operational efficiency of machines, equipment, processes and to optimize the logistics chain, especially the collection and transport of biomass. Thus, the joint work between the use of algorithms and multi-criteria decision methods provides greater assertiveness in choices, helping to identify the most viable projects and mitigating risks and uncertainties for decision-makers. Full article

In Malaysia, palm oil industries have played significant roles in the economic sectors and the nation’s developments. One aspect of these industries that is gaining growing interest is oil palm residue management and bio-based product generations. EFB has been identified to be a feasible raw material for the production of bio-energy, bio-chemicals, and bio-materials. In this paper, our previous deterministic mathematical programming model was extended to include decisions for selecting optimal transportation modes and processes at each level of the processing stage in the supply chain. The superstructure of alternatives was extended to show states of produced products whether solid, liquid, or gaseous, and for which truck, train, barge, or pipeline would be possible modes of transportation. The objective function was to maximize profit which accounts for associated costs including the emission treatment costs from production and transportation. The optimal profit was USD 1,561,106,613 per year for single ownership of all facilities in the supply chain. Full article

This research aimed to systematically review the development studies pertaining to forest biomass and bioenergy supply chain resilience (SCR). In this regard, a mixed procedure was implemented in order to explore and analyze the relevant publications, and to answer the research questions. First, the databases and journals working on forest biomass and bioenergy supply chains (SCs) were identified based on the indices of the review process and the indices of the barriers and enablers. Next, data refinement was employed to filter the publications into four levels and determine the semifinal cases. Moreover, the references of the semifinal publications were tracked in order to achieve the final cases. Consequently, 88 papers were determined as the final cases through which the barriers and enablers were explored and analyzed. Furthermore, in order to meet the research gap in this area and prove the connections of those barriers and enablers with the resilience capability, their relationships with the main resilience factors were investigated. According to the assessment, the findings of this research on the definition, barriers and enablers of forest biomass and bioenergy SCR can be applied as a basis for the comprehension and optimization of the structure of SCs in the forest biomass and bioenergy industries. Full article

Extensive studies are conducted to investigate the potential and techno-economic feasibility of bioenergy routes in different countries. However, limited researches have been focused on the whole national agricultural bioenergy resources in Egypt. This research provides an assessment of the potential agricultural biomass resources for electric energy production in Egypt. It provides a strategic perspective for the design of a national network of biomass power plants to utilize the spatially available agricultural residues throughout a country. A comprehensive approach is presented and is applied to Egypt. First, the approach estimates the amount, type, and characteristics of the agricultural residues in each Egyptian governorate. Then, a techno-economic appraisal for locating a set of collection stations, and installing a direct combustion biomass power plant in each governorate is conducted. SAM simulation software is used for the technical and economic appraisals, and preliminary plant capacities are estimated assuming one plant in each governorate. Secondly, a new mixed integer linear programming (MILP) model is proposed and applied to optimally design a biomass supply chain national network to maximize the overall network profit. The network is composed of the collection stations, the potential biomass power plants, and the flow distribution of residues to supply the selected plants. Results indicate that the Egyptian agricultural residue resources can produce 10 million ton/year of dry residues, generate 11 TWh/year, an average levelized cost of electricity (LCOE) of 6.77 ¢/kWh, and supply about 5.5% of Egypt’s current energy needs. Moreover, the optimization results reveal that a network of 5 biomass power plants with capacities of 460 MW each should be established in Egypt. This approach is thought to be particularly suitable to other developing countries whose energy demand depends on fossil fuels and poses a heavy economic burden, and whose residues are massive, wasted, and not industrialized. The obtained results may also enrich future comparative research that studies the impact and feasibility of implementing agro-residue based biomass electric energy generation. Full article