Biomass Supply Chain Research Articles

Design and operation of modular biorefinery supply chain under uncertainty using generalized Benders decomposition

AbstractBiomass supply chain performance is heavily affected by uncertainties stemming from supply, demand, or unexpected disruptions. Unlike petrochemical plants that use crude oil, biorefineries often have to deal with the uneven spatial‐temporal distribution of feedstock supply. The modular production strategy provides more flexibility in chemical manufacturing by allowing fast capacity expansion and unit movement. However, modeling and optimizing modular biomass supply chain under uncertainties becomes challenging due to high dimensionality and the existence of discrete decisions. This work optimizes the multiperiod biomass supply chain using the rolling horizon planning and two‐stage stochastic programming framework. We then applied generalized Benders decomposition to reduce the computational complexity of the stochastic mixed integer nonlinear programming supply chain optimization. Furthermore, the solution of the stochastic programming could be used to quantitatively describe the life‐cycle assessment uncertainties of the biomass supply chain performance, demonstrating seasonality and random variability.

Resource allocation and route planning under the collection price uncertainty for the biomass supply chain

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.

Market analysis of coupled biochar and carbon credit production from wildfire fuel reduction projects in the western USA

AbstractThe investment potential for biochar production from low‐value forest biomass in the western USA is over USD 20 billion at current carbon prices. This investment could generate approximately 70 million carbon credits annual – roughly the same number currently generated globally by all forestry and agricultural carbon projects. Biochar production that utilizes woody biomass specifically from wildfire fuel thinning projects as a feedstock can contribute financially to much‐needed fuel thinning projects. Each coupled biochar and carbon credit production system has positive returns in certain scenarios. Light upgrades to existing biopower facilities have the highest returns, with internal rates of return generally between 10–30%. Mobile biochar production often had the lowest returns. However, land managers can subsidize mobile biochar production up to USD 150–300 per tonne of biochar and still save money in comparison with pile‐burning low‐value biomass while also generating approximately one carbon credit for every two bone‐dry tonnes of low‐value biomass turned to biochar. The critical barrier to industry growth is the lack of transparent biomass supply chains that enable long‐term contracting for feedstock, production schedules, and investment. Moving forward, there are three potential pathways for the biochar industry to scale and utilize biomass from forest management and fuel thinning projects. Either (1) the carbon market will need to sustain high carbon prices, (2) a subsidy or other mechanism will need to decrease the cost of feedstock biomass, or (3) production will need to take advantage of economies of scale to bring down biochar prices while increasing production.

Bi-objective optimization modeling for biomass supply chain planning

Biomass energy plays an essential role in renewable energy for many reasons, such as reducing the dependence on fossil fuels and lowering greenhouse gas emissions, providing heat, electricity, and biofuels for various applications, and utilizing waste materials for helpful energy products. Besides, it can create employment opportunities and promote rural development, especially in developing countries where biomass resources are abundant and accessible. In the context of renewable energy research and application, this paper aims to develop a multi-objective mixed integer linear programming for designing multiple echelon biomass supply chain networks. The model is formulated to consider the economic costs and environmental impact of biomass distribution from the suppliers to the biomass plants. In this research, the Epsilon constraint method is adopted to generate Pareto fonts, which provides the trade-offs between two objectives. Moreover, sensitivity analysis is implemented to provide decision-makers with information about a network with changed parameters such as demand. Our model allows the decision maker to determine the capacity of warehouses and biomass power plants, inventory levels, type of trucks, etc. The proposed model is verified and evaluated using a practical dataset from Can Tho province, Central Mekong River Delta in Vietnam, generating several benefits for energy security and sustainability. Such a network includes 3 types of power plants, 3 scales of warehouses, 13 potential locations, and 41 suppliers. From the generated solutions, with the proportion of biomass electricity satisfaction varying from 5% to 30%, Hung Phu, O Mon, and Cai Rang industrial parks are the most suitable for power plants.

A supply chain of a coastal biomass incorporating fuzzy deterioration and freshness under dynamic unit price

A supply chain of a coastal biomass incorporating fuzzy deterioration and freshness under dynamic unit price

Decarbonizing the Fertilizers Sector: An Alternative Pathway for Urea and Nitric Acid Production

Abstract In order to alleviate the environmental impact that nitrogen fertilizers production is responsible for, several efforts have been addressed to incentivize the partial or total decarbonization of the supply chains of ammonia and its derivatives. The decarbonization of the nitrogen fertilizers sector might help not only improve its carbon footprint, but also reduce its dependence on international market prices of natural gas. To this end, in this work, two fertilizers production plants, namely nitric acid and urea, are integrated into a kraft pulp mill via black liquor gasification to supply the renewable input required by the chemical processes. The performance of the proposed systems is assessed and compared in the light of thermodynamic, economic, and environmental indicators. The choice of the utility system has been driven by the interrelation of the market conditions, as well as the extent of the energy integration of the chemical plants. As a result, the exergy efficiency of the integrated plants remains competitive against the conventional kraft pulp mill thanks to the optimal selection of the operating conditions of the utility system. This efficiency indicator compares the exergy input with the minimum theoretical work needed to reversibly produce the chemicals out from the components in the environment. Furthermore, the overall CO2 emissions balance achieves negative values for some scenarios, pointing out the potential for atmospheric CO2 depletion when the integrated processes are implemented. The indirect emissions of the biomass supply chain proved to have a great impact on the CO2 balance. Finally, the findings suggest that both nitric acid and urea production have positive incremental net present values across the range of carbon taxes considered, indicating the economic viability of these integrated systems vis-à-vis the traditional standalone pulp and fertilizers production plants.

Towards a Bioeconomy: Supplying Forest Residues for the Australian Market

Australia has abundant volumes of forest residues that are a potential feedstock for supplying biomass as a renewable carbon carrier to the market. However, there remains an underutilization of this resource, even in mature bioeconomy markets. Several existing or perceived barriers can be attributed to the underdeveloped, forest-based bioeconomy in Australia. One of these is the limited understanding of feedstock supply costs. In this study, two ranking approaches were applied to identify the optimal biomass feedstock supply chain from field to conversion plant gate. A panel of experts embedded in the Australian bioeconomy were employed to first assign ranks to biomass supply chain items by cost intensity. Then, a layer of analytic hierarchical process (AHP) was used to weigh and rank various biomass supply pathways by efficiency. The results reveal that biomass extraction ranks the highest and biomass feedstock storage ranks the lowest, relative to other supply chain costs. Extracting and chipping material in the field attracted the most support from the experts in terms of efficiency, followed by transporting and chipping at the roadside and, finally, transporting and chipping at the conversion plant. This study provides insights for designers of the forest-based bioeconomy in Australia into relative cost drivers that may be applied to investment and industry decisions. It also provides a framework to support further investigations into forest biomass development and the management of biomass as a renewable carbon carrier at a time when Australia is transitioning from an energy policy focused on fossil fuels to a renewable energy strategy.

Matheuristic approach and a mixed-integer linear programming model for biomass supply chain optimization with demand selection

It is crucial to identify alternative energy sources owing to the ever-increasing demand for energy and the other environmental problems associated with using fossil fuels. Biomass as a source of bioenergy is considered a promising alternative to fossil fuels. This study aims to optimize the biomass supply chain by developing an integrated model incorporating typical tactical supply chain decisions based on market or demand selection decisions. To this end, a novel mixed-integer linear programming (MILP) model is proposed to maximize the profit of the corresponding biomass supply chain and to commercialize electricity production by selecting electricity demand and making supply chain decisions regarding power plant operations, biomass feedstock purchase and storage, and biomass transport trucks. Owing to the intricacy of the MILP model, a fix-and-optimize-based solution strategy is developed and validated by applying it to several instances of a real-world case study. The results demonstrate that the proposed strategy can significantly reduce computational time while preserving high solution quality. Additionally, it helps improve planning and decision-making as it reveals the effect of essential biomass logistics characteristics on routing outcomes.

A research on mathematical model approaches in biomass supply chain

A research on mathematical model approaches in biomass supply chain

Waste-to-Energy Online Marketplace: Leveraging AI Recommendation Matchmaking for Enhanced Biomass Sourcing in Bioenergy Production

This paper introduces a novel platform of Waste-To-Energy Online Marketplace. The platform maintains a comprehensive catalogue of available biomass resources, detailing types, quantities, and geographical locations. This allows bioenergy facilities to identify and select suitable biomass feedstock based on their specific energy production requirements. Through an intuitive online marketplace, stakeholders can negotiate agreements, ensuring a streamlined and mutually beneficial exchange of biomass feedstock for bioenergy production. The online matchmaker by A.I. recommendation engine platform opens new avenues for biomass suppliers and bioenergy facilities to connect beyond traditional geographical and logistical constraints, fostering a more expansive and interconnected market. Efficient matching ensures that biomass resources are utilised optimally, reducing waste, and maximising bioenergy production. The proposed model seeks to enhance the efficiency of converting sugarcane biomass into bioenergy, leveraging digital and A.I. technologies to match biomass producers with bioenergy facilities, optimising the efficient conversion of biomass resources into renewable energy and fostering a reduction in GHG emissions associated with traditional waste disposal methods. This innovative approach has the potential to revolutionise the biomass supply chain, facilitates competitive pricing and cost-effective transactions, benefitting both biomass suppliers and bioenergy producers, promoting sustainability, efficiency, and collaboration in the journey towards a greener and more resilient energy future.

Synthesis and Optimisation of an Integrated Renewable Energy and Greenhouse Network

This paper presents a study on the synthesis and optimisation of a renewable energy and resource supply chain network (SCN) to satisfy agricultural greenhouse resource and energy demands. This is motivated by the high costs and emissions to run greenhouses due to the resources and energy required for optimal growth conditions. The investigation aims to contribute to the growing area of research on circular practices in the agricultural sector. The SCN includes biomass from agricultural waste transported to a utility hub, consisting of water and carbon dioxide (CO2) as resource supplies, as well as a boiler and steam turbine-generator and solar photovoltaic (PV), as energy conversion technologies. The resources and energy are distributed to greenhouses. The objectives are to minimise both the total annualised costs (TAC) and environmental impact (EI), calculated as CO2 emissions of the integrated network. Multi-periodicity accounts for the monthly and daily variation in constraints on the system, such as feedstock and solar radiation availability and energy limitations. The model is applied to a case study of an agro-industrial zone in Kwa-Zulu Natal (KZN) province, South Africa. The network meets greenhouse demands at a minimum TAC and EI of 8.052 ×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{6}$$\\end{document} R/y and 14,370 tCO2e/y, respectively. The biomass supply chain (BSC) contributes most significantly to the TAC at 43.7% while biomass combustion makes up 99.8% of the EI. Additional key results include bagasse and corn stover being selected as the only feedstocks and rail as the transport mode. The 26,560 MWh/y of electricity supplied to the greenhouses is made up by 71.4% from the boiler and turbine-generator with the remainder from solar PV. The potential of solar PV as a clean and low-cost energy source is demonstrated by the model selection at its full capacity. The study confirms that holistic modelling and optimisation of renewable energy SCNs for greenhouses is necessary to meet a trade-off between the network costs and CO2 emissions.

Optimisation framework of biomass supply chain in southwest Nigeria

The article investigates the development of a biomass supply chain model by studying the logistic, network optimisation, transportation and economic impact as it affects the southwest Nigeria, with a particular emphasis on the conversion of crop residues into bioproducts such as bioethanol and biodigestate for electricity generation. This model compares the energy produced from single and multiple feedstocks using simulation methods implemented in the MATLAB 2022b programming language solver with the linprog function to implement linear programming to investigate the maximum revenue and minimum production costs as they affect biomass supply chain in southwest Nigeria. The sensitivity analysis is performed to examine the impact of key parameters by measuring essential uncertainty features to show the profitability of the supply chain network while employing feedstock such as cassava peel, maize husk, rice straw, and sorghum bran. The study's findings provide insight into the optimal design and operation of the biomass supply chain in Southwest Nigeria, as well as the viability and potential of using various feedstocks for energy generation while taking into account the economic and logistical aspects of the supply chain. The optimisation framework provides decision-makers and stakeholders with practical tools to improve the sustainability of biomass utilisation and efficiency in the region, and research will help to develop a more robust and environmentally friendly energy system in the region, easing the transition to a renewable and sustainable future.

BIOMASS SUPPLY CHAIN OPTIMIZATION FOR RICE INDUSTRY IN PERLIS REGION

The efficient utilization of agricultural biomass holds significant promise in addressing energy and sustainability challenges. This study focuses on the optimization of the rice biomass supply chain in the Perlis region, aiming to enhance the resource utilization and minimize environmental impact. The abundance of rice residues generated after harvesting presents an opportunity to create a sustainable energy source while managing agricultural waste. This research encompasses a comprehensive analysis of the entire rice biomass supply chain, from residue collection at farms to the final biomass conversion process. Geographic information systems are employed to assess rice cultivation patterns and estimate potential biomass yield. Mathematical models are utilized to design an integrated supply chain network that considers collection, transportation, storage, and biomass conversion. Key factors influencing the supply chain are identified, including transportation costs, storage infrastructure, seasonal variations in biomass availability, and technological feasibility of conversion processes. Environmental implications are also evaluated, considering emissions reduction through the displacement of fossil fuels. The outcomes of this study contribute to the establishment of an efficient and sustainable rice biomass supply chain in the Perlis region. The developed optimization framework can serve as a blueprint for similar agricultural biomass utilization initiatives in other regions. The research not only addresses the technical aspects of biomass supply chain management but also considers the broader economic, social, and environmental implications, paving the way for a more resilient and resource-efficient agricultural sector.

Priorities for the sustainability criteria of biomass supply chains for energy

The sustainability of biomass supply chains is a topic of significant debate, given its importance in the emerging bioeconomic context. This study aims to identify the criteria perceived to have the highest preference when evaluating the sustainability of biomass supply chains. The data includes the assessments of 122 international experts providing their evaluations through a questionnaire distributed between November 2019 and February 2020. The questionnaire presents pairwise comparisons of 12 sustainability sub-criteria organised into economic, social, and environmental categories. The results are analysed using the analytic hierarchy process (AHP) in combination with kernel methods to identify consensus regions in the experts’ assessments. The responses showed that a large majority of experts belong to two distinct priority groups: an environmental oriented group (N = 78) and an economic oriented group (N = 21). The environmental-priority provided average weights of 22% to economic criteria, 22% to social criteria, and 56% to environmental criteria (highest priority); whereas the economic-priority group of experts assigned weights of 64% to economic criteria (highest priority), 13% to social criteria, and 23% to environmental criteria. Variations in the priorities were explained by the experts' contextual factors and backgrounds. In both groups, however, there was a consensus to prioritize the reduction of greenhouse gas emissions among environmental criteria, and revitalization of rural areas among social criteria. The results and methods proposed have broad applications in policy making, particularly in the comprehensive assessment of biomass feedstocks and supply chains, providing valuable insights for sustainable decision-making.

Biomass-to-power: Opportunities and challenges for Nigeria

Power is needed for the growth and development of any country. The Nigerian power sector continues to face tremendous challenges, especially in the areas of power generation and sustainability in the electricity value chain. Successive governments have carried out several reforms, but these problems persist. The major causes are tied to the lack of diversity in the country’s energy mix and over-dependence on non-renewable fossil fuels for power generation. With Nigeria’s growing power demand and the Federal Government’s commitment to renewable energy for reduced carbon emission, the potential of biomass in power generation can no longer be overlooked. Biomass is a renewable resource that is abundant in supply in Nigeria. Biomass resources can be obtained from multiple sources and has very low net carbon emission. Its immediate addition to Nigeria’s energy mix would aid the country in meeting its 2030 goal of increasing renewable power generation by 45% and reducing greenhouse emissions by 20%. This paper therefore studies the history of power generation in Nigeria and the challenges encountered so far. It then looks at biomass resources availability in the country, their viability as feedstocks for power generation, and biomass-to-power conversion processes. The challenges of this alternative power solution are identified and recommendations given. If these can be implemented, the biomass-to-power solution can significantly address the issues of low power generation and carbon emission. It would help improve energy security, environmental sustainability, circular economy, and economic opportunities for those in the biomass and power supply chain in Nigeria.

Multi-objective optimization design of sustainable biofuel network with integrated fuzzy analytic hierarchy process

Biofuel is one of the emerging sources of renewable energy, which is produced from biomass materials called feedstocks. Wood biomass, in particular, can improve air quality by reducing the smoke created by burning woody biomass. In this research, our key objective is to model the wood biomass supply chain to promote the effective operation of biofuel. Initially, we propose the mixed integer linear programming (MILP) model with multiple objectives for assessing the biofuel supply chain. Next, the fuzzy analytic hierarchy process (FAHP) is applied to assess uncertain data from decision makers and integrated with the proposed mathematical model. The developed model evaluates the perspective of sustainability by considering total cost – the surrogate criterion for the economic aspect, unsatisfied demand – the surrogate criterion for the social aspect, and CO2 emission – the surrogate criterion for the environmental aspect, into account. Next, both the supply of wood biomass from each farm node and the demand for produced electricity are evaluated with probabilistic scenarios and stochastic analysis in our analysis. Then, the sensitivity analysis and Pareto analysis are performed by varying parameters related to objective weights to verify and validate the model functionalities and objective tradeoffs. Finally, the regional case study in Thailand based on the wood-biomass data is applied with a real-world context to validate the model functionalities. Given that farmers’ locations in the case study are distant with diverse areas, the results of tradeoff analysis in our study can provide a strategic choice for enhancing competitive advantages for key decision makers in the biofuel network.

Optimizing the agroforestry residual biomass supply chain: A disruptive tool for mitigating logistic costs and enhancing forest management

In a world with different energy needs, and more focused on renewable energies, biomass makes a very positive contribution. As a way of not creating more pressure on the forest, reducing the combustible loads of the forest, and reducing forest fires, residual biomass becomes an interesting resource. However, logistical costs and lack of information are threats to this valorization. In this sense, the objective of this work is to propose a technological tool (concept and prototype) that allows in an easy way and with high usability to connect the various stakeholders. This study began with an analysis of the state of the art, firstly carrying out a systematic review of the literature, accompanied by a benchmarking that led to a consolidation of the concept in a first phase, which was then followed by the construction of a prototype using the Figma tool. Thus, this study resulted in a disruptive tool that could optimize the information flow between the stakeholders, and that could result in benefits for all of them. The study presents some insights that can be used in the success of future projects in this field.

Residual Forestry Biomass Supply Chain: A Mapping Approach

Residual Forestry Biomass Supply Chain: A Mapping Approach

The booming non-food bioeconomy drives large share of global land-use emissions

The non-food bioeconomy is widely recognized as a crucial strategy to address climate change. However, the growing non-food demand for biomass, such as bioenergy and bio-based products, is leading to global land use changes and consequent greenhouse gas emissions. In this study, a global region- and biomass-specific land-use emissions (LUE) inventory and the Food and Agricultural Biomass Input-Output Model (FABIO) were used to quantify the LUE embodied in global non-food biomass supply chains. The results revealed that in 2013, the LUE induced by non-food biomass demand accounted for over one-quarter of the global LUE, with wood as the dominant contributor (47%), followed by live animals (17%) and oil crops (13%). Additionally, over 876 million tons (Mt) of LUE were associated with international biomass trade for non-food use, mainly from tropical countries/regions (e.g., Brazil, Indonesia and Thailand) to large developing ones (Mainland China and India) and major developed ones (the USA, the EU27 and Japan). This underscores the need for consumption-based accounting of non-food biomass uses, including emissions from land use changes. Furthermore, demand-side measures such as enhancing transparency in international biomass supply chains, establishing monitoring and certification systems for certain bio-based commodities to track LUE, and fostering multi-stakeholder collaboration are crucial for effectively reducing LUE.

A bi-objective robust model for minimization of costs and emissions of syngas supply chain

Producing syngas from forest-based biomass could facilitate the transitions in energy and forest sectors by replacing natural gas; reducing emissions and wastes; and generating revenues. Optimizing the economic and environmental impacts of the biomass supply chains can help realizing these benefits. In this study, a bi-objective robust optimization model is developed for tactical supply chain planning of forest biomass gasification at a pulp mill. It optimizes the monthly flow, inventory, and preprocessing of biomass while minimizing annual costs and emissions. Robust optimization with an adjustable risk of constraint violation is used to model the uncertainties in supply and cost of biomass. The average cost and emissions of the robust Pareto-optimal solutions are 68% and 41% higher than those in the deterministic solutions, respectively. Although, the cost and emissions and their trade-off in the deterministic case are more favorable, the robust solutions ensure no biomass shortage while avoiding over-conservatism.