Search Results (170)

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Based on evidence synthesized from 152 studies and supporting geospatial and scenario analyses, results indicate that AI-optimized systems can recover 75–94% of prime-land yields, achieve carbon sequestration rates of 2.1–6.8 t CO₂e ha⁻¹ yr⁻¹, central estimate ≈ 7–9 Gt CO₂e yr⁻¹ at 35% adoption with moderate exclusions, and generate projected internal rates of return ranging from 8–22%, depending on feedstock type, regional conditions, and financing assumptions. Yield-recovery and carbon-sequestration ranges are drawn from synthesis of the reviewed literature; IRR, financial-leverage, and market-expansion figures are author-constructed scenario projections based on this evidence, not independently observed outcomes. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively projected, under scenario-based modeling, to reduce composite project risk scores by 30–45% and expand the investable universe of degraded-land biofuel projects by an estimated 340% relative to a no-AI, no-green-finance baseline; these figures represent author-constructed scenario estimates rather than direct empirical findings. We develop the AI-Biofuel-Land Restoration-Green Finance (ABLR-GF) conceptual framework (not yet empirically validated through field pilots or simulation) with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management. Full article

The industrial scalability and economic competitiveness of biomass-to-bioenergy and biorefinery systems depend on reliable feedstock supply, consistent biomass quality, and efficient logistics. An aspect that remains underexplored in biomass-to-biorefinery supply chain optimization is the incorporation of biomass quality uncertainty into decision-making models. Biomass quality characteristics, including ash content, moisture, chemical composition, and dry matter loss, can influence storage, preprocessing, transportation, conversion efficiency, biorefinery yields, process reliability, and overall energy utilization. Although these characteristics are difficult to model due to their spatial, temporal, and operational variability, ignoring their effects can lead to suboptimal supply-chain designs, inaccurate cost estimates, and unrealistic assessments of biorefinery performance. This paper reviews the treatment of biomass quality characteristics in the literature on quantitative modeling and analysis of biomass-to-biorefinery supply chains. Positioned from an Operational Research (OR) perspective, this review emphasizes mathematical modeling, computer simulation, optimization, and decision-support approaches for biomass-to-bioenergy systems. A total of 71 English-language published articles are reviewed and classified according to modeling approach and quality characteristic(s) considered. Across the selected literature that quantified biomass quality effects, cost reductions along supply chain operations ranging from 6% to 31% were reported when quality-aware models were compared with approaches that ignored quality or assumed unrealistic biomass quality characteristics. Despite these findings, biomass quality remains underrepresented in current analytical models; ash content, dry matter loss, and chemical composition were considered in only 10.4%, 4.3%, and 0.9% of the reviewed literature, respectively. This review summarizes the current state of research and outlines a future research agenda for integrating biomass quality control, uncertainty modeling, and optimization into scalable bioenergy and biorefinery systems. Full article

Cyanobacteria are promising platforms for bioenergy, carbon sequestration, and bioproduct synthesis, but their photosynthetic efficiency is limited by suboptimal light utilization, electron transport constraints, and environmental stress. Titanium dioxide nanoparticles (n-TiO₂) have emerged as powerful photocatalytic materials that can enhance light absorption, modulate electron transport, and influence the redox balance in biological systems. This review advances the concept of photocatalytic-biological coupling, in which n-TiO₂ functions as artificial light amplifiers that augment cyanobacterial photosynthesis. Current evidence on the physicochemical properties of n-TiO₂, their interactions with cyanobacterial cells, and their effects on photosystems, electron transport chains, and downstream metabolic processes is examined. Particular emphasis is placed on the integration of photophysical and biological mechanisms, including reactive oxygen species (ROS)-mediated signaling, proton motive force (PMF) enhancement, and adenosine triphosphate (ATP) synthesis. Emerging approaches, including nano–bio interface engineering, environmental biotechnology applications, and artificial intelligence-guided optimization, are highlighted. By bridging photophysics, cellular bioenergetics, and computational design within a unified mechanistic framework, this review establishes the scientific foundation needed to translate photocatalytic–biological coupling into scalable and biotechnologically deployable nano-enabled photosynthetic systems. Full article

The transition from a linear to a circular economic model is critical for the sustainability of the Amazonian bioeconomy. Although Amazon nuts (internationally recognized as Brazil nuts, Bertholletia excelsa) represent a major natural bioresource, their production chain faces structural challenges, particularly concerning the underutilization and disposal of agro-industrial residues. This narrative review analyzes the Amazon nut market, processing technologies, and supply chain, with a specific focus on biomass valorization. By evaluating the recent literature across major scientific databases, this study maps the technological readiness and feasibility of integrating a circular economy model. We critically examine alternative technologies for transforming specific residues, particularly the woody fruit pods, hard seed shells, and oil press cakes, into value-added bioproducts. Key valorization routes discussed include protein concentrates and amino acid supplements from the press cake, cellulose nanocrystals and organic panels from the hard seed shell, biosolvents, and bioenergy/biochar generation via pyrolysis. The review concludes that while the current industry remains strictly focused on kernel commerce, transitioning toward a circular model appears technically promising, but its economic feasibility remains unconfirmed for most valorization pathways. However, successful industrial implementation requires overcoming logistical supply chain barriers and advancing the technology readiness levels of these valorization pathways. Full article

The agricultural and livestock sectors of Latin America produce a large number of residues that could be converted into energy through bioenergy production processes. However, the bioenergy sector still faces several limitations across the region, including fragmented logistics systems, weak coordination among institutions, and limited integration of environmental, digital, and compliance-related performance indicators. This review systematically analyses residue-based bioenergy value chains in Latin America between 2015 and 2025 using the PRISMA methodology to evaluate selected peer-reviewed studies and regional reports indexed in Scopus, ScienceDirect, SpringerLink, and IEEE Xplore, and institutional repositories. The final synthesis included 37 studies and institutional contributions, which were further disaggregated into 208 country–residue observations for the regional and feedstock distribution analysis. The review identified three main research gap categories: the limited integration of collection and logistics systems, the insufficient treatment of uncertainty, circularity, and traceability within optimization models, and the weak incorporation of governance and institutional coordination into bioenergy value-chain design. The analysis includes biogas, biomethane and related residue-based systems, with attention to supply-chain optimization, policy alignment, methane mitigation metrics, and traceability requirements. Results indicate that although technologies such as biomass pretreatment, process intensification, and upgrading processes continue to improve conversion performance, most studies still focus mainly on technical feasibility and biomass potential. Less attention is given to governance constraints, uncertainty analysis, and monitoring systems capable of supporting regulatory compliance. This research introduced the Sustainable Bioenergy Chain Management Framework (SBCMF) to respond to these limitations and bring together different aspects of bioenergy management within one analytical structure. The framework combines supply-chain optimization under spatial and temporal constraints, circular economy valorisation, methane-related climate performance, and digital traceability, while also linking techno-economic system design with governance and monitoring requirements. In this way, it can help support the development of more transparent and low-carbon bioenergy systems across Latin America. Full article

Bio-jet fuels produced from biomass-derived feedstocks represent a promising pathway for reducing the carbon intensity of aviation energy systems. However, designing supply chain networks for bio-jet fuel production requires solving large-scale mixed-integer linear programming (MILP) models that integrate facility location, feedstock allocation, material flows, and routing decisions. These models can become computationally expensive, particularly when evaluating multiple network configurations or large candidate sets of production and processing facilities. This study develops a hybrid machine learning and optimization framework to improve the computational efficiency of bio-jet fuel supply chain network design while preserving high-quality decision outcomes. The proposed iterative procedure uses supervised learning to approximate the relationship between facility location decisions and total supply chain cost. First, an initial set of supply chain configurations is generated by solving the optimization model while using randomly selected facility locations. These solutions are then used to train predictive models, including ridge regression, feed-forward neural networks, and ensemble neural networks, with facility-location configurations as inputs and total supply chain cost as the output. The trained learner is subsequently used to identify promising facility-location candidates through Thompson sampling and small-scale linear programming. These candidate solutions are evaluated by the original mixed-integer model, and the resulting observations are fed back into the learning process until convergence. Numerical experiments show that the proposed hybrid approach obtains near-optimal bio-jet fuel supply chain designs while substantially reducing computational time. For the linear case, the method achieves solutions within 0.23–0.29% of the objective function value while reducing computational time by 70.95–81.95%. For nonlinear learning models, the optimality gap decreases further to 0.13–0.15%, with computational time reductions of 45.37–60.36%. For the Texas case study and the modeling assumptions evaluated, the findings demonstrate that machine-learning-assisted optimization can reduce computational effort while preserving high-quality supply chain solutions. The extent of these benefits may vary with network size, candidate-facility structure, facility-capacity assumptions, demand characteristics, and the amount of information available to train the learning models. Full article

The agri-food sector generates significant quantities of by-products and waste biomass, representing both an environmental challenge and an opportunity for resource recovery within a circular bioeconomy. This study developed a decision-support framework for the sustainable valorization of major residues from the Italian agri-food sector. Following a Material Flow Analysis (MFA) of multiple Italian agri-food supply chains, tomato and grape supply chains were selected as representative case studies because they generated the largest quantities of valorizable by-products among the agri-food sectors evaluated, namely tomato pomace and grape marc. MFA based on FAOSTAT and PRODCOM data (2019–2023) was combined with a literature review to assess biomass availability and physicochemical characteristics. The analysis identified tomato pomace and grape marc as the predominant residues. Tomato pomace showed high moisture content (63.34%), balanced organic composition, and a favorable C/N ratio, supporting its suitability for anaerobic digestion with an average biomethane potential (BMP) of 0.143 m³ CH₄/kg TVS. Conversely, grape marc exhibited a carbon-rich lignocellulosic structure, with a carbon content of 47.67%, C/N ratio of 24.73, and BMP of 0.195 m³ CH₄/kg TVS, favoring thermochemical conversion pathways, particularly pyrolysis. The framework also highlights the importance of cascading strategies, prioritizing the recovery of high-value compounds, and pretreatment approaches to improve biomass conversion efficiency. Overall, this approach supports optimized resource recovery and sustainable agri-food waste management. Full article

Ukraine’s energy strategy under martial law necessitates decentralized local energy systems to counter electricity shortages and systemic infrastructure failures. The study develops and validates an optimization model for designing the architecture of decentralized “energy islands” based on circular bioenergy models for agricultural waste use. Empirical verification was conducted using data from the Vinnytsia region in Ukraine. The model accounts for a multi-level structure that separates micro/small generation (0.1–2.0 MW) from medium generation (1–20 MW) based on the logistical radius for raw material collection. The model incorporated the Value of Lost Load (VLL), enabling the monetization of avoided socio-economic losses from energy shortages. In addition, the coefficient of energy island sustainability (I_sred) was introduced to quantitatively assess the effectiveness of investments in terms of replacing external resources. The modeling revealed the nonlinear nature of the total cost function, enabling us to determine an optimal energy-autonomy range of 40% to 50% for communities. At this threshold, the total construction and logistics costs are minimized. The potential socio-economic losses from blackouts are effectively mitigated, as confirmed by the calculated sustainability coefficient (I_sred), which ranges from 0.78 to 0.94 across the studied communities. The resource potential assessment confirms that the region’s total potential is approaching 30 million tons of oil equivalent, driven by solid biofuels, agricultural residues, and energy crops (miscanthus, switchgrass). The classification of biomass supply chains shows that exceeding the transportation radius by more than 70 km at the meso level, or deviating from the optimal logistics lever by 20%, reduces the profitability of projects below the critical limit of 15%, which justifies strict localization within raw-material clusters. This enables local communities to eliminate natural gas consumption, reduce energy supply operating costs by 15%, and ensure the autonomous and stable operation of critical infrastructure facilities during prolonged disruptions to the national power grid. Full article

Forest bioenergy holds significant potential for North American decarbonization and energy security, yet persistently high logistics costs, feedstock quality variability, and geographic dispersion of biomass resources continue to constrain commercial viability. This review asks what it will take for forest bioenergy supply chains to achieve economic and operational lift-off, identifying key bottlenecks and the most promising pathways to scale. We systematically review 237 peer-reviewed studies and technical reports with the majority published between 2000 and 2025, covering feedstock types ranging from logging residues and woody biomass to short rotation woody crops, and end-products spanning solid biofuels, heat and power, thermochemical products, and sustainable aviation fuel. The literature consistently identifies delivered cost, feedstock quality control, and the geographic mismatch between biomass supply and conversion facility location as the three primary barriers to sector viability. Depot-based preprocessing, cascading utilization strategies, and participatory landowner contracting emerge as the most effective near-term solutions for improving supply chain economics and mobilizing economically recoverable biomass. At the frontier, AI-enabled optimization, digital twin modeling, and integrated biorefinery configurations show strong potential to manage spatial variability and unlock the scale economies on which commercial viability depends. Translating these advances into practice will require stable, long-term policy signals and coordinated investment across the full supply chain. Full article

Eucalypt plantations have expanded across tropical, subtropical, and temperate regions and now play an important role in the global supply of wood and renewable biomass, while remaining at the center of debates on water use, biodiversity, and socio-economic trade-offs. This review examines whether these plantations can deliver ecological, social, and technological benefits under appropriate management. This review synthesizes evidence from nearly 200 peer-reviewed papers, technical reports, and books covering environmental services, livelihood outcomes, and emerging bio-based applications of Eucalyptus species. The literature shows that well-planned plantations can deliver clear benefits. High biomass production supports carbon sequestration, while improvements in soil structure, nutrient cycling, and the recovery of degraded lands are frequently reported. Effects on water, often described in general terms as negative, vary widely with climate, soils, stand age, and previous land use, and are documented to play roles in biodrainage, salinity control, erosion reduction, and local microclimate regulation under suitable conditions. From a socio-economic perspective, Eucalyptus, a widely planted species, supports rural development by generating income, strengthening value chains for wood products and bioenergy, and offering smallholders a fast-growing resource. Technological work on materials and bioproducts, including nanocellulose, essential-oil formulations, biochar-based applications, and wood vinegar, further illustrates this versatility. Overall, while outcomes remain site-specific and dependent on governance, the evidence indicates that, under science-based management and careful landscape planning, eucalypt plantations can contribute to climate mitigation, rural livelihoods, and the circular bioeconomy. Full article

Bioenergy production from agro-industrial waste has the potential to contribute to climate change mitigation. In Brazil, the pequi (Caryocar brasiliense Camb.) production chain makes an economic, environmental, and social contribution. However, the collection and processing of the fruit produce large amounts of waste, such as the peel, whose improper disposal leads to significant environmental impacts. This study evaluated how moisture and carbonization temperature influence the energy properties of charcoal briquettes made from pequi peel waste. Carbonization was performed at two final temperatures (360 °C/480 °C) with a heating rate of 1.5 °C min⁻¹ and residence times of 4 h and 5 h 20 min, respectively. Carbonization yields were calculated based on dry mass. Briquettes were produced from pequi peel at moisture contents of 5%, 7.5%, and 10% (wet basis). After carbonization, the charcoal briquette samples were characterized by proximate analysis, higher heating value (HHV), bulk density, energy density, and mechanical durability. Carbonization temperature exerted a more pronounced effect on the properties of the carbonized briquettes than the initial moisture content. Carbonization at 480 °C increased the fixed carbon content (76.38%, 74.25%, and 75.10% for treatments 1, 2, and 3) and the HHV (25.10–25.31 MJ kg⁻¹), while reducing the gravimetric yield (32.84–33.25%). The influence of moisture content was more evident in carbonizations carried out at 360 °C, indicating a temperature-dependent interaction. The use of pequi peel for solid biofuel production promotes the valorization of agro-industrial residues and supports strategies aimed at the circular bioeconomy and the decarbonization of the energy matrix. Full article

The growing demand for sustainable energy sources has intensified research on the valorization of biomass residues as feedstocks for energy production. This scoping review provides a comprehensive analysis of recent technological approaches for converting coconut and açaí residues into energy carriers and bioenergy products. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In addition to synthesizing the existing literature, this study evaluates the technology readiness level (TRL) of the reported conversion pathways based on the experimental evidence provided in the reviewed studies. The literature search was conducted using Scopus, Web of Science, and ScienceDirect, focusing on peer-reviewed publications between 2015 and 2025 that reported experimental or pilot-scale research on thermochemical, chemical, and physical conversion processes for coconut and açaí residues. The TRL assessment indicates that most technologies remain at laboratory validation stages, with only a limited number reaching pilot or prototype demonstration levels. Nevertheless, several pathways—particularly thermochemical and densification processes—show promising potential for decentralized bioenergy applications. These findings are especially relevant for regions where coconut and açaí value chains generate significant volumes of agricultural residues. Their valorization could support decentralized energy systems, improve residue management, and contribute to sustainable bioeconomy strategies. Overall, this review identifies the main technological advances, limitations, and research gaps associated with the energy conversion of coconut and açaí residues, providing insights for future technological development and deployment. Full article

The objective of this study was to evaluate the impact of seasonal changes in the chemical and structural composition of giant miscanthus (Miscanthus × giganteus) biomass on the performance, kinetics, and efficiency of anaerobic digestion (AD), as well as on the overall energy and techno-economic balance of the conversion chain. The AD performance was assessed using batch biochemical methane potential (BMP) assays conducted for eight harvest dates (June–January). Comprehensive characterization included fundamental physicochemical properties of the biomass, lignocellulosic fraction composition, AD kinetics, and methane production yield. A statistically significant (p < 0.05) increase in structural fiber fractions was observed with advancing plant maturity, accompanied by a progressive decline in specific methane yield from 281 ± 32 mL CH₄/g VS in June to 170 ± 11–172 ± 13 mL CH₄/g VS in winter harvests. Despite a relatively stable theoretical biochemical methane potential (TBMP) ranging from 425 to 443 mL CH₄/g VS, the conversion efficiency (BMP/TBMP) decreased from approximately 66% to below 40%, indicating increasing structural and kinetic limitations to substrate biodegradability. Kinetic parameters deteriorated systematically in late harvests, as reflected by a reduction in the first-order rate constant k_CH₄ from 0.115 to approximately 0.072 1/d and an extension of the lag phase λ from 2.19 to over 4 days. Regression analysis revealed strong negative correlations between lignocellulosic complex content and both BMP and k_CH₄, whereas the C/N ratio exhibited a positive association with process performance under the experimental conditions applied. The highest methane production per hectare (3904 ± 720 m³CH₄/ha) and the most favorable economic outcome (1979 ± 465 EUR/ha) were achieved for the September harvest. The results demonstrate that harvest timing constitutes a critical optimization parameter in lignocellulosic biogas systems, governing not only methane yield and process kinetics but also the overall energy output and economic viability of the bioenergy production chain. Full article

Biomass combustion, as a key technology for achieving a low-carbon transformation of the energy system, faces multiple challenges in its efficient and clean utilization, including the high heterogeneity of fuels, the complex multi-scale coupling of the combustion process, and the attainment of ultra-low emissions. Traditional research methods have significant disconnections between microscopic mechanism understanding, macroscopic performance prediction of reactors, and end-of-pipe pollution control, which restricts the improvement of system performance. This review presents recent advances in advanced numerical simulation, pollutant control strategies, and bioenergy with carbon capture and storage (BECCS) pathways targeting ultra-low emissions in biomass combustion. This work synthesizes progress across three interconnected domains. First, methodologies are examined for integrating detailed chemical kinetics, particle-scale models, and reactor-scale simulations to develop high-fidelity predictive tools. Second, low-nitrogen combustion and synergistic pollutant control strategies for primary furnace types (e.g., grate, fluidized bed) are evaluated, alongside process optimization from fuel pretreatment to flue gas purification. Third, the potential for integrated design of biomass energy systems with carbon capture is assessed, emphasizing that system efficiency hinges on holistic “fuel-combustion-capture” chain optimization rather than isolated unit improvements. Future research directions are highlighted, including the development of physics-informed AI modeling paradigms, deeper co-design of multiple processes, and the establishment of robust life-cycle assessment frameworks. This review aims to provide a structured reference to inform both fundamental research and the practical development of next-generation clean biomass combustion technologies. Full article

In recent decades, microalgae biodiesel has been repeatedly presented as a fundamental pillar of future bioenergy systems in relation to fossil diesel. This is largely due to the high photosynthetic efficiency of microalgae, their high growth rates, and their ability to accumulate neutral lipids—particularly triacylglycerols (TAGs)—which constitute the main raw materials for biodiesel production. However, this route has not yet become economically competitive with conventional fuels and vegetable oils. In this context, the simultaneous increase in biomass productivity and TAG content remains essential to reduce the cost difference, but achieving these goals depends on a detailed understanding of lipid metabolism and its regulation under different environmental and nutritional conditions—and on overcoming the intrinsic trade-offs between growth and storage. Thus, this article aims to critically analyze the viability of microalgae biodiesel, seeking to identify the main factors that explain why this route has not yet become competitive with conventional fuels after decades of research. In parallel, the growing trend of multi-product microalgae biorefineries is examined, highlighting bottlenecks in downstream processing and product purification, as well as the inherent trade-offs between production strategies. Practical limitations related to biomass productivity per area, culture dilution, intracellular lipid storage, and vital steps such as transesterification are also discussed, which together impose high energy and operational penalties throughout the production chain. Finally, emerging trends and integrated approaches are discussed, with emphasis on strain and process co-optimization, as well as greater integration between cultivation and downstream operations, aiming to enable more efficient and realistically consistent microalgae biodiesel concepts. Full article