BioEnergy Research最新文献

The Indonesian government has consistently expressed its commitment to mitigating greenhouse gas emissions, notably through the partial substitution of fossil fuels with biofuels to increase the share of renewables in the national energy mix. Among the measures introduced is the E5 program, mandating a 5% ethanol blend in gasoline, although its implementation has encountered several technical and logistical barriers. This study examines the feasibility of integrating bioethanol production into the biogas value chain by comparing three development scenarios. The baseline scenario (SC BaU) assumes a stand-alone bioethanol production facility. Scenario 2 considers a biorefinery configuration utilizing wastewater for electricity generation, with two operational sub-cases. SC BIO-TRADE explores the conversion of wastewater into biomethane for commercial distribution. The economic evaluation identifies SC BIO-TRADE as the most feasible option, owing to its relatively lower capital investment of USD 255,314,671 and a projected average annual revenue of USD 126,152,277. At a selling price of USD 6 per MMBTU, this pathway achieves an internal rate of return (IRR) of 14%, outperforming the other scenarios. Moreover, SC BIO-TRADE is particularly suitable for deployment in industrial zones where a reliable gas supply is critical for sustaining production activities.

Graphical Abstract

Soybean is widely used to produce edible vegetable oils, accounting for 57% of their global production. Soybean hull (SH), a byproduct of this chain, has high polysaccharide content and lower recalcitrance compared to other lignocellulosic biomasses. To enhance oil production in the soybean industrial chain, the present study evaluated an SH enzymatic hydrolysate as a substrate for Rhodosporidium toruloides growth, whose metabolism promotes intracellular lipid accumulation under stress conditions. For the first time, NaCl-induced osmotic stress was evaluated to enhance lipid accumulation in lignocellulosic hydrolysate fermentation by R. toruloides. Adding 1% NaCl led to 36 ± 0.98% lipid accumulation, versus 26 ± 1.72% under unstressed conditions. A two-stage fermentation strategy separating growth and production phases was then applied, yielding a maximum lipid concentration of 5.96 ± 0.55 g/L, and improving lipid yield from 0.096 ± 0.006 to 0.115 ± 0.012 g/g. This strategy revealed significant yield differences between stages (0.102 ± 0.0008 g/g in the first stage, 0.134 ± 0.014 g/g in the second stage), indicating that NaCl supplementation enhanced lipid biosynthesis over biomass production. Fatty acid methyl esters analysis revealed palmitic, stearic, oleic, linoleic, and α-linoleic acids as predominant, aligning with biodiesel requirements. With SH availability estimated at 21.0–33.7 million tons annually, converting only 1% could yield 8204–13,167 tons of microbial lipids annually. This study demonstrates the potential of SH as a suitable substrate for microbial lipids, providing crucial data for novel cultivation and scaling up strategies, aiming to enhance lipid productivity.

Palm oil, known for its high energy density and wide availability, is a promising resource for sustainable biodiesel production. Biodiesel, derived from renewable sources such as vegetable oils or animal fats, emits fewer pollutants and contributes to reducing environmental issues related to air pollution and climate change. This paper introduces an intelligent, optimized system for estimating biodiesel yield using IoT-based imagery and machine learning (ML) techniques. The proposed system operates in two main phases, which are palm-oil tree detection and biodiesel yield prediction. In the initial phase, palm-oil trees are identified and counted based on input images using the YOLOv9 object detection algorithm. To assess YOLOv9 in various configurations versusYOLOv8, three experiments were carried out. With high-resolution input, YOLOv9 produced the best results with 100% precision and recall and 99.5% mAP50-95. In the second phase, biodiesel yield is predicted using an optimized gradient boosting model based on environmental variables like temperature, humidity, rainfall, and wind speed. With an R² value of 0.98 and RMSE and MSE values close to zero, the system exhibits high inference quality and a fast inference time of 0.00297 s. The efficacy of the system under various environmental conditions was confirmed by a real-world case study which also confirmed that the system can accurately estimate the production of biodiesel. This system shows how ML and IoT integration can improve the efficiency of biodiesel production providing a scalable and dependable solution for the development of sustainable energy.

This study examines the pyrolysis characteristics of wood apple shell as a sustainable biomass feedstock through morphological, elemental, thermal, calorific, and statistical analysis. Scanning electron microscopy revealed that granulated samples exhibited uniform morphology with larger particle sizes and stable porosity, favouring consistent thermal degradation. In contrast, powdered samples displayed irregular particle distribution and higher pore sizes, contributing to faster decomposition. Energy dispersive X-ray spectroscopy confirmed high carbon content in both forms, with powdered samples reaching 72.6% and granulated samples 62.5%, while oxygen ranged from 26.68% to 38.14%. The carbon-to-oxygen ratios were 2.72 for powdered and 1.62 for granulated, directly influencing volatile release and energy yield during pyrolysis. Fourier transform infrared spectroscopy identified hydroxyl, carbonyl, and amine groups favourable for bio-oil and biochar production. Thermogravimetric analysis indicated major mass loss between 250 to 450°C, associated with hemicellulose, cellulose, and lignin degradation, with a peak decomposition rate at 385°C. Higher heating values, calculated from elemental and proximate data, ranged from 6.54 to 7.54 kWh/kg, with powdered samples showing higher conversion efficiency. Statistical validation using Welch’s t-test confirmed significant differences (p < 0.05) between powdered and granulated forms, reinforcing the reliability of the observed trends. The results suggest that powdered samples are advantageous for rapid decomposition and higher energy output, while granulated forms provide structural stability and sustained energy release. Overall, the findings highlight the complementary roles of particle size and feedstock form in optimizing pyrolysis pathways, with granulated wood apple shells being particularly suited for controlled and stable bio-oil production.

This work deals with the modeling of the enzymatic hydrolysis of pretreated sugarcane bagasse (SB) for fermentable sugars production, where the response surface methodology (RSM) and the adaptive neuro-fuzzy inference system (ANFIS) approach were evaluated. Fuzzy logic is one of the many techniques used by artificial intelligence, which seeks to create intelligent systems capable of solving complex problems and learning from available information. Enzymatic hydrolysis (pH 5.0) of pretreated SB was performed at laboratory (bottles) using commercial cellulase (Sigma, obtained from A. niger with activity of 1.47 U.mg^− 1) in a shaker incubator with 120 rpm and 50 °C. Initially, the RSM was used for evaluating the effects of three variables of hydrolysis and subsequently, ANFIS was tested. The input variables considered in the models were hydrolysis time (t), enzyme concentration (E), and substrate concentration (S), while the yield of sugars (glucose) served as the response (output) variable. The RSM modeling showed a good fitting in this work (R² = 0.9859). The ANFIS tool efficiently predicted the glucose yield (R² = 0.9992). The optimal response, achieving a glucose yield of 25.0 g L^− 1 occurred at process settings of t = 60 h, E = 3.3%, and S = 23.3 g L^− 1. In conclusion, the ANFIS methodology represents an interesting alternative for modeling of complex chemical processes, especially in those cases where RSM falls short in achieving satisfactory results in terms of model fitting.

This study assesses the economic viability of sustainable forest management plans (SFMPs) for fuelwood production in a semi-arid region of Brazil through a probabilistic assessment, offering insights for financially viable and environmentally sustainable production. Data from operational SFMPs in Pernambuco were used to parameterize the analysis. Net Present Value (NPV), Internal Rate of Return (IRR), Uniform Annual Equivalent Value (UAEV), and Discounted Payback were used as feasibility criteria. After the deterministic analysis, a sensitivity analysis was conducted to determine the riskiest variables, followed by a Monte Carlo simulation to quantify associated risks and uncertainties. The deterministic results showed an average NPV of R$527,101.28 and an IRR of 36.56% in a scenario considering land costs. The average probability of viability was 78.43% and 84.65% for the scenarios with and without land costs. Large properties showed higher returns and a payback period of only two years, highlighting economies of scale. The price of firewood was identified as a critical variable for the attractiveness of the projects. These findings offer concrete support for producers and policymakers, highlighting opportunities to strengthen the regional economy and sustainably expand the use of firewood for energy.

The escalating demand for sustainable and carbon-neutral energy sources has intensified research into biodiesel production from renewable feedstocks using environmentally friendly catalysts. This study presents the synthesis and application of a low-cost solid acid catalyst derived from tomato stalk, a widely available agricultural waste, for biodiesel production from microalgal oil. The catalyst was prepared via carbonization and subsequent sulfonation, and its physicochemical characteristics were thoroughly evaluated. The highest sulfonic acid density was achieved at a carbonization temperature of 500 °C and sulfonation at 100 °C for 5 h. Under the conditions of 9:1 methanol/oil molar ratio, 5 wt% catalyst and 60 min resulted with a biodiesel yield of 93%. Statistical analysis confirmed the significant influence of carbonization parameters on catalytic efficiency. Compared to conventional homogeneous acid catalysts, produced biochar based solid acid catalyst offers a greener and reusable alternative for biodiesel synthesis. Moreover, the valorization of tomato stalk waste into high-performance catalysts aligns with circular bioeconomy principles, addressing both agricultural waste management and renewable fuel production. This work shows a promising route toward overcoming key economic and ecological barriers in biofuel technology, thereby contributing to the broader transition toward sustainable energy systems.

This study aimed to assess the environmental impact of converting biogas to biomethane using ex situ, hydrogenotrophic methods with hydrogen (H₂) sourced from alternative production lines. The base scenario used electricity from the national grid, while scenarios 1 and 2 used solar power via photovoltaic panels and wind power, respectively. A life cycle analysis (LCA) was conducted for the different energy source scenarios. The results were evaluated using the ReCiPe 2016 Midpoint (H) impact assessment method, which considers 18 impact categories. A model was developed based on production data from a real biogas plant in Izmir, for which activity data were obtained directly from the plant’s logs. The LCA analysis revealed that producing hydrogen from grid electricity resulted in 10.3 kg of CO₂ equivalent in the climate change category, a figure that decreased to 1.27 kg of CO₂ equivalent when solar energy was used instead. Overall, the carbon footprint decreased from 8.66 to − 0.494 kg CO₂ eq. In the second scenario, hydrogen was produced using wind energy rather than grid electricity. The analysis indicated that producing hydrogen from grid electricity resulted in 10.3 kg CO₂ eq in the climate change category, whereas using wind energy reduced this figure to 0.199 kg CO₂ eq. The LCA results demonstrate that energy sources and electricity demand play a crucial role in determining GHG emissions and that the LCA can assist companies and governments in decision-making and policy development.

Discharging untreated industrial or municipal wastewater containing high levels of nutrients, such as nitrogen and phosphorus, into aquatic systems poses significant environmental concerns such as eutrophication. In addition, the urgent need to replace fossil fuels with renewable energy sources is crucial for achieving sustainable development. Microbial fuel cells (MFCs) provide an environmentally sustainable approach by utilizing microorganism metabolisms for bioelectricity generation and wastewater treatment. Consequently, MFC technology holds great promise for addressing water and energy issues. MFCs are bioelectrochemical hybrid systems that integrate bioelectricity generation, wastewater treatment, fouling mitigation, and water desalination. Despite the growing interest in MFCs, a critical gap remains in understanding how recent innovations in hybrid configurations impact performance and scalability. This review provides a novel perspective by analyzing the latest technological advancements and their role in overcoming key operational challenges. Recent trends in hybrid systems, such as UASB-MFC (up-flow anaerobic bed-MFC), EMBR-MFC (electro-membrane bioreactor-MFC), CW-MFC (constructed wetland-MFC), and MDC (microbial desalination cell), are thoroughly described. This study highlights the importance of MFCs in providing sustainable, clean, and renewable systems for bioenergy generation and wastewater treatment. Furthermore, it identifies critical knowledge gaps and proposes targeted future research directions to optimize power output, improve efficiency, and enhance long-term system performance. By addressing these gaps, this review contributes to advancing MFC technology towards real-world applications in sustainable energy and wastewater management.

As the global demand for fossil-based products increases, biomass offers a sustainable and renewable alternative. However, its utilization faces significant supply chain challenges. The biomass supply chain (BSC) encompasses harvesting, collection, transportation, storage, preprocessing, production, and delivery of bio-products. High moisture content and low calorific value of biomass result in high cost of logistics, and consequently high cost of delivered biomass. Other challenges in BSC are related to uncertainties and variations in biomass availability and quality, weather conditions, demand, prices, costs, and policies. Integrating strategic, tactical, and operational decisions is essential to ensure that high-level plans are implementable at lower levels; however, accounting for uncertainties in such integrated decision-making models requires advanced techniques. This paper reviews existing studies on hybrid simulation optimization techniques—specifically the integration of simulation models (e.g., Discrete Event Simulation) with optimization approaches (e.g., mixed integer linear programming)) in BSC management, focusing on forest-based, agricultural BSC planning, and biomass to biofuel/bioenergy supply chain planning. The studies are further categorized into three subgroups based on their use of hybrid models: (1) manage uncertainties, (2) tackle large-scale problems, and (3) interpret complex interdependencies. We analyzed 31 articles published till July 2025 using a systematic approach that combines bibliometric and descriptive analyses. The most commonly applied simulation and optimization approaches were Monte Carlo, discrete event simulation, mixed integer linear programming, and stochastic modeling. Future research could focus on developing multi-objective hybrid models to address sustainability, using machine learning techniques to address uncertainties, and considering relevant governmental policies in the models. Emphasis on resiliency and use of agent-based simulation can enhance decision-making and sustainability.