BioEnergy Research最新文献

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.

This study evaluated the potential of mixed microalgae-bacteria cultures as electroactive microorganisms to enhance electricity production while simultaneously treating wastewater in emerging biophotovoltaic (BPV) systems. Graphite felt decorated with TiO₂ nanotubes (TiO₂-NTs) was used as the anode, supporting the growth of a biofilm composed of mixed microalgae-bacterial cultures. Batch BPV reactors treated real domestic-industrial wastewater and were compared with BPV systems using bare graphite felt anodes, both with and without microalgae-bacteria cultures. All BPVs operated under a 12-h light/dark cycle, with continuous current monitoring and water quality assessments performed at the beginning and end of the experiments. The TiO₂-NT BPV exhibited the highest power output, reaching 2.37 mW/m². BPVs with bare graphite felt anodes achieved power densities of 1.48 mW/m² and 0.41 mW/m² with and without microalgae, respectively. Systems inoculated with microalgae-bacteria cultures also showed lower ohmic losses, reaching open circuit voltages between 0.125 and 0.130 V, whereas systems without microalgae reached only 0.050 V and exhibited faster potential drops. In terms of wastewater treatment, TiO₂-NT BPVs achieved the highest removal efficiencies for chemical oxygen demand (COD) and nitrogen, exceeding 90% COD removal and over 80% removal of total ammoniacal nitrogen and nitrate. This study demonstrates that integrating mixed microalgae-bacteria cultures with TiO₂ nanostructured anodes in multifunctional BPV systems significantly enhances both wastewater treatment performance and power generation.

The growing demand for sustainable energy calls for efficient and accurate methods to optimize biofuel production processes. Hydrothermal liquefaction (HTL) is a promising thermochemical technique to convert wet biomass into biocrude oil, but estimating yield across diverse feedstocks and conditions remains challenging. In this study, we develop and benchmark a series of machine learning models to predict biocrude oil yield from HTL, using a comprehensive dataset of 650 biomass samples and process parameters, including elemental composition and higher heating value (HHV). Notably, this is the first study to incorporate HHV as a predictive feature at this scale. Seven ML models—including XGBoost, Random Forest, and Gaussian Process Regressor—were optimized via Bayesian hyperparameter tuning and evaluated through a dual-validation strategy combining tenfold cross-validation with a hold-out test set. XGBoost achieved the highest performance (R² = 0.97, RMSE = 0.033). To ensure model interpretability, SHAP and SAGE techniques were applied, identifying HHV, carbon content, and pressure as key yield predictors. These results provide a transparent, data-driven framework for enhancing reactor design and feedstock selection in bio-oil production systems. The study underscores the potential of interpretable ML in advancing the predictive capabilities of renewable fuel technologies.

The production of biodiesel and valuable products from ground safflower seed using supercritical methanol was investigated, focusing on optimizing biodiesel production through response surface methodology (RSM). It was determined that the optimal conditions for maximizing fatty acid methyl esters (FAMEs) production were 250 °C, 0.25 g ground safflower seed, and 29 min reaction time, achieving a 74% yield. In addition to FAMEs, the reaction resulted in the formation of oxygenated compounds, hydrocarbons, and aromatics, with maximum yields of 56%, 39.3%, and 7.19%, respectively, under different reaction conditions (200 °C, 0.1 g catalyst, 40 min for oxygenates and aromatics, and 200 °C, 0.5 g catalyst, 40 min for hydrocarbons). In the presence of ZnO nanoparticles, the reaction conditions that maximized the yield of FAMEs resulted in a 7% reduction in biodiesel yield, a 52% increase in the yield of hydrocarbons, and an 85% increase in the yield of oxygenates, while there was no apparent effect on the yield of aromatics. The results highlighted the complex role of ZnO in the supercritical methanol process, where it can inhibit biodiesel production while simultaneously aiding in the conversion of biomass into hydrocarbons and oxygenated compounds.

Managing cow manure is a great challenge for European countries such as Sweden, given the growth of livestock farming. Land return, biofuel, and biochar may be considered the three principal measures for cow manure management in recent years. However, a knowledge gap remains in understanding the environmental and economic impacts of these utilization methods in Sweden. To address this issue, this study assesses the environmental impacts and economic benefits of land return, biofuels, and biochar in Sweden. It employs a combination of life cycle assessment (LCA) and life cycle costing (LCC). The analysis was performed using SimaPro 9.0.0.48 software with the Ecoinvent 3 database, following the ISO 14040 and 14044 standards. The study focused on Halland, Sweden. The results showed that composted land return is the most promising technology for utilization of cow manure in Sweden, for its greatest potential for greenhouse gas emission reduction, i.e., −54.47 kg CO₂ eq per ton of cow manure utilized, and relatively low cost, with a total cost of processing 1 tonne of cow manure is 940.04 SEK. The cow manure-to-biofuel deserves to achieve the lowest cost with a cost value per ton of cow manure of 666.58 SEK and GHG reduction potential being −10.73 kg CO₂-eq per ton of cow manure. Biochar has the highest total cost and high GHG emissions. The latter is mainly due to studies that fail to account for biochar’s potential to sequester soil carbon.

Biogas, sourced from livestock waste, wastewater treatment plants, and municipal solid waste, offers a promising feedstock for hydrogen production through chemical looping processes. This study focuses on optimizing a Chemical Looping Reforming system for hydrogen production, using different biogas compositions and enhancing efficiency in the Aspen Plus (software). The goal is to maximize hydrogen production while minimizing CO₂ emissions. A sensitivity analysis evaluated the impact of variables such as biogas composition, temperature, water flow, and oxygen carrier quantity. Results show that higher CH₄ fractions in biogas are more favorable for hydrogen production, with an optimal CH₄/CO₂ ratio of 70/30%. The highest H₂ production was achieved at a reactor temperature of 600 °C, with flows of H₂O, CaO, and NiO set at 4 mol/h, 1.55 mol/h, and 1 mol/h per 1.602 mol/h of biogas feed. The optimized model produced 3.434 mol/h of H₂, with 99.2% CH₄ conversion and 98.5% H₂ purity. Implementing this model at full scale, such as at the Aguas Claras treatment plant (Medellin, Colombia), shows the potential for H₂ production, with an estimated output of 1367 tonnes H₂ year⁻¹. Beyond technical optimization, a preliminary techno-economic analysis was conducted, estimating a levelized cost of hydrogen of $2.99/kg and identifying a minimum hydrogen selling price of $4.75/kg for economic viability. These results demonstrate that biogas-fed CLR systems are technically feasible and approach commercial competitiveness at scale, particularly when low-cost feedstock and economies of scale are secured.

Lignocellulosic biomass pretreatment is crucial to overcoming its recalcitrance to enzymatic hydrolysis. This study investigated the influence of acid-catalyzed IVDV (Intensification of Vaporization by Decompression to the Vacuum) pretreatment on industrial hemp hurds (IHH) and its impact on enzymatic saccharification. IVDV conditions were optimized using a central composite design (CCD), varying sulfuric acid concentration (0.3–1.7% w/w), processing pressure (0.36–1.04 MPa), and processing time (8–24 min). Pretreatment efficiency was assessed through lignin removal (LR) and microstructural changes (A_BET analysis, SEM, FTIR), with overall reducing sugar yield (ORS) determining enzymatic hydrolysis efficiency. FTIR analysis showed that lignin and Hemicellulose linkages were altered, while cellulose linkages remained unaffected even at 1.04 MPa or 1.7% acid. A maximal A_BET of 1.78 m²/g and LR of 79.6% were achieved at 1.04 MPa and 1.7% H₂SO₄, respectively, for 16 min. However, these maximums did not coincide with the maximum ORS of 92.8%, which was obtained at an IVDV pressure of 0.90 MPa, time of 11.2 min, and acid concentration of 0.6%, suggesting that sugar degradation occurred at high treatment severities.

A non-isothermal thermogravimetric analysis was performed on hydrochars produced from sugarcane green harvesting residues (GHR) to assess their combustion reactivity. The Coats–Redfern method was employed to simulate conditions, determining kinetic parameters such as activation energy, pre-exponential factor, and reaction order, and assessing the reactivity of the hydrochars. Additionally, characteristic temperatures (ignition, peak, and burning) and combustion indices were identified. The hydrochars were produced at a rate of 12 °C/min with an airflow of 100 mL/min, reaching a maximum temperature of 900 °C. Results indicate that hydrochars produced at 300 °C with H₂O/GHR ratios of 5:1 and 10:1 exhibit significantly elevated peak decomposition temperatures (447.1 °C and 465.4 °C, respectively) compared to 324.6 °C for the raw GHR, reflecting enhanced thermal stability. These hydrochars also showed a marked reduction in combustion reactivity indices—Di decreased from 12.3 to 3.78, Sn from 49.7 to 7.58, and Cx from 13.7 to 3.64—when compared to those produced at 200 °C. Furthermore, the activation energy increased substantially at 300 °C (40.92 and 38.00 kJ/mol) relative to raw GHR (21.05 kJ/mol) and HTC at 200 °C (≈25 kJ/mol), accompanied by higher pre-exponential factors, particularly for H–300–5 (31.25 min^-1), indicating a shift in combustion kinetics and the formation of more recalcitrant, carbon-rich structures at higher HTC severity.

Lignocellulosic biomass is a valuable and renewable resource for the sustainable production of biofuels and high-value chemicals. However, its rigid and recalcitrant structure presents significant challenges to efficient conversion. Traditional pretreatment methods, such as acid and alkaline hydrolysis, are commonly used to disrupt the biomass matrix but are often associated with environmental concerns and equipment corrosion due to the use of harsh chemicals. In contrast, subcritical water hydrolysis has emerged as a green and efficient alternative, utilizing only water under controlled temperature and pressure to achieve effective biomass deconstruction. In this review, subcritical water was applied directly to lignocellulosic feedstocks without the need for corrosive chemicals, enabling the selective breakdown of hemicellulose and cellulose and promoting the generation of simple sugars and other bioactive compounds in an eco-friendly manner. Recent advances in the integration of subcritical water with modern extraction technologies, such as ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and pressurized liquid extraction (PLE), are also discussed. The synergistic combination of these techniques offers enhanced efficiency, reduced processing time, and improved recovery of valuable compounds.

Forest-based biomass, which mainly includes harvesting and wood processing residues, has great potential for producing bioproducts and transitioning to a bio-based economy. However, the commercial utilization of this material is challenging due to various factors, including supply chain barriers, which motivated the optimization of biomass supply chains in many studies. Government policies have also been instrumental in the feasibility of bio-projects and were considered in recent studies. The purpose of this review is to analyze the literature on forest-based biomass supply chain optimization, investigating how governmental policies are taken into account and their potential impacts. Both bibliographic and descriptive review analyses are conducted on 30 research articles published on this subject. This study employs a scientific mapping approach that includes bibliographic coupling analysis of sources, as well as keyword co-occurrence analysis, using the Visualization of Similarities (VOS) viewer software. Furthermore, the papers are grouped and analyzed based on the types of policy they considered and how uncertainties in supply chains were addressed. The findings show that few studies incorporated governmental policies into mathematical programming models developed for forest-based biomass supply chain planning. Among the studies that did consider these policies, most focused only on a single policy group, particularly carbon pricing policies. Moreover, the impact of uncertainties, especially those related to governmental policies, has been largely overlooked in these models. To advance this area of research, future research can address these gaps, incorporating a broader range of policies and the associated uncertainties.