Anaerobic digestion of the organic fraction of municipal solid waste (OFMSW) is a promising strategy for sustainable waste management and renewable energy generation in small municipalities. This study evaluated the biochemical methane potential (BMP) and biogas yield of OFMSW from five municipalities in southern Brazil sharing a regional landfill. Composite samples were analyzed for total solids, volatile solids, and soluble chemical oxygen demand (sCOD), and elemental composition including trace metals. BMP tests were conducted under mesophilic conditions (39 degrees C) using a substrate-to-inoculum ratio <= 0.5 (VS basis) following VDI 4630 guidelines. Under mesophilic conditions, biogas yields ranged from 519 to 733 m & sup3; t-1 VS, and methane yields from 296 to 445 m & sup3; t-1 VS, with sCOD correlating with energy potential. Elemental analysis revealed that molybdenum presence coincided with higher methane yields, while elevated iron and tungsten concentrations were observed in lower-performing samples. Heavy metal concentrations (Pb, Cd, Cr, Hg and As) exceeded regulatory limits for agricultural digestate application. Estimated regional biogas production reached 2,212 m & sup3; day-1, supporting decentralized anaerobic digestion systems in rural contexts. These findings demonstrate the technical feasibility of biogas recovery from OFMSW and provide data to inform localized waste-to-energy initiatives.
The growing demand for cleaner and more efficient internal combustion engines has increased interest in dual-fuel strategies using renewable fuels such as biogas. This study applies multi-response optimisation to a simulated biogas/diesel RCCI engine using the Taguchi Design of Experiments (DoE) method, considering variations in engine load and biogas energy substitution ratio (ESR). Taguchi analysis identified optimal operating points with fewer experiments, while linear regression provided predictive models for key performance and emission parameters. Analysis of Variance (ANOVA) assessed statistical significance and quantified the relative contribution of each factor. All analyses were conducted using Minitab 22 statistical software. Results showed that higher engine loads with moderate biogas ESR minimised combustion variability, improved brake thermal efficiency, and lowered HC emissions, while NO formation followed expected thermal trends. This integrated approach demonstrates the feasibility of achieving a balanced trade-off between efficiency and emissions in simulated biogas-diesel RCCI operation.
Residential heating relies heavily on pellet fuels, yet conventional designs often suffer from prolonged carbon oxidation stages. This study investigated whether strategic changes to pellet geometry could solve this bottleneck. Researchers compared three configurations of commercial pine pellets: solid (D), single-hole (TD), and double-hole (CD), using both atmospheric combustion experiments and numerical simulations. The results highlight a significant leap in efficiency. Due to superior oxygen diffusion, the carbon oxidation stage was shortened by 30.4% for TD and 34.3% for CD pellets compared to the solid control. This structural change also led to higher thermal output; while the solid pellet reached a maximum flame temperature of 942 K, the TD and CD variants achieved 985 K and 988 K, respectively. Numerical simulations remained highly accurate, showing less than an 11% deviation from experimental data. The single-hole (TD) geometry emerged as the most favorable balance for combustion performance. Ultimately, the study proves that biomass efficiency can be significantly boosted through minor geometric adjustments rather than relying on inorganic additives, offering a cleaner and simpler path to optimized energy output.
The global livestock industry generates approximately 3.7 billion tons of cattle manure annually, creating both an environmental management challenge and an underutilized bioenergy resource. However, large-scale combustion of cattle manure is hindered by severe slagging associated with alkali-rich, low-melting ash. This study evaluated controlled water washing as a simple and low-cost pretreatment to improve the fuel quality of cattle manure under practically relevant conditions. Water washing effectively removed alkali metals, with performance was determined mainly by the water-to-solid ratio and further improved by temperature. As a result, slagging severity was reduced from severe to mild. Mineralogical analysis showed that suppressing potassium- and sodium-rich aluminosilicate phases and promoting the formation of more thermally stable minerals achieved this improvement. These results demonstrate that slagging control can be achieved through a readily implementable pretreatment that directly modifies ash chemistry before combustion. From an engineering perspective, the most promising deployment route is as a front-end pretreatment for manure-combustion systems in which wash water can be recycled and drying can be assisted by low-grade or waste heat. In such settings, the principal benefit is improved operational stability through reduced slagging, rather than stand-alone fuel upgrading.
Biodiesel is considered a key alternative fuel as it has the potential to replace diesel in reciprocating engines. Despite the identification of effective commercial inhibitors, many of them have been found to have undesirable ancillary effects. Accordingly, this work sought to investigate the efficacy of the West Indian Bayleaf extract as an inhibitor for biodiesel and its methanol blends. It examined the impact of variations in extract concentration and temperature on the effectiveness of the extract for mild steel samples submerged in coconut oil biodiesel and biodiesel/methanol blends. Efficacy was assessed via gravimetric analyses, vibration frequency response tests and surface hardness tests. Further, electrochemical impedance spectroscopy was utilized to gain a more precise assessment of the extract's inhibitor efficiency. A maximum inhibitor efficiency of 82% was recorded for a concentration of 2000 ppm in neat biodiesel. Moreover, in neat biodiesel, at concentrations of 1250 ppm and above, the extract outperformed the commercial inhibitor examined in this work. Further, the impact of alcohol addiction was found to improve inhibitor efficiency at lower concentrations, with a maximum value of 96.3% being recorded for 5% methanol. However, higher alcohol concentrations were found to negatively influence inhibition efficiency and alter general inhibitor characteristics.
Efficient mixing of oil and alcohol is critical for high-quality biodiesel production through transesterification. Static mixers like the Sulzer SMX type, offer energy-efficient mixing but require optimization for practical applications. This study employs computational fluid dynamics (CFD) to investigate the hydrodynamic behavior and design optimization of SMX static mixers for methanol and soybean oil mixing. Three length-to-diameter (L/D) ratios (6, 10 and 14) and inlet velocities (0.283, 0.566, and 1.132 mm/s) were simulated under laminar flow conditions. The results indicated that increasing the L/D ratio enhances mixing efficiency but significantly increases the pressure drop. Although the highest mixing efficiency is obtained at L/D = 14, the incremental efficiency gain becomes progressively smaller relative to the rising hydraulic losses. As a result, an L/D ratio of 6 is identified as the optimal configuration, providing a favorable balance between mixing performance and energy consumption under viscous-dominated laminar flow conditions. The pressure drop exhibits an approximately linear dependence on inlet velocity, which is consistent with laminar flow behavior.
In anaerobic reactors, effluent recirculation is important to enhances distribution, buffer and boost contacts. This study explores the synergistic effects of effluent recirculation and biochar addition on methane production from water hyacinth juice (WHJ) using high rate upflow anerobic reactor. Water hyacinth biomass was crushed and compressed to extract juice, while the residual solids were carbonized at 800 degrees C to produce biochar. The upflow anerobic reactor was operated at mesophilic conditions (37 +/- 1 degrees C) with varying hydraulic retention times (HRT) and effluent recirculation ratios of 1:3, 1:1, and 3:1 with and without biochar addition. Among all recirculation configurations, the 3:1 recirculation ratio with biochar addition exhibited the highest methane content (79.5%) at HRT of 2 days, indicating a strong synergistic effect between recirculation and biochar's microbial support and adsorption functions. Biochar alone also improved methane purity (up to 84.5%) by enhancing volatile suspended solid (VSS) removal and reducing CO2 content. Implementing recirculation reduced the stabilization period from 30 days to 20 days. Chemical oxygen demand (COD) and VSS removal efficiencies were also maximized under recirculation and biochar conditions, reaching up to 81.8% and 76.1%, respectively. Despite slightly elevated pH levels (up to 8.36) under recirculation scenarios, microbial activity and biogas production remained stable, suggesting the robustness of the anaerobic reactor system. Overall, integrating biochar and effluent recirculation offers a promising strategy for optimizing biogas production from WHJ while improving process stability, and methane purity at short HRT.
The viability of 2G ethanol depends on low-cost enzymes and efficient biomass hydrolysis. The quality of cellulolytic enzymes also plays a crucial role. The cost of enzyme in 2G ethanol process is up to 20%. In view of the urgency of biofuels and demand for cellulolytic enzymes, good hyper-cellulolytic strains are needed. The present study aims to further improve the available fungal mutant. To improve fungal strain, protoplasts of previously developed Penicillium funiculosum MRJ-16 strain were subjected to UV and chemical mutagen. Isolated mutants were evaluated for their ability to secrete cellulases and xylanases using glucose or cellulose or pre-treated rice straw as a substrate. At 7.5 L reactor scale, one of the mutant MPA-F9 produces 7.99 FPU/ml enzyme activity. Mutants were also assessed for their ability to hydrolyze acid-pre-treated rice straw at 15% total solid loading. The MPA-F9 mutant produced a maximum total sugar of 70.5 g/L, compared to 62.9 g/L for its parent strain MRJ-16. Secretome analysis by LC-MS/MS revealed that MPA-F9 has a few other secretory proteins, including glucanase, swollenin, and low levels of dipeptidyl peptidase. This study shows that the mutant strain is a promising candidate for cellulolytic enzyme production, aiding cost-effective conversion of biomass to sugars.
This study presents a comprehensive theoretical investigation of a europium-based luminescent complex, [Eu(DBM)3 & centerdot;NTZ], designed as a potential chemical marker for biodiesel systems derived from methylated oleic acid. Semi-empirical calculations were initially employed to obtain structural and electronic parameters of the complex, followed by molecular dynamics simulations to evaluate its stability, organization, and intermolecular interactions in a biodiesel environment. The simulations were conducted using a fully parameterized system, including explicit biodiesel molecules and charge-balanced Eu & sup3;+ ions, under NVT (constant number of particles, volume, and temperature) and NPT (constant number of particles, pressure, and temperature) ensembles to equilibrate the system. The results demonstrate the spontaneous formation of stable Eu & sup3;+-ligand coordination complexes, with approximately 90% complexation efficiency observed during the simulation trajectory. In addition, supramolecular interactions between biodiesel molecules and the ligands, such as pi-alkyl and ion-dipole interactions, were identified, thereby stabilizing the system. The simulated density closely matched experimental biodiesel values, confirming the adequacy of the model. These findings highlight the structural robustness and interaction profile of the Eu & sup3;+ complex in a biodiesel matrix, supporting its potential application as a luminescent biofuel marker and reinforcing the relevance of molecular dynamics simulations in the rational design of chemical tracers for renewable energy systems.
As the global pursuit of sustainable and clean energy intensifies, biofuels have emerged as a viable alternative to fossil fuels. However, effective impurity removal during biofuel production remains a key technical hurdle, affecting both process efficiency and fuel quality. This study focuses on optimizing biofuel purification by evaluating the performance of cyclone separators using computational fluid dynamics (CFD) simulations. Specifically, it investigates the impact of inflow velocity, impurity volume fraction, and critical geometric parameters-such as barrel length, cone length, and vortex finder diameter-on separation efficiency. By analyzing both individual and interactive effects of these parameters, the research provides detailed insights into optimizing cyclone design for enhanced impurity removal. The results demonstrate that specific configurations significantly improve separation performance, offering practical guidelines for designing efficient, application-specific cyclone separators in biofuel production. The findings also underscore the importance of customizing cyclone geometry and operational conditions to maximize impurity extraction. Ultimately, this study advances the understanding of cyclone-based separation systems in biofuel applications and supports the development of more efficient, environmentally friendly energy technologies. It contributes to cleaner production practices and strengthens the role of biofuels in the global shift toward renewable energy solutions.
Nannochloropsis oculata is a promising source of biodiesel with high biomass productivity and the capacity to accumulate high lipid contents. Accordingly, the growth rate and lipid productivity of this microalga were studied under various conditions. N. oculata was cultivated with continuous lightening and aeration for autotrophic and mixotrophic culturing, while its growth was performed in the presence of 1% glucose for heterotrophic and mixotrophic conditions. The growth of N. oculata was monitored for different temperatures, pH and salinity. , Under the conditions studied, N. oculata growth performed better than in other conditions at 20 degrees C at 30 ppt salinity for both autotrophic and mixotrophic growth . For heterotrophic growth , microalgae grown at 25 degrees C temperature and 30 ppt salinity outperformed all other conditions. This condition was also the best condition for lipid yield. The fatty acid profiles of the obtained lipid mass were determined and biodiesel quality parameters were evaluated. With values of 184-194 mg KOH/g for saponification value (SV), 51-69 g I2/100g iodine value (IV) and 59-63 for the cetane number (CN), biodiesel properties were all in the ranges within the European and American standards. Our results confirm N. oculata as a promising strain that could serve as an efficient bioproducer under both autotrophic and heterotrophic conditions.
Heavy metals (HMs) in water and soil are major environmental and public health problems that require long-term sustainable solutions for remediation. This study investigated the application of biochar as a sustainable and effective adsorbent for HM elimination from wastewater and soil, and the application of artificial intelligence (AI) to enhance the predictive modeling of adsorption efficiency. The primary objective of this study is to develop a robust AI-based predictive framework for estimating the heavy-metal adsorption efficiency of biochar under complex and multi-contaminant conditions. The novelty of this work lies in integrating the Gamma Test (GT)-based variable selection with advanced machine learning (ML) and Gaussian Process models to enhance the prediction accuracy and generalizability beyond conventional adsorption modeling approaches. This study used a dataset of 361 experimental values to develop a set of 22 AI-based ML models, which are ensemble combinations of Linear Regression, Regression Trees, Support Vector Machine, Gaussian Process Regression (GPR), Ensemble of Trees, and Artificial Neural Network. These intelligent predictive models were compared with each other using statistical methods such as the root mean squared error (RMSE) and mean absolute error (MAE) to predict the Adsorption Efficiency of Heavy Metals by biochar. The GT used in this study simplifies and enhances the development of AI-based ML models by selecting the most effective input values to predict the adsorption capacity of HMs by biochar. Among all the developed models, the Matern 5/2 GPR model showed the best predictive performance (R2 = 0.989; RMSE = 0.0317; and MAE = 0.0114). This AI-based predictive workflow links biochar physicochemical descriptors, as obtained from biofuel-related pyrolysis processes, to their adsorption performance, offering practical implications for sustainable bioenergy and wastewater treatment applications. This indicates that ensemble modeling using AI offers better prediction performance than individual models, with Matern 5/2 GPR being the best fit. This study shows that AI can be a crucial method for assisting the biochar-based remediation of pollutants from water and soil systems.
