BioEnergy Research最新文献

Cell wall composition influences biomass use as a forage and as a feedstock for biofuel and chemical conversion. To examine the influence of environment on composition of switchgrass (Panicum virgatum L.), we utilized a multi-environment experiment consisting of clones of switchgrass genotypes grown at up to ten locations in the continental US. We tested the influence of different milling treatments on biomass composition trait predictions via near-infrared reflectance spectroscopy (NIRS). We found that most compositional trait predictions (29/34) were significantly different (P < 0.05) when a single lot of biomass was subjected to disparate milling treatments, i.e., knife milling vs. knife milling with an additional cyclone milling. Further, depending on the plant material tested, three to eight compositional trait predictions vary (P < 0.05) when identical biomass was knife milled at different sites followed by cyclone milling at a single site, including for traits such as Klason lignin, nitrogen, and carbon. In some cases, variation due to milling site exceeded environmentally induced compositional variation of a single switchgrass genotype grown at different sites. From these observations, we recommend a protocol with two sequential millings that decouples growth environment from a particular mill. Utilizing this approach, we found that 46/46 biomass composition traits from the warm season herbaceous forage and switchgrass bioethanol NIRS equations vary significantly (P < 0.001) in clones of a switchgrass genotype (WBC) grown at ten sites, with the growth site representing the largest average source of variation (41%). This multi-site milling approach can be used to examine environmental and gene-by-environment influences on composition with the goal of optimizing cell wall composition in different environments for biomass utilization.

The rising energy consumption, energy crises, industrial growth, and environmental pollution have prompted a shift towards renewable energy resources. This study investigates the dynamic behavior of high-viscosity fluids in stirred tanks, both baffled and non-baffled, relevant for laboratory applications and biogas production. A cylindrical tank with four vertical baffles was analyzed, focusing on the flow field, turbulent kinetic energy, and its dissipation rates at rotational speeds of 20, 40, and 60 rpm. Comparisons between experimental and simulated results highlighted an acceptable 6% error in power consumption and Reynolds number predictions. At different rotational speeds, the radial velocities measured along the blades were 0.237, 0.130, and 0.041 m/s for the baffled tank, and 0.226, 0.128, and 0.041 m/s for the non-baffled tank, respectively. Furthermore, an increase in vertical flow contributed to greater turbulence intensity. The turbulent kinetic energy for baffled and non-baffled vessels at these speeds was recorded as 0.0215, 0.0102, 0.0024 m²/s² and 0.0188, 0.0091, 0.0022 m²/s², respectively. Dissipation rates were 0.049, 0.011, 0.0006 m²/s³ and 0.037, 0.0091, 0.0005 m²/s³. The findings confirm that baffled configurations enhance radial flow and mixing efficiency. The final cumulative biogas production results after 19 days of operation showed significant differences in the performance of each stirring speed. The amounts of biogas collected were 367, 488, 418, and 325 L at stirring speeds of 0, 20, 40, and 60 rpm, respectively. The optimal stirring speed was 20 rpm, yielding a positive energy balance of 4.424 MJ, the highest net energy efficiency. These findings suggest that moderate stirring with baffles optimizes both methane output and energy performance.

Anaerobic digestion (AD) is a sustainable waste-to-energy technology that addresses both environmental pollution and renewable energy generation. This study investigates the potential use of parboiled rice mill wastewater (PRMWW), assesses the energy balance of its co-digestion process, and addresses water requirements in AD. Since PRMWW has an unbalanced nutrient profile, particularly low in carbon, its co-digestion with lignocellulosic substrates such as rice straw (RS) can enhance overall digestion efficiency by improving the nutrient balance and promoting lignocellulose degradation. RS was incorporated into PRMWW to balance the carbon-to-nitrogen (C/N) ratio and provide structural carbon for enhanced biogas production. Various co-substrate combinations with different C/N ratios were tested under mesophilic conditions. The study investigated the effect of RS particle size on biogas production and observed that medium-sized particles (1.18–2.36 mm) yielded higher biogas compared to small (0.6–1.18 mm) and large (2.36–4.75 mm) particles. The corresponding biogas yields were 363, 439, and 313 mL/g VS_added for small, medium, and large particles, respectively. Additionally, it was observed that increasing the C/N ratio beyond a certain point reduced biogas production due to nitrogen limitation. At C/N = 23 with RS particles of medium, methane and biogas yields were 235 and 439 mL/gVS_added, respectively, with an 83% reduction in volatile solids (VS). The impact of the inoculum-to-substrate (I/S) ratio (0.5 to 4) on digester performance was also examined. The optimal I/S = 1 produced significant yields of 443 mL/gVS for biogas and 267 mL/gVS for methane, with 72% biodegradability. The modified gompertz model (MGM) showed the highest methane production (268.59 mL/gVS) after a lag period of 2.60 ± 0.71 days. The energy balance analysis of anaerobic co-digestion (ACoD) of PRMWW and RS reveals a net energy gain of 43.75 kWh/MgVS, demonstrating superior energy yield compared to mono-digestion.

Trickle bed reactors (TBRs) are widely employed in oleochemical processing due to their continuous operation and high efficiency. However, further improvements are needed to develop sustainable packing materials for TBR systems. In this study, a TBR packed with activated carbon impregnated with a deep eutectic solvent (DES) was used for the treatment of acidic crude palm oil (ACPO). An ammonium-based DES was synthesized from p-toluenesulfonic acid monohydrate (PTSA) and choline chloride (ChCl). The successful impregnation of –SO₃H groups onto activated carbon was evidenced by FESEM, verified by EDX through the detection of sulfur and further confirmed by FTIR analysis showing characteristic sulfonic peaks. Under optimal conditions using 8 g of DES-loaded activated carbon, a reaction temperature of 60 °C, and flow rates of 4 mL/min for ACPO and 3 mL/min for methanol, the free fatty acid (FFA) content was reduced from 9 to 1%. Compared to the batch process, the system showed lower catalyst consumption (17.86 mg/g over 37 mg/g) and higher treated ACPO yield (86 ± 0.1%). These results highlight the TBR-DES system as a promising and sustainable approach for upgrading low-grade palm oil to biodiesel feedstock.

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.