BioEnergy Research最新文献

Bioenergy and biofuels sourced from biomass are recognized as sustainable, renewable alternatives to fossil fuels, with the potential to reduce greenhouse gas emissions. This study explores how the circularity and sustainability of the biomass supply chain can be evaluated, specifically focusing on rice straw utilization in Vietnam, a topic that has not been comprehensively explored. To assess different sustainability and circularity supply chain alternatives for biomass to enhance utilisation of it for energy production in the Vietnamese agriculture context, an integrated Decision Support System based on a life cycle thinking approach and the multi-criteria decision making methods is employed. The study examines five supply chain alternatives for utilizing rice straw in bioenergy production in Vietnam, including anaerobic digestion for biogas and use in a CHP plant. Key sustainability and circularity indicators were analysed, including Global Warming Potential, Internal Rate of Return, job creation, and waste recycling rates. The results showed that the digestate pyrolysis alternative achieved the highest scores for sustainability and circularity. The captured and liquefied CO₂ alternative exhibited the lowest Global Warming Potential at 2.27 k.ton gCO₂eq/year, while the microalgae alternative had the highest GWP at 107 k.ton gCO₂eq/year. The digestate pyrolysis alternative also yielded the highest IRR at 20.46%, while the captured and liquefied CO₂ alternative had the lowest at 13.88%. In terms of job creation, the microalgae alternative generated the most jobs (31.11 man-years per year), and it also had the highest waste recycling rate at 90.49%. The findings suggest that integrating circular economy principles, such as upcycling waste by suitable treatment technologies, can significantly enhance the sustainability and circularity of biomass supply chains. The digestate pyrolysis alternative emerged as the most sustainable and circular option, while microalgae cultivation excelled in job creation and recycling. These insights are crucial for optimizing biomass supply chains and supporting Vietnam’s transition to a low-carbon economy.

Biomass–plastic co-pyrolysis is a promising route for sustainable fuel production; however, predictive modeling is limited by the inability of conventional kinetics to capture composition-dependent interactions and by thermodynamic inconsistencies in purely data-driven models. This study develops a physics-informed neural network (PINN) that integrates composition-dependent Arrhenius kinetics with global mass and elemental (C, H, O) conservation through a multi-objective loss formulation. The model was trained on 106 experimental datasets and achieved high predictive accuracy on unseen data (R2 = 0.988, RMSE = 1.838 wt%, MAPE = 2.4%), corresponding to a 66% reduction in RMSE relative to an XGBoost baseline. The PINN strictly preserved mass balance (0.00% violation) and demonstrated stable extrapolation up to 1000 °C. External validation confirmed strong generalization (R2 = 0.991, RMSE = 1.74 wt%) with mass closure error below 0.5 wt%. Beyond prediction, the framework enabled mechanistic interpretation by identifying a critical compositional threshold at 30 wt% plastic, consistent with the onset of hydrogen-transfer interactions reported in the literature, reducing the effective activation energy from 180 to 85 kJ/mol and enhancing bio-oil yield by up to 11.9 wt% above additive behavior. The trained PINN was further deployed as a differentiable surrogate for gradient-based optimization, identifying optimal conditions near 513 °C and high plastic fractions (> 90 wt%), with predicted yields exceeding 92 wt%. The results demonstrate that physics-informed learning enables thermodynamically consistent, interpretable, and efficient modeling, establishing a domain-aware predictive surrogate for laboratory-scale co-pyrolysis process optimization.

This review focuses on invasive aquatic and wetland plants, such as cattail (Typha latifolia) L., common reed (Phragmites australis (Cav.) Trin. ex Steud.), and water hyacinth (Eichhornia crassipes (Mart.) Solms), as feedstocks for microbial production of organic acids used in reductive bioleaching of manganese, iron, and nickel. These species are abundant, fast-growing, low in lignin relative to woody biomass, and often available at zero or negative acquisition cost as products of environmental mitigation, making them attractive alternatives to feedstocks that compete with food or established markets. As high-grade ore reserves decline, sustainable and low-cost alternatives for metal recovery have become vital, and organic acids such as citric, oxalic, and acetic acid derived from biomass fermentation offer a promising alternative to synthetic inorganic acids. This paper synthesizes current literature on the compositional characterization of these aquatic and wetland feedstocks, the low-input pretreatment strategies suited to their low-lignin structure (including brief physical pre-conditioning via microwave or ultrasonic exposure alongside biological pretreatment), and the microbial fermentation pathways that convert their carbohydrate content into organic acids for reductive bioleaching. Key research gaps specific to bioconversion of these underexplored feedstocks are highlighted to guide the development of an integrated, scalable bioprocess for sustainable metal recovery.

The production of renewable chemicals and plastics has been extensively investigated in recent decades, with the purpose of reducing the global dependence on fossil resources. In this review, the main routes to produce chemicals from biomass are reviewed and a critical discussion on the prospects and techno-economic viability of these technologies is presented. As these topics are often considered as independent from each other, the aim of this work is to provide an unified and interdisciplinary perspective of main possible production routes and their development status for experts and non-experts. The main processes of biomass concentration were reviewed and two main approaches for obtaining renewable chemicals were considered: (i) the production of compounds chemically identical to petroleum derivatives, and (ii) the production of conventional and new oxygenated chemicals. The literature shows that there are sustainable technological alternatives to the substitution of many petroleum derived chemicals, but the complexity of the production processes should not be simplistically evaluated. Solutions and technologies must be carefully evaluated, both from the point of view of their technoeconomic viability and their sustainability.

High-ash biogas residue exhibits low reactivity and yields mineral-rich char with limited fuel quality under mono-pyrolysis. This study investigated the macro-isothermal co-pyrolysis of swine digestate (SD) with spent coffee grounds (SCG) or rice husk (RH) at a fixed 50:50 mass ratio using a custom thermogravimetric system with approximately 5 g samples, providing a controlled framework closer to abrupt external heating in a preheated reaction zone than conventional micro-scale TGA. Isothermal experiments at 500–800 °C were analyzed using a first-order model, and the resulting biochars were characterized by elemental analysis, N₂ adsorption, and higher heating value measurements. The first-order model described the main conversion range well (R² > 0.98). The blends showed lower apparent activation energies (21.47–22.06 kJ mol⁻¹) than the corresponding theoretical additive values, indicating positive kinetic synergy under macro-isothermal conditions. Co-pyrolysis also improved char properties relative to digestate mono-pyrolysis. The SD-RH biochar exhibited the highest BET surface area (82.48 m² g⁻¹), while biochar HHV increased from 4.67 MJ kg⁻¹ for SD to 9.75–13.03 MJ kg⁻¹ for the blends. In addition, the blended chars showed low H/C_org and O/C_org ratios (0.34–0.40 and 0.15–0.17), consistent with European Biochar Certificate (EBC) stability thresholds. These results suggest that co-pyrolysis with lignocellulosic biomass can improve the apparent reactivity and quality of digestate-derived biochar, while macro-isothermal testing offers a useful intermediate approach for studying conversion behavior under abrupt external heating.

In this investigation, we analysed the impact of ten chemical additives – galactose (Gal), arabinose (Ara), trehalose (Tre), urea (U), gum arabic (GA), ammonium sulphate (AS), sodium pyrophosphate (SPP), sodium thiosulphate (STS), sodium malonate (SM), and sodium sulphate (SS) – administered at four distinct concentrations, on the astaxanthin production of H. lacustris in nitrogen-deficient settings. Furthermore, the impact of the supplements on the growth, biomass, chlorophyll (chl) a, chl b, and total carotenoid levels of H. lacustris was documented. The most potent supplements, SPP, SM, and U, had remarkable outcomes. On day 15, the H. lacustris culture reached its maximal biomass (2.00 times relative to the control) when U-0.1 g/L (1.66 mM/L) was added. Similarly, adding SPP (0.01 g/L or 0.04 mM/L) was the most effective way to raise the astaxanthin concentration (55.94 ± 0.35 mg/g, a 4.24-fold increase from the control). Similarly, the maximum astaxanthin percentage of biomass (5.59 ± 0.04)% was found in the culture treated with SPP (0.01 g/L or 0.04 mM/L). Furthermore, the culture supplemented with SM-0.01 (0.07 mM/L) demonstrated the highest astaxanthin productivity (0.28 mg/L/day), which was 1.55 times higher compared to the control. The results of this work offer a useful foundation for future research targeted at developing an efficient and sustainable H. lacustris cultivation system for industrial applications.

The aim of this study was to investigate the feasibility of using biochar or its blends with pulverized coal injection (PCI) coal as a potential blast furnace (BF) fuel. A comparison was conducted between the properties of biochar obtained from the pyrolysis of coconut shell and PCI coal. Coconut shells were pyrolyzed at different temperatures (between 200 °C and 450 °C) to produce biochar. The physicochemical characteristics and combustion indices of the materials were evaluated. Combustion rate was also studied using thermogravimetric analysis (TGA) to determine the effect of biochar addition on the PCI coal. Based on the combustion indices (Tmax, DTGmax, biochar yield at Tmax, and residue at 900 °C) and the three-stage thermal decomposition behavior observed in the TG/DTG analysis, it was found that biochars produced at low pyrolysis temperatures exhibited suitable characteristics for use as an auxiliary fuel in the BF. It was also observed that biochar had a higher alkalinity index (AI = 32.40) compared to the reference PCI coals (AI = 3.08 and 2.06). The results suggested that the addition of biochar coal increases the combustion rate of the resulting blends. Blends containing 10%, 30%, and 50% biochar (C200) were evaluated, corresponding to biochar/PCI coal ratios of 10/90, 30/70, and 50/50, respectively. Although biochar cannot be used as a complete replacement of PCI coal in a BF, the blend containing 30% biochar and 70% PCI coal (30% C200) showed promising performance without significantly altering fuel characteristics, demonstrating the potential of an integrated approach combining physicochemical characterization, combustion behavior, and blend performance for PCI applications.

Graphical Abstract

Glycerol is a high-value by-product of biodiesel production, yet its utilization remains suboptimal in many biodiesel-producing countries, including Indonesia. This study integrates techno-economic analysis (TEA), mixed-integer linear programming (MILP), and multi-objective optimization to evaluate glycerol purification strategies and identify economically and environmentally preferable market pathways for United States Pharmacopeia (USP)-grade glycerol in Indonesia. Four production scenarios were assessed based on purification level and plant configuration. Economic feasibility was evaluated using net present value (NPV), internal rate of return (IRR), and payback period (PBP), while the MILP model optimized export allocation under hub-and-spoke and milk-run shipping configurations by considering logistics costs and domestic market obligation (DMO) constraints. The results show that centralized production of USP-grade glycerol provides the highest economic performance, with an IRR of up to 40.8% and an NPV of approximately USD 430 million, although it requires substantial capital investment. Decentralized USP-grade production offers a viable alternative for standalone plants, whereas centralized technical-grade purification provides a more balanced risk–return profile for clustered biodiesel facilities. The optimization results indicate that full export allocation consistently maximizes profitability, with China and India accounting for more than 90% of total exports. The multi-objective optimization further demonstrates that the highest-revenue allocation does not necessarily result in the lowest environmental impact because export destinations, shipping distances, and transported volumes influence tanker fuel consumption and associated greenhouse gas emissions.

Graphical Abstract

Torrefaction and mild pyrolysis are widely used to improve the fuel quality of lignocellulosic biomass, but these processes induce coupled changes in mass yield, elemental composition, proximate composition, and higher heating value. Conventional single-stage machine-learning models can achieve high global prediction accuracy, but they may generate physically infeasible combinations of biomass fuel properties. In this study, a physically constrained multi-stage machine-learning model was developed to predict the fuel properties of thermally upgraded lignocellulosic biomass. The proposed model consisted of three sequential stages: mass yield prediction, elemental composition and higher heating value prediction using logarithmic enhancement factors, and proximate composition prediction with deterministic closure. A single-stage baseline model showed high apparent accuracy but produced proximate closure violations, mass-yield boundary violations, and directional inconsistencies in higher heating value. In contrast, the proposed multi-stage model maintained physically feasible predictions by construction, with cross-validated R² values of 0.8992 for Stage 1, 0.8772 for Stage 2, and 0.8258 for Stage 3. The corresponding RMSE values were 6.3682%p for mass yield, 0.0729 in the Stage-2 logEF domain, and 0.1904 in the Stage-3 logEF domain. The unconstrained baseline produced proximate-closure violations in 40.24% of the evaluated samples, whereas the proposed framework eliminated such violations through deterministic closure. External validation using literature-derived biomass datasets showed that the model reproduced the major trends of carbon enrichment, oxygen removal, volatile matter reduction, and fixed carbon increase. These results indicate that physically constrained machine learning can provide a useful screening tool for estimating solid biofuel properties of torrefied and mildly pyrolysed lignocellulosic biomass.