RESUMEN
This study explored the effectiveness of hydrothermal liquefaction (HTL) in converting sewage sludge (SS) into high-quality biocrude. It scrutinized the influence of various solvents, including conventional choices like dichloromethane (DCM) and hexane, alongside environmentally friendly alternatives, such as ethyl butyrate (EB) and ethyl acetate (EA). HTL experiments, conducted at 350 °C for 60 min in a 20 mL batch reactor, include solvent-based biocrude extraction. Notably, EB showed the highest extraction yield (50.1 wt %), the lowest nitrogen distribution (5.4% with 0.32 wt %), and a remarkable 74% energy recovery (ER), setting a noteworthy benchmark in nitrogen reduction. GCMS analysis reveals EB-derived biocrude's superiority in having the least heteroatoms and nitrogenous compounds compared to hexane, EA, and DCM. Solid residues from hexane, EB, and EA displayed the highest nitrogen distribution range (62-68%), hinting at potential applications in further processes. These findings significantly inform solvent selection for efficient and sustainable waste-to-energy conversion. While promising, the study emphasizes the need to explore solvent-solute interactions further to optimize biocrude quality, highlighting the pivotal role of solvent choice in advancing clean, cost-effective waste-to-energy technologies.
RESUMEN
Electrochemical conversion of underutilized biomass-based glycerol into high-value-added products provides a green approach for biomass and waste valorization. Plus, this approach offers an alternative to biofuel manufacturing procedure, under mild operating conditions, compared to the traditional thermochemical routes. Nevertheless, glycerol has been widely valorized via electrooxidation, with lower-value products generated at the cathode, ignoring the electroreduction. Here, a review of the efficient glycerol reduction into various products via the electrocatalytic reduction (ECR) process was presented. This review has been built upon the background of glycerol underutilization and theoretical knowledge about the state-of-the-art ECR. The experimental understanding of the processing parameter influences towards electrochemical efficiency, catalytic activity, and product selectivity are comprehensively reviewed, based on the recent glycerol ECR studies. We conclude by outlining present issues and highlighting potential future research avenues for enhanced ECR application.
RESUMEN
Both the conversion of lignocellulosic biomass to bio-oil (BO) and the upgrading of BO have been the targets of many studies. Due to the large diversity and discontinuity seen in terms of reaction conditions, catalysts, solvents, and feedstock properties that have been used, a comparison across different publications is difficult. In this study, machine learning modeling is used for the prediction of final higher heating value (HHV) and ΔHHV for the conversion of lignocellulosic feedstocks to BO, and BO upgrading. The models achieved coefficient of determination (R2) scores ranging from 0.77 to 0.86, and the SHapley Additive exPlanations (SHAP) values were used to obtain model explainability, revealing that only a few experimental parameters are largely responsible for the outcome of the experiments. In particular, process temperature and reaction time were overwhelmingly responsible for the majority of the predictions, for both final HHV and ΔHHV. Elemental composition of the starting feedstock or BO dictated the upper possible HHV value obtained after the experiment, which is in line with what is known from previous methodologies for calculating HHV for fuels. Solvent used, initial moisture concentration in BO, and catalyst active phase showed low predicting power, within the context of the data set used. The results of this study highlight experimental conditions and variables that could be candidates for the creation of minimum reporting guidelines for future studies in such a way that machine learning can be fully harnessed.
