Photocatalytic reforming of aqueous phase obtained from liquefaction of household mixed waste biomass for renewable bio-hydrogen production

Hydrothermal liquefaction of composite household waste to biocrude: the effect of liquefaction solvents on product yield and quality

The hydrothermal liquefaction (HTL) of composite household waste (CHW) was investigated at different temperatures in the range of 240-360 °C, residence times in the range of 30-90 min, and co-solvent ratios of 2-8 ml/g, by utilising ethanol, glycerol, and produced aqueous phase as liquefaction solvents. Maximum biocrude yield of 46.19% was obtained at 340 °C and 75 min, with aqueous phase recirculation ratio (RR) of 5 ml/g. The chemical solvents such as glycerol and ethanol yielded a biocrude percentage of 45.18% and 42.16% at a ratio of 6 ml/g and 8 ml/g, respectively, for 340 °C and 75 min. The usage of co-solvents as hydrothermal medium increased the biocrude yield by 35.30% and decreased the formation of solid residue and gaseous products by 19.82% and 18.74% respectively. Also, the solid residue and biocrude obtained from co-solvent HTL possessed higher carbon and hydrogen content, thus having a H/C ratio and HHV that is 1.01 and 1.23 times higher than that of water as hydrothermal medium. Among the co-solvents, HTL with aqueous phase recirculation resulted in higher carbon and energy recovery percentages of 9.36% and 9.78% for solid residue and 52.09% and 56.75% for biocrude respectively. Further qualitatively, co-solvent HTL in the presence of obtained aqueous phase yielded 33.43% higher fraction of hydrocarbons than the pure water HTL and 7.70-17.01% higher hydrocarbons when compared with ethanol and glycerol HTL respectively. Nitrogen containing compounds, such as phenols and furfurals, for biocrudes obtained from all HTL processes, were found to be present in the range of 8.30-14.40%.

Conversion of tobacco processing waste to biocrude oil via hydrothermal liquefaction in a multiple batch reactor

Fossil fuels are the primary energy source of almost all societies and economies, but it is finite and scarce. The use of non-renewable fossil fuels threatens earth's environment. At the same time, waste from agricultural and industrial activities is increasing. Most of this waste is discarded or poorly managed, causing many other environmental issues. Converting waste to energy is a promising route to address these challenges. We investigated the hydrothermal liquefaction (HTL) of high moisture content, tobacco-processing waste in a multiple batch thermal reactor to produce biocrude oil. The effects of operating conditions were studied and optimized for maximum liquid biocrude oil yield. HTL operating conditions considered were temperatures from 280 to 340 °C and residence times from 15 to 45 min for a fixed ratio of biomass to deionized water of 1:3. The reaction temperature was found to affect the yields and distribution of products significantly. The maximum yield of the liquid biocrude oil obtained was more than 52% w/w at 310 °C and 15 min. Under these conditions, almost 90% of the energy was recovered in biocrude oil and solid products. The liquid fraction was mainly composed of phenols, ketones, and nitrogenous compounds. This study provides a potential framework for eco-technologies for biomass waste-to-energy conversion with respect to converting tobacco processing residues to liquid biofuels and biochemicals.

Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes

Machine learning (ML) approach was applied for the prediction of biocrude yields (BY) and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass and wastes using 17 input features from feedstock characteristics (biological and elemental properties) and operating conditions. Several novel ML algorithms were evaluated, based on 10-fold cross-validation, with 3 different sets of input features. An extreme gradient boosting (XGB) model proved to give the best prediction accuracy at nearly 0.9 R² with normal root mean square error (NRMSE) of 0.16 for BY and about 0.87 R² with NRMSE of about 0.04 for HHV. Temperature was found to be the most influential feature on the predictions for both BY and HHV. Meanwhile, feedstock characteristics contributed to the XGB model for more than 55%. Individual effects and interactions of most important features on the predictions were also exposed, leading to better understanding of the HTL system.