Co-liquefaction of Chlorella and soybean straw for production of bio-crude: Effects of reusing aqueous phase as the reaction medium

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%.

Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate

Machine learning (ML), a powerful artificial intelligence tool, can effectively assist and guide the production of bio-oil from hydrothermal liquefaction (HTL) of wet biomass. However, for hydrothermal co-liquefaction (co-HTL), there is a considerable lack of application of experimentally verified ML. In this work, two representative wet biomasses, sewage sludge and algal biomass, were selected for co-HTL. The Gradient Boosting Regression (GBR) and Random Forest (RF) algorithms were employed for regression and feature analyses on yield (Yield_oil, %), nitrogen content (N_oil, %), and energy recovery rate (ER_oil, %) of bio-oil. The single-task results revealed that temperature (T, °C) was the most significant factor. Yield_oil and ER_oil reached their maximum values around 350 °C, while that of N_oil was around 280 °C. The multi-task results indicated that the GBR-ML model of the dataset#4 (n_estimators = 40, and max_depth = 7,) owed the highest average test R2 (0.84), which was suitable for developing a prediction application. Subsequently, through experimental validation with actual biomass, the best GBR multi-task ML model (T ≥ 300 °C, Yield_oil error < 11.75 %, N_oil error < 2.40 %, and ER_oil error < 9.97 %) based on the dataset#6 was obtained for HTL/co-HTL. With these steps, we developed an application for predicting the multi-object of bio-oil, which is scarcely reported in co-hydrothermal liquefaction studies.

Hydrothermal liquefaction of municipal sludge and its products applications

Hydrothermal liquefaction (HTL) is an effective medium-temperature, high-pressure thermochemical process to dispose municipal sludge (MS), and biocrude (a crude bio-oil) is its main product. Many efforts are continued extensively to improve conversion efficiency and to promote industrial application of this technology. This work focuses on critical influencing factors (e.g., reaction temperature, residence time, atmosphere, solvent, catalyst, and pretreatment) and fundamental transformation mechanisms of main components (i.e., lipids, proteins, and carbohydrates) in MS HTL. It also analyzes migration behavior of heavy metals during MS HTL, which can provide a reference for subsequent recovery of nutrients from HTL products. Moreover, the applications of MS HTL products are systematically expounded, and potential challenges and opportunities are highlighted as well. It is necessary to develop advanced methods of catalyst recovery and innovative biocrude upgrading methods so as to reduce HTL investment and operating costs. Reusing aqueous phase and solid phase products as reaction medium and catalyst carrier separately after MS HTL is feasible to realize resource utilization of MS. This information can provide valuable guidance to promote MS HTL industrialization.

Effects of light quality on microalgae cultivation: bibliometric analysis, mini-review, and regulation approaches

The ever-increasing concern for energy shortages and greenhouse effect has triggered the development of sustainable green technologies. Microalgae have received more attention due to the characteristics of biofuel production and CO2 fixation. From the perspective of autotrophic growth, the optimization of light quality has the potential to promote biomass production and bio-component accumulation in microalgae at low cost. In this study, bibliometric analysis was used to describe the basic features, identify the hotspots, and predict future trends of the research related to the light quality on microalgae cultivation. In addition, a mini-review referring to regulation methods of light quality was provided to optimize the framework of research. Results demonstrated that China has the greatest interest in this area. The destination of most research was to obtain biofuels and high-value-added products. Both blue and red lights were identified as the crucial spectrums for microalgae cultivation. However, sunlight is the most affordable light resource, which could not be fully utilized by microalgae through the photosynthetic process. Hence, some regulation approaches (e.g., dyes, plasmonic scattering, and carbon-based quantum dots) are proposed to increase the proportion of beneficial spectrum for enhancement of photosynthetic efficiency. In summary, this review introduces state-of-the-art research and provides theoretical guidance for light quality optimization in microalgae cultivation to obtain more benefits.

Application of Chlorella vulgaris Beijerinck as a Biostimulant for Growing Cucumber Seedlings in Hydroponics

Hydroponics is a promising method for growing agricultural plants and is especially relevant in the context of global climate change. Microscopic algae, including Chlorella vulgaris, has great potential for use in hydroponic systems as natural growth stimulators. The effect of the suspension of an authentic strain of Chlorella vulgaris Beijerinck on the length of cucumber shoots and roots, as well as its dry biomass, was studied. During cultivation in a Knop medium with the addition of Chlorella suspension, the length of the shoots was shortened from 11.30 to 8.15 cm, while the length of the roots also decreased from 16.41 to 10.59 cm. At the same time, the biomass of the roots increased from 0.04 to 0.05 g. The data obtained indicate the positive effect of the suspension of the Chlorella vulgaris authentic strain on the dry biomass of cucumber plants in hydroponic conditions and make it possible to recommend this strain for use when growing plants in hydroponic systems.

Synergistic citric acid-surfactant catalyzed hydrothermal liquefaction of pomelo peel for production of hydrocarbon-rich bio-oil

Citric acid showed good performance of hydrothermal liquefaction (HTL) of biomass waste via promoting the depolymerization of macromolecules. The synergistic effects of citric acid-surfactants/solid catalysts in the low-temperature (200 °C) catalytic hydrothermal liquefaction of pomelo peel (PP) were studied for the first time. It turned out that citric acid-surfactants promoted the conversion of pomelo peel to bio-oil with a higher yield (26.10-67.72 wt%), higher heating value (17.79-24.77 MJ/kg) and energy yield (33.53-114.11 %), while citric acid-solid catalysts were more conducive to the formation of gas and other products. FT-IR and GC-MS analysis testified that citric acid-surfactants increased the selectivity of hydrocarbons from 49.99 % to 74.19 %. Additionally, the chemical functional groups of bio-oil were characterized by ¹H NMR and ¹³C NMR, indicating that the highest aliphatic content of bio-oils was 89.67 %. Moreover, citric acid-surfactant more environmentally friendly for low temperature liquefaction of biomass.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R² of 0.92 and 0.95, respectively. Acceptable predictive performance (test R² of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.