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.

Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass.

Hydrothermal treatment (HTT) is a potential technology for producing biofuel from wet biomass. However, the aqueous phase (AP) is generated inevitably in the process of HTT, and studies are lacking on the detailed exploration of AP properties. Therefore, machine learning (ML) models were built for predicting the pH, total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) of the AP based on biomass feedstock and HTT parameters. Results showed that the gradient boosting decision tree (average testing R2 0.85-0.96) can accurately predict the above wastewater properties for both single- and multi-target models. ML-based feature importance indicated that nitrogen content of biomass, solid content, and temperature were the top three critical features for pH, TN, and TP, while those for TOC were reaction time, lipid, and temperature. This ML approach provides new insights to understand the formation and properties of the HTT AP by ML rather than time-consuming experiments.

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

Co-liquefaction was combined with hydrothermal liquefaction (HTL) aqueous phase (AP) recirculation to improve the practicality of HTL process. The Chlorella powder (CL), soybean straw (SS), and their mixture (CS) with ratio 1:1 were processed at 300 °C for 20 min, and the AP was recirculated four times. The yield of CS bio-crude was increased (from 24.28% to 31.83%) by co-liquefaction, but remained stable during AP recirculation. By contrast, the yields were increased for CL bio-crude (from 32.40% to 41.19%), SS hydrochar (from 19.55% to 30.88%), and CS hydrochar (from 9.42% to 14.76%) by recirculation. The elemental analysis, chemical composition analysis, functionality analysis, thermogravimetric analysis, and verification experiments (HTL with model AP components) show the N-containing compounds (e.g., amines) in AP were converted into amides (acylation) for CL bio-crude, into N-heterocycles (Maillard reactions) for CS hydrochar, and into Mannich bases for SS hydrochar, which contributed to the increased yield and N content (from 7.27% to 8.82% for CL bio-crude). Furthermore, the O content of CS bio-crude was decreased (from 15.31% to 12.52%) by recirculation, resulted from the conversion of N-heterocyclic ketones into pyrazine derivates. The decreased O content and comprehensive combustibility index (from 0.306 to 0.177) of CS bio-crude indicate the great potential of this craft combination.

Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae.

Hydrothermal liquefaction (HTL) of algae is a promising biofuel production technology. However, it is always difficult and time-consuming to identify the best optimal conditions of HTL for different algae by the conventional experimental study. Therefore, machine learning (ML) algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs, and bio-oil yield (Yield_oil), and the contents of oxygen (O_oil) and nitrogen (N_oil) in bio-oil as outputs. Results indicated that gradient boosting regression (GBR, average test R2 âˆ¼ 0.90) exhibited better performance than random forest (RF) for both single and multi-target tasks prediction. Furthermore, the model-based interpretation suggested that the relative importance of operating conditions (temperature and residence time) was higher than algae characteristics for the three targets. Moreover, ML-based reverse and forward optimizations were implemented with experimental verifications. The verifications were acceptable, showing great potential of ML-aided HTL for producing desirable bio-oil.

A novel approach of using zeolite for ammonium toxicity mitigation and value-added Spirulina cultivation in wastewater.

Owning to the ammonium toxicity, some ammonium-rich wastewater may not be used for algae cultivation. To overcome this problem, herein, a novel approach of using zeolite to mitigate ammonium toxicity in wastewater for value-added Spirulina production was proposed. Synthetic zeolite was used as medium for ammonium adsorption in wastewater and subsequently as slow-releaser providing nitrogen to Spirulina growth. The optimal conditions for ammonium adsorption include pH value of 8.0, zeolite dose of 300 g/L, and adsorption time of 9 h. The results showed that in terms of biomass production and ammonium recovery, zeolite-based pretreatment has great advantages over some conventional pretreatment technologies. After algae-assisted desorption treatment, ammonium adsorption capacity of zeolite increased back to 1.21 mg/g. In a real-world application, this work will provide a feasible and sustainable approach to remediate ammonium-rich wastewater, produce value-added Spirulina biomass, and recycle used zeolite, further promoting the industrialization of algae-based wastewater remediation.