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 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.

Effect of Eucommia ulmoides leaves on hyperuricemia and kidney injury induced by a high-fat/high-fructose diet in rats.

Objectives: To investigate the protective and preventive treatment effects of Eucommia ulmoides leaves on a rat model of high-fat and high-fructose diet (HFFD) induced hyperuricemia and renal injury. Materials and Methods: Network pharmacology and molecular-docking methods were used to predict the effects and action mechanisms of the major components of E. ulmoides leaves on hyperuricemia. Combining literature collection, we used SciFinder and the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and Analysis Platform to collect E. ulmoides leaf flavonoid and iridoid components. Swiss Target Prediction, Similarity ensemble approach (SEA), GeneCards, and the Online Mendelian Inheritance in Man (OMIM) database were used to obtain core targets, and the Search Tool for Recurring Instances of Neighbouring Genes (STRING) protein database was used as core target for gene ontology enrichment Set and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Molecular docking was applied to predict the pathways regulating the metabolism of uric acid. The selected targets and targeting efficacy were validated using a rat model of hyperuricemia and renal injury induced by a high-fat and high-fructose diet. Results: A total of 32 chemical components with effective targets, which regulated the PI3K-AKT pathway and endocrine resistance, were collected. Molecular docking results showed that iridoids and flavonoids are bound to proteins related to inflammation and uric acid metabolism. In addition, it was verified via animal experiments that an E. ulmoides leaf extract ameliorated hyperuricemia, renal injury, and inflammation, which are closely related to the targets Interleukin- 6 (IL-6), Tumor necrosis factor-α (TNF-α), Toll-Like Receptor 4 (TLR4), and Glucose transporter 9 (GLUT9). Conclusion: E. ulmoides leaf flavonoids and iridoids ameliorate hyperuricemia and uric-acid-induced inflammation through a multi-component, multi-target, and multi-pathway mechanism, which provides a theoretical basis for the development of therapeutics from E. ulmoides leaf components.

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.

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.