Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

ACS ES&T Engineering, Год журнала: 2023, Номер 4(3), С. 525 - 539

Опубликована: Окт. 19, 2023

Accurate prediction of methane production in anaerobic digestion with various pretreatment strategies is the utmost importance for efficient sludge treatment and resource recovery. Traditional machine learning (ML) algorithms have shown limited accuracy due to challenges optimizing complex parameters scarcity data. This work proposed a novel integrated system that employed an ensemble semisupervised (SSL)-automated ML (AutoML) model variable inputs reveal effects different pretreatments on during explainable analysis. Considering direct correlations type substrates, considered as hidden variable. Results demonstrated AutoML outperformed conventional models (i.e., support vector regression (SVR), extreme gradient boosting (XGB), etc.), evidenced by its higher R2 value. Moreover, integration SSL further enhanced effectively leveraging unlabeled data, leading reduction mean squared error from 11.3 9.7. Explainable analysis results revealed significance variables operating time, followed proteins, carbohydrates, chemical oxygen demand, volatile fatty acids. Furthermore, principal component correlation unveiled interconnected relationships among substrate concentration, microbial communities, metabolic functions found increasing concentration promoted enrichment functional functions. These insights shed light advantages SSL-AutoML predicting systems elucidate dependence key variables, offering valuable guidance effective recovery capabilities.

Язык: Английский

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Robust prediction for characteristics of digestion products in an industrial-scale biogas project via typical non-time series and time-series machine learning algorithms

Chemical Engineering Journal, Год журнала: 2024, Номер 498, С. 155582 - 155582

Опубликована: Сен. 11, 2024

Язык: Английский

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Can wood waste be a feedstock for anaerobic digestion? A machine learning assisted meta-analysis

Chemical Engineering Journal, Год журнала: 2024, Номер 487, С. 150496 - 150496

Опубликована: Март 17, 2024

Anaerobic digestion is widely employed to process various organic wastes while generating renewable energy and nutrient-rich digestate. However, lignocellulosic wastes, especially wood waste, suffer from the recalcitrance associated with high lignin content, thereby adversely impacting on biogas production. It remains unclear whether waste suitable as a feedstock for anaerobic what extent pretreatment techniques could affect its biochemical methane potential. In this paper, 769 datasets production were collected meta-analysis. The results showed an average 146 % increase in other compared when not applied, but gap be mitigated 99 considered, indicating that more effective waste. A further analysis of different significantly increased by 113 combination was than single method. Finally, three machine learning algorithms applied explore relationship between selected variables. random forest method yielded better predictive performance (R2 = 0.9643) artificial neural networks support vector regression. Feature importance found particle size had higher influence temperature or composition. Overall, study gives insight into potential utilizing employing methods. This work also reveals correlations critical variables, which serve guide optimizing operational adjustments during digestion.

Язык: Английский

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Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification

Renewable Energy, Год журнала: 2024, Номер 231, С. 120969 - 120969

Опубликована: Июль 14, 2024

Язык: Английский

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Analyzing the trends and hotspots of biochar’s applications in agriculture, environment, and energy: a bibliometrics study for 2022 and 2023

Biochar, Год журнала: 2024, Номер 6(1)

Опубликована: Сен. 19, 2024

Язык: Английский

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