Machine learning-based prediction of the C/N ratio in municipal organic waste

Environmental Technology & Innovation, Год журнала: 2024, Номер unknown, С. 103977 - 103977

Опубликована: Дек. 1, 2024

Язык: Английский

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Novel cofactor regeneration-based magnetic metal–organic framework for cascade enzymatic conversion of biomass-derived bioethanol to acetoin

Bioresource Technology, Год журнала: 2024, Номер 408, С. 131175 - 131175

Опубликована: Июль 29, 2024

Язык: Английский

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Anaerobic digestion of a curious VFA complex feed for biomethane production; A study on ANN modeling optimized with genetic algorithm

Desalination and Water Treatment, Год журнала: 2024, Номер 317, С. 100257 - 100257

Опубликована: Янв. 1, 2024

Anaerobic digestion is a complex biological process widely used for organic waste treatment and biogas production. Understanding the intermediate stages biochemicals essential effective management. This study uses ANN modeling as well genetic algorithm optimization to explore predict how these intermediates behave. By scrutinizing interactions between VFAs CH4 production, within context of our VFA Complex Feed characterized by unique concentrations, this model underscores paramount significance three VFAs: acetate, propionate, butyrate. Notably, in distinctive study, contrary prior research, acetate manifests deleterious influence on production (CI = -1.92), whereas propionate +1.22) butyrate +1.14) exhibit favorable impact. exerts most substantial absolute (AAS +4.7) when juxtaposed with other VFAs. These results support supporting its validity. combining machine learning theoretical knowledge, advances comprehension anaerobic offers valuable insights optimizing process.

Язык: Английский

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Using Machine Learning and GPT Models To Enhance Electrochemical Pretreatment of Anaerobic Cofermentation: Prediction, Early Warning, and Biomarker Identification

ACS ES&T Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Electrochemical enhancing anaerobic cofermentation of waste activated sludge and food to produce volatile fatty acids (VFAs) represents an innovative promising approach. Despite its potential, optimizing system performance, providing early warnings, identifying biomarkers remain challenging tasks due the intricate interplay numerous environmental variables unclear dynamics microbial interactions. This study first employed machine learning (ML) models including XGBoost, random forest (RF), support vector regression (SVR), CatBoost forecast VFA production by integrating initial feedstock properties, electrochemical pretreatment conditions, fermentation parameters. demonstrated highest R2 0.977 lowest root-mean-square error (RMSE) at 95.69 mg COD/L. Key factors, days (VFA reaching 90% day 5), salinity (0.5–1.0 g/L), carbon-to-nitrogen (C/N) ratio (16.53–22), were identified as optimal for production. To enhance long-term monitoring facilitate warning systems, process indicators (pH, ORP, PNs, SCOD, PSs) from last used predict on following fine-tuning generative pretrain transformer (GPT), with gpt-3.5-turbo-0125 model exhibiting 0.837 ± 0.004 RMSE 296.98 3.65 Local sensitivity analysis revealed that SCOD was most important factor affecting Moreover, this ML uncover genus levels, Prevotella_7, Veillonella, Megasphaera, Lactobacillus, thereby elucidating nexus among communities, offered a novel modeling workflow cofermentation, enabling optimization mechanism exploration assistance large language models.

Язык: Английский

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Machine learning and response surface methodology for optimization of bioenergy production from sugarcane bagasse biochar-improved anaerobic digestion.

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106907 - 106907

Опубликована: Фев. 1, 2025

Язык: Английский

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