Optimizing Membrane Bioreactor Performance in Wastewater Treatment Using Machine Learning and Meta-Heuristic Techniques

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104626 - 104626

Опубликована: Март 1, 2025

Язык: Английский

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Bibliometric analysis of artificial intelligence in wastewater treatment: Current status, research progress, and future prospects

Journal of environmental chemical engineering, Год журнала: 2024, Номер 12(4), С. 113152 - 113152

Опубликована: Май 23, 2024

Язык: Английский

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Challenges in achieving partial nitrification: Simultaneous nitrification-denitrification as the dominant pathway in municipal wastewater treatment

Journal of environmental chemical engineering, Год журнала: 2025, Номер unknown, С. 115839 - 115839

Опубликована: Фев. 1, 2025

Язык: Английский

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Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning

Journal of Environmental Management, Год журнала: 2024, Номер 364, С. 121430 - 121430

Опубликована: Июнь 13, 2024

Язык: Английский

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Machine-learning-aided prediction and optimization of struvite recovery from synthetic wastewater

Journal of Water Process Engineering, Год журнала: 2024, Номер 58, С. 104896 - 104896

Опубликована: Фев. 1, 2024

Язык: Английский

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Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122386 - 122386

Опубликована: Сен. 10, 2024

Язык: Английский

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Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries

Desalination, Год журнала: 2024, Номер 592, С. 118092 - 118092

Опубликована: Сен. 7, 2024

Язык: Английский

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Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis

Energies, Год журнала: 2023, Номер 16(23), С. 7785 - 7785

Опубликована: Ноя. 27, 2023

The sustainability and efficiency of the wind energy industry rely significantly on accuracy reliability speed forecasting, a crucial concern for optimal planning operation power generation. In this study, we comprehensively evaluate performance eight prediction models, spanning statistical, traditional machine learning, deep learning methods, to provide insights into field forecasting. These models include statistical such as ARIMA (AutoRegressive Integrated Moving Average) GM (Grey Model), like LR (Linear Regression), RF (random forest), SVR (Support Vector well comprising ANN (Artificial Neural Network), LSTM (Long Short-Term Memory), CNN (Convolutional Network). Utilizing five common model evaluation metrics, derive valuable conclusions regarding their effectiveness. Our findings highlight exceptional particularly Convolutional Network (CNN) model, in prediction. stands out its remarkable stability, achieving lowest mean squared error (MSE), root (RMSE), absolute (MAE), percentage (MAPE), higher coefficient determination (R2). This underscores model’s outstanding capability capture complex patterns, thereby enhancing renewable industry. Furthermore, emphasized impact parameter tuning external factors, highlighting potential further improve accuracy. hold significant implications future development

Язык: Английский

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A novel micromagnetic carrier-modified integrated fixed-film activated sludge system for simultaneous efficient removal of tetracycline and mitigation of antibiotic resistance genes proliferation and dissemination from swine wastewater

Water Research, Год журнала: 2025, Номер 274, С. 123166 - 123166

Опубликована: Янв. 20, 2025

Язык: Английский

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The Impact of Bacteria on Nitrous Oxide Emission from Wastewater Treatment Plants: Bibliometric Analysis

Sustainability, Год журнала: 2025, Номер 17(4), С. 1592 - 1592

Опубликована: Фев. 14, 2025

Nitrous oxide (N2O) is a potent greenhouse gas and contributor to ozone depletion, with wastewater treatment plants (WWTPs) serving as significant sources of emissions due biological processes involving bacteria. This study evaluates research on the role bacteria in N2O from WWTPs between 2000 2023 based an analysis Web Science Core Collection Database using keywords “bacteria”, “nitrous oxide”, “emission”, “wastewater plant”. The findings reveal substantial growth past decade, leading publications appearing Water Research, Bioresource Technology, Environmental & Technology. China, United States, Australia have been most active contributors this field. Key topics include denitrification, treatment, emissions. microbial community composition significantly influences WWTPs, bacterial consortia playing pivotal role. However, further needed explore strain-specific genes, enzyme expressions, differentiation contributing production emission. System design operation must also consider dissolved oxygen nitrite concentration factors. Advances genomics artificial intelligence are expected enhance strategies for reducing WWTPs.

Язык: Английский

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