Journal of Water Process Engineering, Год журнала: 2025, Номер 75, С. 107976 - 107976
Опубликована: Май 21, 2025
Язык: Английский
Journal of Water Process Engineering, Год журнала: 2025, Номер 75, С. 107976 - 107976
Опубликована: Май 21, 2025
Язык: Английский
The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175407 - 175407
Опубликована: Авг. 9, 2024
Язык: Английский
Процитировано
27Water, Год журнала: 2025, Номер 17(3), С. 310 - 310
Опубликована: Янв. 23, 2025
When confronted with different influent conditions, WWTPs often lack targeted and effective operational control strategies. For the three typical scenarios of low C/N, water temperature high temperature, 441 carbon source dosage DO concentration coordination strategies were designed under premise ensuring effluent quality meets standard. The purpose was to provide clear guidance for efficient operation in scenarios. To determine optimal strategy, prediction model based on LSTM GRU constructed testing. results showed that: (1) LSTM-GRU is better than SVR RF predicting COD TN; (2) In C/N scenario, should be controlled between 0.23 t/h 0.26 t/h, ranging from 2.0 mg/L 2.6 mg/L; (3) 0.25 0.27 2.8 (4) 0.20 2.5 mg/L.
Язык: Английский
Процитировано
1Results in Engineering, Год журнала: 2025, Номер unknown, С. 104158 - 104158
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1Environmental Research, Год журнала: 2025, Номер unknown, С. 121127 - 121127
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1Water Research X, Год журнала: 2024, Номер 26, С. 100291 - 100291
Опубликована: Дек. 3, 2024
Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment treated effluents discharge quality standards. To aid real-time decision-making for stable WWTP operations, this study developed probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity producing probability predictions, enhance robustness effluent prediction under such events. The LSTM (P-ED-LSTM) was tested an actual WWTP, where bihourly total nitrogen performed and compared classical models, including LSTM, gated recurrent unit (GRU) Transformer. It found events, P-ED-LSTM could achieve 49.7% improvement accuracy predictions concentration GRU, A higher quantile data from output, indicated value more approximate real quality. also exhibited predictive power next multiple time steps scenarios. captured approximately 90% over-limit discharges up 6 hours ahead, significantly outperforming other models. Therefore, model, its robust adaptability fluctuations, has potential broader applications across WWTPs different processes, as well providing strategies system regulation emergency conditions.
Язык: Английский
Процитировано
5Process Safety and Environmental Protection, Год журнала: 2024, Номер 188, С. 995 - 1008
Опубликована: Май 31, 2024
Язык: Английский
Процитировано
4Water Research, Год журнала: 2024, Номер 266, С. 122315 - 122315
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
4Journal of Water Process Engineering, Год журнала: 2025, Номер 74, С. 107784 - 107784
Опубликована: Апрель 23, 2025
Язык: Английский
Процитировано
0Chemical Engineering Journal, Год журнала: 2025, Номер 516, С. 164048 - 164048
Опубликована: Май 26, 2025
Язык: Английский
Процитировано
0Euro-Mediterranean Journal for Environmental Integration, Год журнала: 2024, Номер unknown
Опубликована: Окт. 3, 2024
Abstract Membrane filtration processes have demonstrated remarkable effectiveness in wastewater treatment, achieving high contaminant removal and producing high-quality effluent suitable for safe reuse. technologies play a primary role combating water scarcity pollution challenges. However, the need more effective strategies to mitigate membrane fouling remains critical concern. Artificial intelligence (AI) modeling offers promising solution by enabling accurate predictions of fouling, thus supporting advanced mitigation strategies. This review examines recent progress application AI models, with particular focus on artificial neural networks (ANNs), simulating treatment processes. It highlights substantial potential ANNs, particularly widely studied multi-layer perceptron (MLP) other emerging configurations, accurately predict thereby enhancing process optimization efforts. The discusses both benefits current limitations AI-based strategies, analyzing studies offer valuable insights designing ANNs capable providing predictions. Specifically, it provides guidance selecting appropriate model architectures, input/output variables, activation functions, training algorithms. Finally, this connect research findings practical applications full-scale plants. Key steps crucial address challenge been identified, emphasizing revolutionize control drive paradigm shift toward efficient sustainable membrane-based treatment.
Язык: Английский
Процитировано
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