194 related articles for article (PubMed ID: 26040735)
1. Prediction of effluent concentration in a wastewater treatment plant using machine learning models.
Guo H; Jeong K; Lim J; Jo J; Kim YM; Park JP; Kim JH; Cho KH
J Environ Sci (China); 2015 Jun; 32():90-101. PubMed ID: 26040735
[TBL] [Abstract][Full Text] [Related]
2. Performance evaluation of hybrid constructed wetlands for nitrogen removal and statistical approaches.
Kumar S; Sangwan V; Kumar M; Shweta S; Shivani S; Kumar M; Deswal S
Water Environ Res; 2023 Oct; 95(10):e10932. PubMed ID: 37759364
[TBL] [Abstract][Full Text] [Related]
3. Improved neural network with least square support vector machine for wastewater treatment process.
Zhu J; Jiang Z; Feng L
Chemosphere; 2022 Dec; 308(Pt 1):136116. PubMed ID: 36037940
[TBL] [Abstract][Full Text] [Related]
4. Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.
Park Y; Cho KH; Park J; Cha SM; Kim JH
Sci Total Environ; 2015 Jan; 502():31-41. PubMed ID: 25241206
[TBL] [Abstract][Full Text] [Related]
5. Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology.
Rios Fuck JV; Cechinel MAP; Neves J; Campos de Andrade R; Tristão R; Spogis N; Riella HG; Soares C; Padoin N
Chemosphere; 2024 Mar; 352():141472. PubMed ID: 38382719
[TBL] [Abstract][Full Text] [Related]
6. Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants.
Elsayed A; Ghaith M; Yosri A; Li Z; El-Dakhakhni W
J Environ Manage; 2024 Apr; 356():120510. PubMed ID: 38490009
[TBL] [Abstract][Full Text] [Related]
7. Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning.
Lv J; Du L; Lin H; Wang B; Yin W; Song Y; Chen J; Yang J; Wang A; Wang H
Bioresour Technol; 2024 Feb; 393():130008. PubMed ID: 37984668
[TBL] [Abstract][Full Text] [Related]
8. Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods.
Ye G; Wan J; Deng Z; Wang Y; Chen J; Zhu B; Ji S
Bioresour Technol; 2024 Mar; 395():130361. PubMed ID: 38286171
[TBL] [Abstract][Full Text] [Related]
9. A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant.
Xie Y; Chen Y; Wei Q; Yin H
Water Res; 2024 Feb; 250():121092. PubMed ID: 38171177
[TBL] [Abstract][Full Text] [Related]
10. Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study.
Gholizadeh M; Saeedi R; Bagheri A; Paeezi M
Environ Res; 2024 Apr; 246():118146. PubMed ID: 38215928
[TBL] [Abstract][Full Text] [Related]
11. Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study).
Hejabi N; Saghebian SM; Aalami MT; Nourani V
Water Sci Technol; 2021 Apr; 83(7):1633-1648. PubMed ID: 33843748
[TBL] [Abstract][Full Text] [Related]
12. Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm.
Zhou M; Zhang Y; Wang J; Shi Y; Puig V
Sensors (Basel); 2022 Jan; 22(2):. PubMed ID: 35062384
[TBL] [Abstract][Full Text] [Related]
13. Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application.
Wang Z; Dai H; Chen B; Cheng S; Sun Y; Zhao J; Guo Z; Cai X; Wang X; Li B; Geng H
J Environ Manage; 2024 Feb; 351():119900. PubMed ID: 38157580
[TBL] [Abstract][Full Text] [Related]
14. Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm.
Xie B; Ma YW; Wan JQ; Wang Y; Yan ZC; Liu L; Guan ZY
Environ Sci Pollut Res Int; 2018 Jul; 25(21):20956-20967. PubMed ID: 29766428
[TBL] [Abstract][Full Text] [Related]
15. Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model.
Li B; Lu C; Zhao J; Tian J; Sun J; Hu C
J Environ Manage; 2023 May; 333():117416. PubMed ID: 36758403
[TBL] [Abstract][Full Text] [Related]
16. Neighborhood component analysis for modeling papermaking wastewater treatment processes.
Zhang Y; Yang J; Huang M; Liu H
Bioprocess Biosyst Eng; 2021 Nov; 44(11):2345-2359. PubMed ID: 34226973
[TBL] [Abstract][Full Text] [Related]
17. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method.
Zhang R; Xie WM; Yu HQ; Li WW
Bioresour Technol; 2014 Apr; 157():161-5. PubMed ID: 24556369
[TBL] [Abstract][Full Text] [Related]
18. Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration.
Wang R; Yu Y; Chen Y; Pan Z; Li X; Tan Z; Zhang J
J Environ Manage; 2022 Jan; 302(Pt A):114020. PubMed ID: 34731713
[TBL] [Abstract][Full Text] [Related]
19. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance.
Mjalli FS; Al-Asheh S; Alfadala HE
J Environ Manage; 2007 May; 83(3):329-38. PubMed ID: 16806660
[TBL] [Abstract][Full Text] [Related]
20. Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant.
Elmaadawy K; Elaziz MA; Elsheikh AH; Moawad A; Liu B; Lu S
J Environ Manage; 2021 Nov; 298():113520. PubMed ID: 34391109
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]