Articles | Volume 11, issue 1
© Author(s) 2018. This work is distributed underthe Creative Commons Attribution 4.0 License.
Optimum coagulant forecasting by modeling jar test experiments using ANNs
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- Artificial Neural Network (ANN) Modelling of Palm Oil Mill Effluent (POME) Treatment with Natural Bio-coagulants N. Mohd Najib et al. 10.1007/s40710-020-00431-w
- Cationic Starch and Polyaluminum Chloride as Coagulants for River Nile Water Treatment S. Abdo et al. 10.1016/j.gsd.2020.100331
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- Towards sustainable and energy efficient municipal wastewater treatment by up-concentration of organics H. Guven et al. 10.1016/j.pecs.2018.10.002
- Complementarity‐based selection strategy for genomic selection S. Moeinizade et al. 10.1002/csc2.20070
- Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant S. Abba et al. 10.1016/j.jwpe.2019.101081
- Development of reservoir’s optimum operation rules considering water quality issues and climatic change data analysis B. Yaghoubi et al. 10.1016/j.scs.2020.102467
- Potential application of natural coagulant extraction from walnut seeds for water turbidity removal T. Zedan et al. 10.2166/wpt.2022.019
- Application of cascade feed forward neural network to predict coagulant dose D. Wadkar et al. 10.1080/23249676.2021.1927210
- Application of soft computing in water treatment plant and water distribution network D. Wadkar et al. 10.1080/23249676.2021.1978881
- Determination of coagulant dosages for process control using online UV-vis spectra of raw water Z. Shi et al. 10.1016/j.jwpe.2021.102526
Latest update: 01 Jun 2023