Z
Zahra Bagheri
Publications - 12
Citations - 307
Zahra Bagheri is an academic researcher. The author has contributed to research in topics: HEC-HMS & HEC-RAS. The author has an hindex of 8, co-authored 12 publications receiving 237 citations.
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Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach
TL;DR: In this paper, the authors developed hybrid artificial neural network-genetic algorithm models (MLPANN-GA and RBFANNGA) to accurately predict sludge volume index (SVI) for a wastewater treatment plant.
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Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks
TL;DR: In this paper, a sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN) and the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH4+-N.
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Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm
TL;DR: In this paper, the effect of simultaneous upward and downward aeration on the membrane fouling and process performances of a submerged membrane bioreactor was simulated using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN).
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Performance evaluation and modeling of a submerged membrane bioreactor treating combined municipal and industrial wastewater using radial basis function artificial neural networks
TL;DR: The results showed that the treatment efficiencies increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and industrial wastewaters.
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Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models
TL;DR: In this article, the authors developed hybrid multilayer perceptron and radial basis function artificial neural network-genetic algorithm (MLPANN-GA and RBFANNGA) models to accurately predict effluent biochemical oxygen demand (BOD), chemical oxygen demand(COD), total nitrogen (TN), and total phosphorus (TP) in a submerged membrane bioreactor.