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Prediction of effluent quality in iceas-sequential batch reactor using feedforward artificial neural network

WATER SCIENCE AND TECHNOLOGY 2019年第2期80卷 213-222页

作者： Khatri, Narendra Khatri, Kamal Kishore Sharma, Abhishek LNM Inst Informat Technol Dept Mech Mechatron Engn Jaipur 302031 Rajasthan India LNM Inst Informat Technol Dept Elect & Commun Engn Jaipur 302031 Rajasthan India

It is highly essential that municipal wastewater is treated before its discharge and reuse in order to meet the standard requirements for safe marine life and for farming and industries. It is beneficial to use reclaimed water, since availability of fresh water is inadequate. An investigation was conducted on the Jamnagar Municipal Corporation Sewage Treatment Plant (JMC-STP) to develop a feedforward artificial neural network (FF-ANN) model. It is an alternate for the modelling/prediction of JMC-STP to circumvent over the versatile physical, chemical, and biological treatment process simulations. The models were developed to predict effluent quality parameters through influent characteristics. The parameters are pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), ammonium nitrogen (AN) and total phosphorus (TP). The correlation coefficient R-TRAINING and R-ALL were calculated for all parametric models. The MAD (mean absolute deviation), MSE (mean square error), RMSE (root mean square error) and MAPE (mean absolute percentage error) were evaluated for FF-ANN models. This proves to be a useful tool for the plant management to optimize the treatment quality as it enhances the performance and reliability of the plant. The simulation results were validated through the measured values.

关键词： feedforward artificial neural network iceas-sequential batch reactor prediction of effluent quality sewage treatment plant

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