TY - JOUR
T1 - Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network
AU - Khatri, Narendra
AU - Khatri, Kamal Kishore
AU - Sharma, Abhishek
N1 - Funding Information:
The contributions of Centre of Material Science & Energy Studies of the LNMIIT, Jaipur and the generous support for study and data collection from JMC-STP authorities and ESSAR Projects Limited India are gratefully acknowledged.
Publisher Copyright:
© IWA Publishing 2019.
PY - 2019/7/15
Y1 - 2019/7/15
N2 - 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 RTRAINING and RALL 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.
AB - 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 RTRAINING and RALL 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.
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U2 - 10.2166/wst.2019.257
DO - 10.2166/wst.2019.257
M3 - Article
C2 - 31537757
AN - SCOPUS:85072393666
SN - 0273-1223
VL - 80
SP - 213
EP - 222
JO - Water Science and Technology
JF - Water Science and Technology
IS - 2
ER -
