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

Narendra Khatri, Kamal Kishore Khatri, Abhishek Sharma

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)213-222
Number of pages10
JournalWater Science and Technology
Issue number2
Publication statusPublished - 15-07-2019

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Water Science and Technology


Dive into the research topics of 'Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network'. Together they form a unique fingerprint.

Cite this