Predicting BOD of Greywater using Artificial Neural Networks

Predicting BOD of Greywater using Artificial Neural Networks
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© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Samir Sadik Shaikh, Rekha Shahapurkar
https://doi.org/10.14445/22315381/IJETT-V70I3P222

How to Cite?

Samir Sadik Shaikh, Rekha Shahapurkar, "Predicting BOD of Greywater using Artificial Neural Networks," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 195-200, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P222

Abstract
The performance of an artificial neural network (ANN) model in evaluating the quality of water measures, such as BOD for greywater, is investigated in this article. Representative criteria for greywater quality include chemical oxygen demand (COD) and biochemical oxygen demand (BOD), along with indirect organic matter indicators. Mean square error (MSE) measurements were used to assess the ANN models` performance. The ANN model outperformed with MLR model in terms of performance, according to the results. MSE = 0.1299. Comparative indices of the improved ANN using temperature (T), pH, total suspended solids (TSS), chemical oxygen demand (COD), also total solids (TS) as input variables for BOD prediction were MSE = 0.1299. The ANN model was shown to be effective in predicting greywater BOD and COD levels. Furthermore, sensitive research findings revealed that the pH parameter has a greater impact on BOD and COD when compared to other factors.

Keywords
ANN, BOD, Greywater, Keras model, MSE.

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