Application of feed forward neural network for prediction of optimum coagulant dose in water treatment plant

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Abstract

Coagulation is anecessary process used mainly to reduce turbidity and natural organic matter in water treatment. The dosage of coagulant required is conventionally determined by carrying out jar tests which consume time and chemicals.In India, coagulant dose in a WTP remains constant during certain periods due to delay in jar testing, which may lead to under-dosing or over-dosing of coagulant. This research work is focused on applying artificial neural network (ANN) approach to predict coagulant dose in a WTP. Forty-eight months daily water testing data concerning inlet & outlet water turbidity and coagulant dose were obtained from the plant laboratory for ANN modelling. The appropriate architecture of feed forward neural network (FFNN) coagulant models were established with several steps of training and testing by applying various training algorithms vizLevenberg-Marquardt (LM) and Bayesian regularization (BR), resilient back propagation (RBP), one step secant(OSS),variants of conjugate gradient(CG) and modifications of gradient descent (GD) with evaluating coefficient of correlation (R) & mean square error (MSE). Further, best performed LM and BR training algorithm were used for development of four ANN models of FFNN for prediction of coagulant dose at WTP. FFNN coagulant model with BR training algorithm provided excellent estimates with network architecture (2-50-1) for coagulant dose with maximum value of R= 0.943 (training) and R = 0.945 (testing). Thus, ANN provided an effective diagnosing tool to understand the non-linear behavior of the coagulation process, and can be used as a valuable performance assessment tool for plant operators and decision makers.

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Kote, A. S., & Wadkar, D. V. (2019). Application of feed forward neural network for prediction of optimum coagulant dose in water treatment plant. International Journal of Innovative Technology and Exploring Engineering, 8(12), 1853–1856. https://doi.org/10.35940/ijitee.L2864.1081219

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