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Implementation of artificial neural network model for continuous hydrogen production using confectionery wastewater.

Yogeswari, M K; Dharmalingam, K; Mullai, P.
J Environ Manage; 252: 109684, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31622794
In the present study, an artificial neural network (ANN) was implemented to estimate the hydrogen production from confectionery wastewater. From the experimental investigation, it could be concluded that maximum COD removal efficiency of 99% and hydrogen production rate of 6570 mL/d was achieved at 7.00 kg COD/m3d and 24 h HRT. To validate this, a back propagation ANN configuration of 4-12-4-2 was opted. The modelling was performed using the input parameters like time, influent chemical oxygen demand (COD), effluent pH and volatile fatty acids (VFA). The correlation coefficient between the experimental and predicted hydrogen production rate was 0.996. The result of the tested data for hydrogen production rate was successful. The calculated average percentage error (APE) for hydrogen production rate was 0.0004. As the APE values were closer to zero, the trained ANN model fitted well with the experimental data.