The Use of Artificial Neural Network for Modeling Coagulation of Reactive Dye Wastewater Using Cassia fistula Linn. gum

Ha Manh Bui, Yuan Shing Perng, Huong Giang Thi Duong
Vol 19 No 1 (2016), pp. 1-8




Natural seed gum extracted from Cassia fistula Linn. (CF) was experimentally evaluated to treat reactive dye (Red 195) in an aqueous solution, whose color and Chemical Oxygen Demand (COD) were to measure the treatment efficiency. To investigate ve parameters i.e. pH, reaction time, agitation speeds, dye concentration and CF gum concentration were used to implement a one-factor-at-a-time experiment with Jar-test apparatus. Carried out under weak basic condition (pH 10) for 30 min, the COD and decolorization ef ciency of the dye stuff wastewater was observed at 42.4% and 57.8%, respectively. A single-layer Artificial Neural Network (ANN) model was also developed to predict the removal ef ciency of the dye by using the determination coeficient (R2) and the root mean square error (RMSE). The observed and predicted outputs were found to be 0.924 and 3.759, respectively. Furthermore, the ANN model was analysed using Garson’s algorithm, connection weight method, and neural interpretation diagram to understand the in uence of each operation factor on the treatment process. 



Keywords: Artificial neural network; dye removal; natural coagulant; reactive red 195


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