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In this study, aluminum chloride-poly (acrylamide-co-dimethyldiallyammonium chloride) inorganic- organic hybrid copolymer was synthesized by free radical solution polymerization. The polymerization was initiated by the redox initiation system (NH 4)2S2O8 and NaHSO3 at 45°C in an aqueous medium. The AlCl3-P(AM-co- DADAAC) inorganic-organic hybrid copolymer was characterized by Fourier Transform Infrared spectroscopy (FT- IR) and Transmission Electron Microscopy (TEM). The AlCl 3-P(AM-co-DADAAC) hybrid copolymer was employed to treat the turbidity of kaolin suspension. Taguchi's experimental design method was used to determine the optimal conditions for turbidity removal. The design variables in this research were the initial concentration of kaolin suspension, pH and the AlCl3-P(AM-co-DADAAC) hybrid copolymer dosage. Taguchi Orthogonal arrays, the Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) were utilized to determine the optimal level and to analyze the effect of design variables in the flocculation process on the turbidity removal. ANN model was per formed to predict the final turbidity. According to the values of the error analysis and the coefficient of determination, ANN model was found that the proposed model was more appropriate to describe the turbidity reduction using the AlCl 3-P(AM-co-DADAAC) hybrid copolymer in the flocculation process. The Levenberg- Marquardt Algorithm (LMA) was found to be the best of the six proposed Back Propagation (BP) algorithms with a minimum Mean Squared Error (MSE). The optimum neuron number in the hidden layer of the LMA was 12 neurons with MSE of 0.4438. Hence, ANN presented a very good performance for turbidity response value.

Taguchi and ANN Modeling of Turbidity Removal Using New Hybrid Flocculant