Modeling and Optimization of Photocatalytic Degradation Process of 4-Chlorophenol using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
P.S. Patel, V. Gandhi, M.P. Shah, T.S. Natarajan, K. Natarajan, R.J. Tayade
Present study focuses on modeling and optimization of photocatalytic degradation of 4-Chlorophenol (4-CP) using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Titanium nanotube synthesized by hydrothermal method were used and characterized using various physico-chemical and electronic techniques. The influence of operational parameters was investigated by employing face centred experimental design.. Response surface model was developed and its significance was evaluated by an ANOVA study. This process was also modeled by a novel approach of ANN. A two layer feed forward neural network with back propagation algorithm was employed. Architecture of ANN was optimised based on fractional factorial design. Optimum parameters were found to be eight hidden layer neurons, tansig transfer function in the hidden as well as output layer. Optimum conditions were found using the response surface model with initial concentration of 40 mg/L, catalyst dose of 184.84 mg/L, and initial pH of 3.94. From the ANN model, the optimum conditions were found to be with initial concentration of 42 mg/L, catalyst dose of 212.51 mg/L, and initial pH of 3.38. These optimum conditions were experimentally verified and reasonably good agreement was found between predicted and experimental conditions.
Keywords
Photocatalytic Degradation, 4-Chlorophenol (4-CP), Response Surface Methodology (RSM), Artificial Neural Network (ANN)
Published online 2/25/2018, 28 pages
DOI: https://dx.doi.org/10.21741/9781945291593-15
Part of Photocatalytic Nanomaterials for Environmental Applications
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