Response Surface Methodology and Artificial Neural Network for Modeling and Optimization of Distillery Spent Wash Treatment Using Phormidium valderianum BDU 140441
Rajarathinam Ravikumar1, Kumarasami Renuka2, Varadaraj Sindhu1, Kavindapadi B. Malarmathi3
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1Department of Biotechnology, Bannari Amman Institute of Technology, Sathyamangalam,
Erode District-638 401, Tamil Nadu, India
2Department of Biotechnology, Vivekanandha College of Engineering for Women, Namakkal,
Erode District-638052, Tamil Nadu, India
3Department of Biotechnology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
Pol. J. Environ. Stud. 2013;22(4):1143–1152
The aim of this work was to evaluate the capability of Phormidium valderianum BDU 140441 on biodegradation and decolorization of distillery spent wash. The effect of initial pH (6-10), temperature (24- 32ºC), and light intensity (20-54 W/m2) was studied using single factorial design and achieved a maximum decolorization of 74.5% with COD reduction of 83.48%. A 23 full factorial experimental central composite design (CCD) of response surface methodology (RSM) was used to investigate the interaction effect between these variables and the optimal values. The predicted results showed that a maximum decolorization of 85.5% and COD reduction of 87.29% was achieved under the optimum conditions of 8 pH, 30ºC, and light intensity of 36 W/m2. It was observed that model predictions were in good agreement with experimental values (R2 = 0.9830, Adj-R2 = 0.9677), which indicated the suitability of the model and the success of the optimization tool. A non-linear artificial neural network (ANN) model was developed to predict the biological decolorization of the spent wash. The results indicated that ANN revealed reasonable performance (R2=0.9999, y=0.9781x-0.5679).