Optimization of photocatalytic oxidation by using of artificial network and genetic algorithm

فاقد چكيده

Soft computing technique for advanced experimental design was applied to optimization of photocatalytic degradation of phenol by new composite nano-catalyst (TiO2/perlite). In the ANN-GA approach, an artificial neural network (ANN) model is constructed for correlating process data. The operating conditions were consisted of initial pH, concentration, reaction temperature, catalyst dosage, UV irradiation time, UV light intensity and calcination temperature and the phenol conversion is output variable. Then, model inputs describing process operating variables are optimized using genetic algorithms (GAs) with a view to maximize the process performance. Testing results demonstrate that the model is suitable for predicting the response parameter. It is observed that the error when the network is optimized by genetic algorithm has come down to less than 2%.