Abstract
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.
Highlights
In the past few years, previous studies have demonstrated that several PAEs can take the biodegradation under aerobic conditions and anaerobic conditions[10,11,12,13,14,15], in activated sludge[16,17] and in acclimated sludge[18,19,20,21]
wavelet neural network (WNN), which take the advantages of neural networks (NN) and wavelet transform (WT), are designed by using wavelet functions as the neuron’s activation functions and can be regarded as the function-linked networks based wavelet function
Treatment system, the degradation models including biodegradation and sorption according to the Activated Sludge Model (ASM2) was developed based on the fate of Dimethyl phthalate (DMP), which had been described by Huang et al.[32,36] rh(anaerobic)
Summary
In the past few years, previous studies have demonstrated that several PAEs can take the biodegradation under aerobic conditions and anaerobic conditions[10,11,12,13,14,15], in activated sludge[16,17] and in acclimated sludge[18,19,20,21]. Due to the highly nonlinearity and complexity of degradation mechanism for DMP, traditional mathematical methods are hard to exactly to model and simulate the biodegradation process[22]. There is a shortcoming for WNN31, which is difficult to understand the mapping rules This is exactly the advantage of fuzzy logic (FL). Combining the advantages of NN, FL and WT, a novel hybrid intelligent technique- fuzzy wavelet neural network (FWNN), which make effective use of self learning and memory abilities of NN, handling uncertainty capacity of FL and analyzing local details superiority of WT, could be constructed to enhance the abilities of approximation accuracy, convergence rate and generalization[26]. Compared with other conventional modeling techniques, the hybrid FWNN provide a more powerful way for process modeling, simulation and optimizing, for complex wastewater treatment process
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