Simulation of biodegradation process in a fluidized bed bioreactor using genetic algorithm trained feedforward neural network

Abstract The biodegradation process of phenol in a fluidized bed bioreactor (FBR) has been simulated using genetic algorithm trained feedforward neural network. Experiments were carried out using the microorganism Pseudomonas sp. on synthetic wastewater. The steady state model equations describing the biodegradation process have been solved using feedforward artificial neural network (FFANN) and genetic algorithm (GA). The mathematical model has been directly mapped onto the network architecture and the network has been used to find an error function (mean squared error criterion). The minimization of the error function with respect to network parameters (weights and biases) has been considered as training of the network. Real-coded genetic algorithm has been used for training the network in an unsupervised manner. The diffusivities of phenol and oxygen in biofilm obtained from the simulation have been compared with the literature values.

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