Z. Liu, J. Xu, X. Wang, L. Cui, and X. Lian (PRC)
Mathematic model, RBF neural network, genetic algorithm, sewage disposal, activated sludge process
A mathematic model based on RBF neural network and genetic algorithm for multivariable optimal control with the lowest operational cost by limiting total substrate discharge in sewage disposal process was discussed. Intelligent soft sensing with neural network may be used to fulfill measurement of the effluent BOD(Biochemical Oxygen Demand) from the sewage disposal system. Based on satisfying the requirements of precision, binary coding was used to express units, and 20 bits of binary digits expressed DO, Qw separately. The adaptive degree of units can be operated genetically through genetic operator. It shows that the RBF neural network has preferable convergence for modeling the sewage disposal process. Genetic algorithm is an effective searching method to resolve the optimal problem in this case. The optimization strategy made up of RBF neural network and genetic algorithms is adopted. After achieving the discharge standard of BOD, the control rule for variables to make operation cost be least is found.
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