A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, a variable metric chaos optimization neural network (VMCNW) prediction model is established standing on the actual operation data in the wasterwater treatment system. The model overcomes several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum.The results of model calculation show that the predicted value can better match measured value,played a effect of simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provide a simple and practical way for the operation and management in wastewater treatment plant,and have good research and engineering practical value.
Sliding window neural network based sensing of bacteria in wastewater treatment plants
