DYNAMIC MODELING OF THE ACTIVATED-SLUDGE PROCESS - IMPROVING PREDICTION USING NEURAL NETWORKS

A procedure has been developed to improve the accuracy of an existing mechanistic model of the activated sludge process, previously described by Lessard and Beck [Wat. Res. 27, 963-978 (1993)]. As a first step, optimization of the numerous model parameters has been investigated using the downhill simplex method in order to minimize the sum of the squares of the errors between predicted and experimental values of appropriate variables. Optimization of various sets of parameters has shown that the accuracy of the mechanistic model; especially on the prediction of the dissolved oxygen (DO) in the mixed liquor, can be easily improved by adjusting only the values of the overall oxygen transfer coefficients, K(L)a. Then, in a second step, neural network models have been used successfully to predict the remaining errors of the optimized mechanistic model. The coupling of the mechanistic model with neural network models resulted in a hybrid model yielding accurate simulations of five key variables of the activate sludge process.