Modeling of Sludge Compost Maturity Degree Based on Radial Basic Function Network for Sewage Treatment

Because of the complicated interaction of the sludge compost components, it makes the compost maturity degree judging system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost maturity degree modeling method based on radial basic function neural network (RBFNN) is presented. We select the index of compost maturity degree and take these indexes as the judgment parameters. We construct the structure of RBFNN that used for the maturity degree judgment of sludge compost, and adopt the K-nearest neighbor algorithm and least square method to train the network. With the ability of strong function approach and fast convergence of radial basic function network, the modeling method can truly judge the sludge compost maturity degree by learning the index information of compost maturity degree. The experimental results show that this method is feasible and effective.