Prediction of Fouling in Condenser Based on Fuzzy Stage Identification and Chebyshev Neural Network

The prediction of fouling in condenser is heavily influenced by the periodic fouling process and dynamics change of the operational parameters, to deal with this problem, a novel approach based on fuzzy stage identification and Chebyshev neural network is proposed. In the approach, the overall fouling is separated into hard fouling and soft fouling, the variation trends of these two kinds of fouling are approximated by using Chebyshev neural network, respectively, in order to make the prediction model more accurate and robust, a fuzzy stage identification method and adaptive algorithm considering external disturbance are introduced, based on the approach, a prediction model is constructed and experiment on an actual condenser is carried out, the results show the proposed approach is more effective than asymptotic fouling model and adaptive parameter optimization prediction model.