Study of periodic fouling prediction method based on fuzzy neural network
Zhou Ze-kui · Journal of Zhejiang University(Engineering Science) · 2004
For batch heat exchangers, washing and CIP (cleaning in place) processes cause periodic fouling and other undesirable changes. A novel FNN-based prediction method was developed for periodic fouling measurements. Reversible fouling and irreversible fouling comprise periodical fouling. Two MISO (multi input single output) four-layer fuzzy neural networks were trained to learn the short-term reversible fouling growth trend and long-term irreversible fouling growth trend. The combination of two network outputs provides the prediction for overall fouling. The results of experiments show that the method, compared to the experimental formula, has better performance in prediction wort evaporator fouling. Using the predicted fouling values, a compensation factor for time-varying gain was applied successfully in the evaporation intensity control system.