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Application of support vector machine with simulated annealing algorithm in MBR membrane pollution prediction

In order to predict the MBR membrane flux more accurately, this paper studies the various models in the field of MBR, and it is difficult for the traditional neural network model to fall into the local minimum in the MBR film pollution application [1] The model of MBR film pollution prediction based on SA-SVM is established by using simulated annealing algorithm and support vector machine. In this paper, we first use the simulated annealing algorithm to search the three important parameters of the support vector machine, the insensitive coefficient and the kernel parameters, and then use the obtained optimal parameters as the initial parameters of the support vector machine and establish the MBR membrane pollution prediction model Finally, the factors influencing membrane fouling were analyzed by principal component analysis (PCA), and the main influencing factors were selected as the input of the model and the size of membrane flux as output. The experimental results show that the prediction model of MBR membrane contamination based on SA-SVM has better fitting effect and higher prediction accuracy than the previous GA-SVM model in membrane flux prediction.