Real-time Monitoring of Fluidized Bed Agglomerating based on Improved Adaboost Algorithm

Abstract

Absctract: In order to to detect the polymer agglomeration in fluidized bed reactor (FBR), a method of real-time monitoring of agglomeration in fluidized bed polyolefin reactor based on voiceprint feature recognition is developed. First, the acoustic emission detection technology is applied to collect the acoustic signal generated by the polymer collision on the inner wall of FBR. Then, the voiceprint features of the collected acoustic signal are extracted with the Mel Frequency Cepstrum Coefficients (MFCC) and the Linear Prediction Cepstrum Coefficients (LPCC). To classify the extracted voiceprint features, an improved Adaboost algorithm is proposed to establish the real-time agglomeration classification model. Due to the introduction of cost factor and Gini index decision-making calculation to the Adaboost algorithm, the proposed improved Adaboost algorithm can classify unbalanced small samples with better accuracy and F-score index compared with the traditional Adaboost algorithm. The experiment results in a fluidized bed pilot plant have verified the effectiveness and feasibility of the proposed method.