基于SSA-CNN-CBAM的超声气固两相流浓度信息识别OA

Pneumatic transportation has always been an important part of some industrial production processes,and the accurate and real-time detection of gas-solid two-phase flow in metal pipelines has always been a major challenge with the continuous improvement of detection technology requirements in industrial environments.To solve this problem,a hybrid model is proposed combining Sparrow Optimization Algorithm(SSA),Convolutional Neural Network(CNN)and Attention Module(CBAM)to identify the concentration of gas-solid two-phase flow.SSA is used to optimize the parameters of the CNN-CBAM model,and the SSA-CNN-CBAM hybrid model is constructed for ultrasonic backscatter waveform recognition,and the classification results of concentration are obtained.Through repeated experiments,the experimental results of the mixed model are compared with the experimental results of the SSA-CNN model,the CNN-CBAM model and the CNN model,and the results show that the proposed model is more ideal for the classification and identification of gas-solid two-phase flow concentration.The test results show that the method can realize the real-time concentration detection of gas-solid two-phase flow transportation,which provides a reference for the detection of gas-solid two-phase flow transportation in industry,and provides new ideas and methods for the development and application of related technologies.