Twin-array capacitance sensor for multi-parameter measurement of gas-solid particle flow based on BP-Adaboost

Abstract

The measurement of gas-solid particle flow is a complex and error-prone task due to the intricacies of the flow process. To address the limitations associated with Electrical Capacitance Tomography (ECT) such as the complexity of hardware structure and the computational demands of imaging algorithms, this study proposes a novel TAC measurement method that utilizes the BP-Adaboost algorithm. The proposed method utilizes TAC to measure multiple parameters of the gas-solid particle flow under several working conditions and calibrate the measured flow rate with gravity sensors. At first, we conduct experiments to identify the working conditions that have the least impact on the measurements of concentration and velocity, offering valuable insights for subsequent two-phase flow measurement. The BP-Adaboost algorithm is then utilized to classify the flow pattern based on capacitance data obtained from the TAC sensor. Finally, the pattern, concentration, and velocity are used as inputs for BP-Adaboost neural network to optimize the mass flow rate and improve the measuring accuracy. Experimental results demonstrate the effectiveness of the method, with an accuracy of 83.3% for two-phase flow pattern classification and a mass flow rate error of 6.71%. Additionally, BP-Adaboost algorithm, an ensemble learning technique, effectively avoids overfitting, leading to good generalizability.