Mass Flow Rate Measurement of Pneumatically Conveyed Solids Through Multimodal Sensing and Data-Driven Modeling

Abstract

Online continuous measurement of the mass flow rate of pneumatically conveyed solids is desirable in the monitoring and optimization of a range of industrial processes, such as food processing, chemical engineering, and power generation. This article introduces a technique for the mass flow rate measurement of pneumatically conveyed solids based on multimodal sensing and data-driven modeling. The multimodal sensing system is comprised of an array of ring-shaped electrostatic sensors, four arrays of arc-shaped electrostatic sensors, and a differential-pressure (DP) transducer. Data-driven models, including artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN), are established through training with statistical features extracted from the postprocessed data from the sensing system. Statistical features are shortlisted based on their importance by calculating the partial mutual information (PMI) between the features and the corresponding reference mass flow rate of solids. Experimental work was conducted on a laboratory-scale rig to train and test the models on both horizontal and vertical pipelines with particle velocity ranging from 10.1 to 36.0 m/s and mass flow rate of solids from 3.2 to 35.8 g/s. Experimental results suggest that the ANN, SVM, and CNN models predict the mass flow rate of solids with a relative error within ±18%, ±14%, and ±8%, respectively, under all test conditions. The predicted mass flow rate measurements with the ANN, SVM, and CNN models are repeatable with a normalized standard deviation within 14%, 8%, and 5%, respectively, under all test conditions.