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Mass Flow Rate Measurement of Solids in a Pneumatic Conveying Pipeline in Different Orientations

Abbas, Faisal, Wang, Lijuan, Yan, Yong (2020) Mass Flow Rate Measurement of Solids in a Pneumatic Conveying Pipeline in Different Orientations. Measurement: Sensors, 10-12 . Article Number 100021. ISSN 2665-9174. (doi:10.1016/j.measen.2020.100021) (KAR id:83292)

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Abstract

Extensive work has been undertaken for the mass flow rate measurement of solids in a horizontal or vertical pneumatic conveying pipe. However, flow regime of the two-phase flow is highly influenced by different orientations of the pipe, resulting in different characteristics of sensor signals and hence large errors in mass flow rate measurement using conventional methods. This paper presents a novel technique to measure the mass flow rate of pneumatically conveyed particles in different pipe orientations. A range of low-cost sensors, including an array of electrostatic sensors, a differential-pressure transducer, and an accelerometer, are integrated to form a sensing unit. Data-driven models, based on support vector machine (SVM), are developed to take the selected features from post-processed sensor data and infer the mass flow rate of solids in different pipe orientations. The partial mutual information algorithm is applied to quantify the importance of each feature. The firefly algorithm is used to optimize the selection of useful features and tune the learning parameters in SVM models. Experimental tests were conducted on a pneumatic conveying test rig circulating flour over the mass flow rate of solids from 3.2 g/s to 35.8 g/s in pipe orientations from 0° to 90°. Performance comparisons are made between the conventional SVM model and the optimised SVM models with the training data from horizontal orientation and different orientations, respectively. Results demonstrate that the relative error and repeatability of the measured mass flow rate of solids with the optimized SVM model are both improved to within ±12%.

Item Type: Article
DOI/Identification number: 10.1016/j.measen.2020.100021
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Lijuan Wang
Date Deposited: 06 Oct 2020 21:49 UTC
Last Modified: 10 Mar 2021 10:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/83292 (The current URI for this page, for reference purposes)
Wang, Lijuan: https://orcid.org/0000-0002-2517-2728
Yan, Yong: https://orcid.org/0000-0001-7135-5456
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