{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T17:48:07Z","timestamp":1762624087241,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,18]],"date-time":"2018-10-18T00:00:00Z","timestamp":1539820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A method for gas\u2013solid two-phase flow pattern identification in horizontal pneumatic conveying pipelines is proposed based on an electrostatic sensor array (ESA) and artificial neural network (ANN). The ESA contains eight identical arc shaped electrodes. Numerical simulation is conducted to discuss the contributions of the electrostatic signals to the flow patterns according to the error recognition rate, and the results show that the amplitudes of the output signals from each electrode of the ESA can give important information on the particle distribution and further infer the flow patterns. In experiments, the average values and standard deviations of the eight output signals\u2019 amplitudes are respectively extracted as the inputs of the ANN to identify four kinds of flow patterns in a pneumatic conveying pipeline, which are fully suspended flow, stratified flow, dune flow and slug flow. Results show that for any one of those two input values, the correct rates of the ANN model are all 100%.<\/jats:p>","DOI":"10.3390\/s18103522","type":"journal-article","created":{"date-parts":[[2018,10,18]],"date-time":"2018-10-18T10:55:41Z","timestamp":1539860141000},"page":"3522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Gas\u2013Solid Two-Phase Flow Pattern Identification Based on Artificial Neural Network and Electrostatic Sensor Array"],"prefix":"10.3390","volume":"18","author":[{"given":"Fei-fei","family":"Fu","sequence":"first","affiliation":[{"name":"School of Physics and Technology, University of Jinan, Jinan 250022, China"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15268","DOI":"10.1021\/ie3011897","article-title":"Flow pattern characteristics in vertical dense-phase pneumatic conveying of pulverized coal using Electrical Capacitance Tomography","volume":"51","author":"Cong","year":"2012","journal-title":"Ind. 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