Application of neural network and electrodynamic sensor as flow pattern identifier

Abstract

Purpose: Solid particles flowing in a pipeline is a common mode of transport in industries. This is because pipeline transportation can avoid waste through spillage and minimizes the risk of handling of hazardous materials. Pharmaceutical industries, food stuff manufacturing industries, cement, and chemical industries are a few industries to exploit this transportation technique. For such industries, monitoring and controlling material flow through the pipe is an essential element to ensure efficiency and safety of the system. The purpose of this paper is to present electrical charge tomography, which is one of the most efficient, robust, cost-effective, and non-invasive tomographic methods of monitoring solid particles flow in a pipeline. Design/methodology/ approach: Process flow data are captured by fitting an array of 16 discrete electrodynamic sensors about the circumference of the flow pipe. The captured data are processed using two tomographic algorithms to obtain tomographic images of the flow. Then a neural network tool is used to improve image resolution and accuracy of measurements. Findings: The results from the above technique show significant improvements in the pipe flow image resolution and measurements. Originality/value: The paper presents electrical charge tomography, which is one of the most efficient, robust, cost-effective, and non-invasive tomographic methods of monitoring solid particles flow in a pipeline.