Flow regime identification and concentration distribution of solid particles flow in pipelines using electrodynamic tomography and artificial neural networks

Abstract

Solid particles flow in a pipeline is a common means of transportation 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 few of the 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. This paper presents electrical charge tomography which is one of the most efficient, robust, cost-effective and noninvasive tomographic methods of monitoring solid particles flow in a pipeline. Process flow data is captured fitting an array of 16-discrete electrodynamic sensors about the circumference of the flow pipe. The data captured is 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. The results from the above technique shows significant improvements in the pipe flow image resolution and measurements.