Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline

Abstract

The desire to satisfy demand of industrial sector by improving product quality and reducing environmental emission leads up to identify and monitor the behaving of the internal flows inside pipelines. The flow of solid particles through pipeline in vertical gravity flow rig system has been monitored by 16-electrodynamic sensors that measure the charge carried by solid particles. The identification model has been built and developed based on the training of the captured data at different flow patterns. The final identification model consists of four ANFIS based fuzzy C-means clustering where every ANFIS is able to identify the presence of the flow inside specific quarter in the cross section of the pipe. It is shown that the four ANFIS models are able to work simultaneously to provide the expected output after applying simple thresholding for the ANFIS’ output. The identification model has been evaluated by ten different types of flow patterns. The accuracy of the identification model has improved at higher flow rate. As a result, the identified flow pattern has been used to acquire the concentration profile by using filtered back projection. The successful ANFIS model can be extended for horizontal pipeline to present the percentage of flow inside the pipe.