Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data

Abstract

Industries are rapidly moving toward mitigating errors and manual interventions by automating their process. The same motivation is carried out in this research which targets to study a conveyor system installed in soda ash manufacturing plants. Our aim is to automate the determination of optimal parameters, which are chosen by identifying the flow rate of the materials available on the conveyor belt for maintaining the ratio between raw materials being carried. The ratio is essential to produce 40% pure carbon dioxide gas needed for soda ash production. A visual sensor mounted on the conveyor belt is used to estimate the flow rate of the raw materials. After selecting the region of interest, a segmentation algorithm is defined based on a voting-based technique to segment the most confident region. Moments and contour features are extracted and passed to machine learning algorithms to estimate the flow rate of different experiments. An in-depth analysis is completed on various techniques and convincing results are achieved on the final data split with the best parameters using the Bagging regressor. Each step of the process is made resilient enough to work in a challenging environment even if the belt is placed in an outdoor environment. The proposed solution caters to the current challenges and serves as a practical solution for estimating material flow without manual intervention.

Keywords

• automation
• industrial process
• conveyor belt
• soda ash
• machine learning
• convolutional neural networks