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.

Real-time monitoring of molten zinc splatter using machine learning-based computer vision

“…Similarly, the CNT algorithm has been used in this work to precisely monitor zinc splatter in real-time to improve steel strip quality, reduce waste and costs, and minimise equipment downtime. Again, the dynamic splatter structure in this work is far more variable and complex than the raw materials in Sabih et al (2023), emphasising the advancement made by this study.…”

“…The approach here is different because although it still contributes to quality control, it involves continuously monitoring a dynamic process, and is also beneficial for process optimisation. The CNT algorithm was used in Sabih et al (2023) to distinguish raw materials on a conveyor belt system from the background, in order to monitor material flow rate on a soda-ash production line. In combination with a frame difference technique, the CNT algorithm proved to be effective in precise, real-time analysis required for process optimisation for improved gas production, reduced production waste and reduced costs (Sabih et al, 2023).…”

“…The CNT algorithm was used in Sabih et al (2023) to distinguish raw materials on a conveyor belt system from the background, in order to monitor material flow rate on a soda-ash production line. In combination with a frame difference technique, the CNT algorithm proved to be effective in precise, real-time analysis required for process optimisation for improved gas production, reduced production waste and reduced costs (Sabih et al, 2023). Similarly, the CNT algorithm has been used in this work to precisely monitor zinc splatter in real-time to improve steel strip quality, reduce waste and costs, and minimise equipment downtime.…”

Implementation of Quality Tools in Mechanical Engineering Piece Production

“…Visual Workplace creates workplace conditions where the worker gets quick, relevant, and correct information. In contrast, written text can be tedious to read and runs the risk of misinterpretation [64][65][66][67][68][69][70]. In the conditions of the studied company, it was necessary to visualize most of the management documents.…”