# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 1 Introduction  
With the changing face of the world, adopting technology in the industrial processes for the purpose of automation is inevitable. Artificial intelligence (AI) is radically transforming the industrial processes to ensure the mitigation of errors or risks induced by manual interventions, simultaneously allowing the processes to be more structured [1]. The same idea of the digital revolution also inspires our research. The belt conveyor system is an example of a widely used means of transporting a variety of items, especially in the industry, having the capability to carry thousands of tons of material per hour through a simple mechanism [2]. Quantifying the real-time flow of items over the belt is crucial for many use cases; for instance, measuring the flow aids in effective planning, adjusting the speed of the belt or for the effective use of energy, etc. This research targets to study a similar conveyor system installed in the soda ash manufacturing plant at LCI, Khewara Pakistan. For the production of soda ash, raw materials, such as coke and limestone, are used to generate 40% pure carbon dioxide gas [3]. The process consists of several stages from which the Belt Conveyor is being studied on which the raw materials, i.e., limestone and coke, are transported. First, these materials are carried to a vertical lime kiln where they are heated at a high temperature to produce gas which is further used in soda ashproduction. The amount of coke mixed with limestone is controlled through a variable-frequency drive (VFD) installed at the source from where coke is passed to the conveyor belt. This drive is used to regulate the flow of coke on the belt because the optimal amount of coke in the mixture of these two substances controls the purity of gas produced through it and makes the production cost-effective. Figure 1 shows a schematic diagram of the environment. The empirical data revealed that conveyor belt system had been a focus of numerous studies in terms of researching the actual amount of material on belt [4; 5; 6], tracking the movements [7; 8; 9], checking for product quality, detecting faults, speed regulation strategy and variable belt-speed energy issue for energy saving [10], etc. These researches discuss various techniques for extracting the material from the belt through computer vision techniques, such as background subtraction, canny edge detection, and morphological operations for analysis and quantification of the material to perform further processes on it to achieve their desired goals [4]. The recent past has witnessed tremendous growth in computer vision which has led to it being widely used in the industry as an automation tool. As righteously quoted by [PERSON] and [PERSON] [1], artificial systems empowered by computer vision are being created to boost the quality and efficiency of production while replacing the need for human intervention. Nowadays, the application of artificial intelligence (AI) in the manufacturing industry covers the areas of product assembly, fault detection, 3D vision system, computer vision-guided Die Cutting, Predictive Maintenance, Safety and Security Standards, Packaging Standards, Barcode Analysis, Inventory Management, and many more, inducing the possibility of increasing the production, controlling downtimes, reducing costs, regulating security and enhancing the quality of production [11]. Taking inspiration from these ideas, several researchers started looking into automating the conveyor belt problems to detect faults, check for product quality, maintain product flow, etc. For instance, [PERSON] et al. [4] initiated their research on these lines to assess the quantity of material on the belt system and develop an intelligent system that could regulate the speed of the belt accordingly to reduce wastage in terms of energy and equipment. A similar study was put forward to measure the real-time load on a conveyor belt with the help of vision techniques by [PERSON] et al. [12].