Multiple regression analysis for the prediction of extraction efficiency in mining industry with industrial IoT
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PRODUCTION PROCESS
Multiple regression analysis for the prediction of extraction efficiency in mining industry with industrial IoT C. Maheswari1 · E. B. Priyanka1 · S. Thangavel1 · S. V. Ram Vignesh1 · C. Poongodi2 Received: 6 April 2020 / Accepted: 10 June 2020 © German Academic Society for Production Engineering (WGP) 2020
Abstract Manufacturers and industrialists have a significant opportunity at hand in automation for the complex processes involved in manufacturing rather than labor-intensive based system analysis and control. Especially industrial IoT (IIoT) technology provides far more intricate details to the industrial automation for prompt decisions automatically through a web server. Hence in this present work, online data monitoring of important attributes associated with the mining industry during the extraction of zinc and lead are analyzed using IIoT. The real-time analysis of the extraction efficiency rate of zinc and lead concerning temperature, pH and a particle size parameter in mining sectors is carried out by storing data in the cloud. It is accomplished by using an integrated IoT module holding Revolution-Pi IIoT (IIoT) gateway with AC500 PLC to afford enhanced data communication from the mining field to the cloud server to increase the performance of the processes. Based on the retrieval of historical data from the cloud, a multivariate regression model for extraction efficiency of zinc and lead is formulated by using pH, temperature and particle size as influencing parameters to estimate the predictions. Keywords Extraction efficiency · IIoT · Data analysis · Prediction
1 Introduction Industrial automation includes the use of specified control technologies to provide automatic operation of manufacturing processes without significant human intervention to achieve superior performance than manual control. These automation devices include Programmable Logic Controllers, PCs, Programmable Automation Controllers, etc. and various industrial communication systems [1]. The prominent task-oriented embedded sensors are connected to the cloud through a variety of methods including cellular, satellite, Wi-Fi, Bluetooth, low-power wide-area networks (LPWAN), or connecting directly to the internet via EtherNet [2]. Based on the specific IoT applications, the connectivity options are considered specific to power * C. Maheswari [email protected] * E. B. Priyanka [email protected] 1
Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India
Department of Information Technology, Kongu Engineering College, Perundurai, India
2
consumption, range and bandwidth. Figure 1 shows the pictorial representation of IoT application working sequences in which data acquisition is done using sensor incorporated with antenna or microcontroller for serial or parallel data communication to the IoT hub or gateway. The collected data in the IoT gateway transfers received field data to the cloud in the form of digital bits with dedicated IP addresses. The data analysis
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