Multiple regression analysis for the prediction of extraction efficiency in mining industry with industrial IoT

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.