Fully automated coal quality control using digital twin material tracking and statistical model predictive control for yield optimization during production of semi soft coking- and power station coal

Abstract

The quality control of a two-stage coal washing process involves several complex components that need to be modelled accurately, to enable autonomous control of the process. The first objective is to develop a method to track the material through the washing process, while ensuring accurate washing prediction models are used. This was achieved through a digital twin model of the Grootegeluk 1 coal processing plant. The model is the amalgamation of manipulating and combining of data-sets from the plant historian, geological wash tables, and mining dispatch servers. This information is then used to control and set the processing medium densities of all 15 modules on the plant, 10 modules in the primary wash and 5 modules in the secondary wash. This controller has been successfully implemented and controlled the plant for 10 days.