Multivariate modelling and on-line data presentation for process monitoring at LKAB

Abstract

A very common problem in mining industry today is to obtain frequent and reliable on-line measurements of process variables, i.e information that to some extent express the quality of the material in the different process streams. In paradox to this modern control systems deliver raw data, often several hundred every minute, which are the base for the operator to make decisions. As a concequense of this the operator is missing some important real-time information and at the same time get overloaded in other. This makes it impossible for the operator to run the plant at an optimal point, and in the economic cut and thrust of today’s mineral industry the necessity to be competitive has never been greater.In this paper we show how PLS (partial least square regression) with advantage can be used to model process data. We also show how these simple models are possible to use for monitoring actual process status and to give the operator guidance how to manipulate the process variables in order to move the process to better conditions. Two applications are presented, a sorting process where the model prediction are used in an ordinary control loop and an induration process where we observe the methods ability for a new way of process monitoring.