A comparative experimental study of five multivariable control strategies applied to a grinding plant

Abstract

This paper deals with the implementation of five multivariable adaptive, as well as classical, control strategies in an industrial grinding plant. The extended horizon, pole-placement, model reference, direct Nyquist Array and sequential loop closing algorithms were studied and implemented at CODELCO-Andina's copper grinding plant, with each of them delivering good performance. The 2×2 system chosen to be controlled has the percentage of solids (percentage of +65 mesh) fed to the hydrocyclones and the level of the sump as output variables, and the water flow added to the sump and the pump speed as input variables. The adaptive extended horizon algorithm performs the best, although all five strategies considerably improve the actual operation of the plant which consists of only one control loop. After a comparison amongst the control strategies, a brief economic impact analysis is performed to support the claim that multivariable control algorithms substantially improve the operation of the grinding plant, maintaining the percentage of solids (percentage over mesh 65) around a pre-specified value (optimal for practical purposes); thus obtaining interesting economic benefits.