Robust control of a SAG mill O. Gala ´n * , G.W. Barton, J.A. Romagnoli ORICAA Laboratory of Process Systems Engineering, Department of Chemical Engineering, University of Sydney, Sydney, NSW 2006, Australia Abstract It is now well accepted that process automation, closed-loop control and optimisation technologies enable higher feed rates, more consistent product qualities, lower utility usage and higher yields in a diverse range of process industries. In the minerals processing industry, as a general rule, it is advisable to consider the grinding circuit first, as good operation here is necessary for stable operation in the rest of the plant. Operating conditions in a grinding circuit are frequently such that production is significantly reduced due to the effects of process disturbances, most notably those associated with the feed ore. This paper demonstrates the design of a robust control scheme for a semi- autogenous grinding (SAG) mill. Feedback (FB), feedback plus feedforward (FF) and reduced order (i.e. less complex) robust controllers were all developed and tested—each providing tight control over the SAG mill power draw by adjusting the feed rate to the mill. This example clearly shows that using the available Matlab-based controller design package, it is possible to develop robust (i.e. in terms of strict guarantees on both controller performance and stability, despite model uncertainties) control systems for the minerals industry as readily as for other industries where advanced control is more routinely used. D 2002 Published by Elsevier Science B.V. Keywords: Grinding; Modelling; Control; Optimisation 1. Introduction There are considerable economic incentives for utilising advanced control techniques in the mineral processing industry, typical drivers being increasing mine operating costs (leading to demands on concentrator managers to improve valuables recovery and/or plant throughput) and the need to meet ever tighter environmental constraints. To meet these demands, an efficient process control system in seen as one of the most cost-effective options available. However, to more fully exploit the capabilities of modern data acquisition and control equipment, the minerals indus- try needs to consider greater use of advanced (typically model based) control systems. The major problems associated with the modelling and control of semi-autogenous grinding (SAG) mills are the lack of on-line process information (such as particle size distributions and ore hardness, and the inherent complexity of the grinding process itself (which makes any mathematical modelling from first principles a real challenge). A validated dynamic model is generally the first step towards the successful implementation of any advanced control system, and fortunately a number of models have been proposed to predict the dynamic behaviour of grinding processes [1]. In this paper, a (conventional) population balance approach was used to develop the necessary dynamic model of the SAG mill in question. A typical grinding circuit consists of a SAG mill, a ball mill, a scats crusher, mill discharge screens, together with primary and secondary cyclones. This study is focused on the primary grinding circuit where the SAG mill is operating in closed-loop fashion, as shown in Fig. 1. The fresh ore feed (flowrate f 1 ) is mixed with two recycled streams (the scats f 4 and the primary cyclone underflow f 8 ), with the combined feed passing to the SAG mill f 2 . Water f 3 is added to the SAG mill, as needed, so that in the output f 11 , the solids density is held at a set value. The pulp exit flow is then passed to vibrating screens that direct almost all water and small particles to a hopper where the solids content is adjusted by water addition f 10 prior to being pumped to the cyclones. The cyclone overflow is sent to the flotation circuit, while in this circuit, the cyclone underflow is split into two—one stream being sent to the ball mill f 9 for additional grinding, while the other is recycled to the SAG mill f 8 . 2. Mathematical model of grinding circuit The modelling approach taken here involved describing the grinding process in terms of a population balance for each particle size of interest [2]. A total of 26 particle sizes 0032-5910/02/$ - see front matter D 2002 Published by Elsevier Science B.V. PII:S0032-5910(02)00021-9 * Corresponding author. Fax: +61-2-93-51-28-54. E-mail address: omarg@chem.eng.usyd.edu.au (O. Gala ´n). www.elsevier.com/locate/powtec Powder Technology 124 (2002) 264– 271