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The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC).The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.

In this paper the control performance of the flotation process is evaluated as a function of the measurement accuracy and sampling frequency of an on-stream analyzer. First, the performance of rule-based control and model predictive control (MPC) strategies is studied using discrete flotation models and the respective performance indices. Next, the net smelter return (NSR) is calculated for varying sampling rate and accuracy combinations using the PI controllers-based control strategy, mechanistic flotation models and the industrial process data as input. The control and economical performance of the process declines strongly when the sampling cycle is increased. The results also indicate that the speed of on-line analysis has a significant effect on the production economics, calculated as the average net smelter return.

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