Analysis of chromite processing plant data by first order autoregressive model

Many mineral processing data can be monitored by a time series model. This research presents results of analysis and simulations of a chromite processing plant data determined by time series model. The plant data obtained by shift to shift include feed grade, concentrate grade, tailing grade, Cr/Fe ratio in concentrate. All the chromite processing data were found stationary over time. The autocorrelation was high for feed grade and Cr/Fe ratio. Weaker autocorrelation was observed for concentrate grade and tailing grade. Autoregressive integrated moving average (ARIMA, 1,0,0) or first order autoregressive (AR, 1) model, was found to fit all data very well. The models obtained have been also shown to be used for the near future estimation of these data. The time constant which is an indicator of sampling frequency of the data sets were determined. It was found that sampling frequency was enough for concentrate and tailing grade and their original values can be used in process control charts for monitoring. On the other hand, the sampling frequency should be reduced for feeding grade and Cr/Fe ratio for the same aims hence ARIMA residual charts were more suitable to monitor their values.