Data reconciliation: a robust approach using contaminated distribution. Application in mineral processing
Résumé
On-line optimisation provides a means for maintaining a process around its optimum operating range. An important component of optimisation relies in data reconciliation which is used for obtaining consistent data. On a mathematical point of view, the formulation is generally based on the assumption that the measurement errors have Gaussian probability density function (pdf) with zero mean. Unfortunately, in the presence of gross errors, all of the adjustments are greatly affected by such biases and would not be considered as reliable indicators of the state of the process. This paper proposes a data reconciliation strategy that deals with the presence of such gross errors. Instead of constructing the objective function to be minimized on the basis of random errors only, the proposed method takes into account both contributions from random and gross errors using a so-called contaminated Gaussian distribution. It is shown that this approach introduces less bias in the estimation due to its natural property to reject gross errors. An academic application to flowrate and concentration data in mineral processing illustrates the efficiency of the proposed method.