Data reconciliation: a robust approach using contaminated distribution. Application to a petrochemical process.

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. Application to total flowrate and concentration data in a petroleum network transportation is provided.