Data reconciliation: a robust approach using contaminated distribution

Abstract

On-line optimisation provides a means for maintaining a process around its optimum operating plant. 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 normally pdf with zero mean. Unfortunately, in the presence of gross errors, all 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.