DATA VALIDATION IN A CONTEXT OF DATA RECONCILIATION
CIM MineSpace 2001
Frédéric Flament, Michel Labranche,
The aim of this paper is to present the development of a data validation software as component of a mass balance package.
Data reconciliation through mass balance is basically an optimization problem constrained by the law of mass conservation. As any optimization problem solved with a modern and robust software application, erroneous information or starting point will always lead to a solution, whether it is realistic or not. Moreover, good information wrongly weighted by an incorrect error model will lead to the same bad ending. In an industrial context where competition requires accurate information, data reconciliation without validation can be disastrous and calls for data validation before and after reconciliation.
The challenge in automated data validation resides in the development of general theoretical solutions. Until now, none has been presented and only partial solutions are available. The development presented in this paper incorporates some of these partial solutions. The integration of human knowledge into the application paved the way to a more general solution. The combination “theoretical validation – human expertise” proved to be flexible and reliable enough to be integrated in a commercial data validation package. Numerous examples though industrial applications are presented. These applications demonstrate the validation of raw measurements, user support in the development of error models and validation of reconciled measurements.
Automatic Data Validation, Optimization, Mass balance, Data Validation, Data Reconciliation