an algorithm for uncertain data reconciliation in process industry

This paper proposes an uncertain data reconciliation algorithm for Process Industry. First of all, the dynamic Event Dependency Graph is defined to abstract the problem. Taking into account the scale of the industry, a granularity partition algorithm relied on event detection is presented. In the following for the purpose of data prediction to improve the precision of the predicted value of the measured data, an improved Least Squares Support Vector Machine (LSSVM) model based on relative error is proposed. On the basis of the above, we present our data reconciliation algorithm by constructing a constraint model to achieve the goal of on-line/off-line data reconciliation. The practical industrial applications proved the efficiency and performance of the algorithm. © 2008 IEEE