Bayesian network approach to process data reconciliation with state uncertainties and recycle streams

Abstract

Data reconciliation is a widely utilised technique in process industries to obtain consistent estimates of the process variables from measurements corrupted with random error and gross error, taking process models as constraint. In the existing formulations for data reconciliation, process models are assumed to be error free. However, in practice, process models can suffer from model inaccuracies, leading to uncertainties in states. This paper introduces a new method for data reconciliation developed in the framework of Bayesian network, accounting for the state uncertainties. The solution is obtained by utilising a Bayesian network model translated from the process model and using statistical inference techniques to estimate the reconciled values of the states. A novel method to construct acyclic Bayesian network for process networks with recycle streams is proposed. This method is also extended for data reconciliation of partially measured systems. The proposed data reconciliation schemes is demonstrated on two case studies.