A MT-NT-MILP combined method for gross error detection and data reconciliation

Data reconciliation is an effective technique for providing accurate and consistent value for chemical process. However, the presence of gross errors can severely bias the reconciled results. In this paper, a MT-NT-MILP (MNM)combined method is developed for gross error detection and data reconciliation for industrial application. An improved MT-NT method is proposed in order to generate gross error candidates before data rectification. Candidates are used in the MILP objective function to improve the efficiency by reducing the number of binary variables. Simulation results show that the method is effective especially in a large-scale problem.