Authors: Jose Co Muñoz and Karl Ezra Pilario
Sensor measurements in a process network inherently contain random and/or gross errors. Data are deemed unreliable for process optimization, monitoring, control, and safety. This paper describes a simultaneous data reconciliation (DR) and gross error detection (GED) strategy for adjusting sensor measurements to satisfy mass and energy balances. The problem is modeled as a mixed-integer nonlinearly constrained optimization problem, to be solved by a branch-and-bound technique. The concept of a search tree explains how the technique reduces the search space in optimization. The objective function in each node of the search tree is evaluated using a recent hybrid Nelder-Mead simplex and particle swarm optimization (NM-PSO) routine. The effectiveness of the method is tested for a highly nonlinear system with 5 measured variables and 3 unmeasured variables. The results for varying number of iterations and number of particles in the NM-PSO routine show that reconciled variables deviate from the true values only slightly.
Keywords: data reconciliation; gross error detection; branch-and-bound; hybrid Nelder-Mead particle swarm optimization