Sequential and nonsequential process data coaptation

Two process coaptation approaches, sequential and nonsequential, for data adjustment and estimation were investigated for a microcomputer implementation for an indirect liquefaction process. The steady-state Kalman filter, a sequential estimator, was found to be suitable for on-line process flow data estimation. The technique also serves as a means to carry out integration of the process data. The integrated data, free of systematic errors, could be used by the nonsequential technique. A reduced balance approach, using optimization routines, was needed for the nonsequential data coaptation problem. Adjustment of the composition variables and calculation of the process model parameters that satisfy material balance constraints are the principle benefits of the optimization approach. Several gross measurement error detection schemes, reported in the literature, were also tested for their suitability to the two techniques.