Abstract
In this paper, state estimation of integrated measurement systems in the presence of stochastic parametric uncertainties has been studied in the framework of the optimal filtering technique. Integrated measurement is common in chemical processes in which a small amount of material are gradually collected for a period of time from a few minutes to some hours. This material is transported to the laboratories for analysis and measurement. The goal is to estimate slow-rate and fast-rate instantaneous states out of this integrated measurement by using the Kalman filter while parametric uncertainties have been considered in the noise parameters. Also, convergence analysis of this filter has been studied. The effectiveness of this method has been evaluated through simulation on a distillation column model and implementation on a laboratory-scale pH neutralization pilot plant.
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