Practical issues in the application of the particle filter for estimation of chemical processes

Abstract

AbstractIn this work, we explore practical aspects of the application of particle filters to chemical processes, with special emphasis on polymerization processes. Particle filters are potentially better suited for estimation of these nonlinear processes than Kalman filter-based estimators, since they do not make the assumption that the process and measurement noise are Gaussian. We demonstrate the improved performance of particle filters over the extended Kalman filter and the unscented Kalman filter when the innovation sequences are non-Gaussian. Another potential advantage of the particle filter is that information on full distribution of the state is obtained, and not just the expectation of the state estimate. We provide results based on k-means clustering that indicate the best method of extracting a point estimate from the full state distribution. Finally, we show that in cases where there is high plant-model mismatch, e.g., when a reduced order model is used for estimation, the basic sequential importance resampling particle filter (SIR-PF) does not provide accurate estimates, while an unscented particle filter provides excellent estimates even under high structural plant-model mismatch.