Particle Filter for Bayesian State Estimation and Its Application to Soft Sensor Development.

摘要

For chemical engineering processes, state estimation plays a key role in various applications such as process monitoring, fault detection, process optimization and model based control. Thanks to their distinct advantages of inference mechanism, Bayesian state estimators have been extensively studied and utilized in many areas in the past several decades. However, Bayesian estimation algorithms are often hindered by severe process nonlinearities, complicated state constraints, systematic modeling errors, unmeasurable perturbations, and irregular with possibly abnormal measurements. This dissertation proposes novel methods for nonlinear Bayesian estimation in the presence of such practical problems, with a focus on sequential Monte Carlo sampling based particle filter (PF) approaches. Simulation studies and industrial applications demonstrate the efficacy of the developed methods.;In practical applications, nonlinear and non-Gaussian processes subject to state constraints are commonly encountered; however, most of the existing Bayesian methods do not take constraints into account. To address this inadequacy, a novel particle filter algorithm based on acceptance/rejection and optimization strategies is proposed. The proposed method retains the ability of PF in nonlinear and non-Gaussian state estimation, while taking advantage of optimization techniques in handling complicated constrained problems.;Dynamical systems subject to unknown but bounded perturbations appear in numerous applications. Considering that the performance of the conventional particle filter can be significantly degraded if there is a systematic modeling error or poor prior knowledge on the noise characteristics, this thesis proposes a robust PF approach, in which a deterministic nonlinear set membership filter is used to define a feasible set for particle sampling that guarantees to contain the true state of the system.;Furthermore, due to the imperfection of modeling and the nature of process uncertainty, it is important to calibrate process models in an adaptive way to achieve better state estimation performance. Motivated by a question of how to use the multiple observations of quality variables to update the model for better estimate, this thesis proposes a Bayesian information synthesis approach based on particle filter for utilizing multirate and multiple observations to calibrate data-driven model in a way that makes efficient use of the measured data while allowing robustness in the presence of possibly abnormal measurements.;In addition to the theoretical study, the particle filtering approach is implemented in developing Bayesian soft sensors for the estimation of froth quality in oil sands Extraction processes. The approach synthesizes all of the existing information to produce more reliable and more accurate estimation of unmeasurable quality variables. Application results show that particle filter requires relatively few assumptions with ease of implementation, and it is an appealing alternative for solving practical state estimation problems.