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in Wiley Online Library (wileyonlinelibrary.com).Data-driven models are widely used in process industries for monitoring and control purposes. No matter what kind of models one chooses, model-plant mismatch always exists; it is, therefore, important to implement model update strategies using the latest observation information of the investigated process. In practice, multiple observation sources such as frequent but inaccurate or accurate but infrequent measurements coexist for a same quality variable. In this article, we show how the flexibility of the Bayesian approach can be exploited to account for multiple-source observations with different degrees of belief. A practical Bayesian fusion formulation with time-varying variances is proposed to deal with possible abnormal observations. A sequential Monte Carlo sampling based particle filter is used for simultaneously handling systematic and nonsystematic errors (i.e., bias and noise) in the presence of process constraints. The proposed method is illustrated through a simulation example and a data-driven soft sensor application in an oil sands froth treatment process.

This study presents the design of soft sensors for estimation of bitumen froth quality in an oil sands natural froth lubricated (NFL) transportation process. One of the most important quality indexes for bitumen froth is the water content. Due to the variation in oil sands composition and the nature of multi‐phase process conditions, existing hardware sensors are not reliable enough to provide on‐line accurate water content measurement. Laboratory analysis result is obtained off‐line with large sampling interval and irregular time delay. Therefore, it is not sufficient for real‐time monitoring and control. To overcome these limitations, Bayesian information synthesis approach is proposed to fuse all the existing information to produce more reliable and more accurate real‐time froth quality information. This technique has been applied in Syncrude Canada Extraction operations; both monitoring and control performance illustrate the promising perspectives of the proposed approach. © 2012 Canadian Society for Chemical Engineering

Particle filters have become an increasingly useful tool for recursive Bayesian state estimation, especially for nonlinear and non-Gaussian problems. Despite the large number of papers published on particle filters in recent years, one issue that has not been addressed to any significant degree is the robustness. This paper presents a deterministic approach that has emerged in the area of robust filtering, and incorporates it into particle filtering framework. In particular, an ellipsoidal set membership approach is used to define a feasible set for particle sampling that contains the true state of the system, and makes the particle filter robust against unknown but bounded uncertainties. Simulation results show that the proposed algorithm is more robust than the regular particle filter and its variants such as the extended Kalman particle filter.