LAUSR

This paper concentrates on Bayesian state estimation approach in the presence of slow-rate integrated measurements. In chemical process, some quality variables, in the sense of measuring, often have an important characteristic resulting from the time taken for samples collection. These kinds of measurements are obtained based on sample of materials that are collected in a period of time. So, the measurement indicates the average property of the measurand in the period of samples collection, which is called Slow-Rate inTegrated Measurement (SRTM). In this paper, our goal is to estimate the fast-rate instantaneous states using available SRTM. Bayesian estimation approach is reformulated to acquire this goal. First, new Bayesian formulation is provided which ends to some complex integral formula. Then, with using the idea of Monte Carlo sampling method a numerical solution is presented for this problem. The advantage of the proposed algorithm is that it can deal with integrated measurement problem in a broader range of models with nonlinearity and non-Gaussian noise. The effectiveness of the proposed methodology is verified and demonstrated through simulation and empirical experiment on a level-flow laboratory-scale plant.