کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
689116 | 889591 | 2013 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Development and industrial application of soft sensors with on-line Bayesian model updating strategy Development and industrial application of soft sensors with on-line Bayesian model updating strategy](/preview/png/689116.png)
This paper deals with the issues associated with the development of data-driven models as well as model update strategy for soft sensor applications. A practical yet effective solution is proposed. Key process variables that are difficult to measure are commonly encountered in practice due to limitations of measurement techniques. Even with appropriate instruments, some measurements are only available through off-line laboratory analysis with typical sampling intervals of several hours. Soft sensors are inferential models that can provide continuous on-line prediction of hidden variables; such models are capable of combining real-time measurements with off-line lab data. Due to the prevalence of plant-model mismatch, it is important to update the model using the latest reference data. In this paper, parameters of data-driven models are estimated using particle filters under the framework of expectation–maximization (EM) algorithms. A Bayesian methodology for model calibration strategy is formulated. The proposed framework for soft sensor development is applied to an industrial process to provide on-line prediction of a quality variable.
► Identification of nonlinear data-driven state space models.
► Practical Bayesian model updating strategy for soft sensor development.
► Industrial applications of the developed soft sensor technology.
Journal: Journal of Process Control - Volume 23, Issue 3, March 2013, Pages 317–325