Bayesian Fill Volume Estimation Based on Point Level Sensor Signals⁎This work is supported by the European Regional Development Fund grant ZW 6-85018381. We would like to thank our industrial partners Heinrich Meier Eisengießerei GmbH & Co. KG, IAV GmbH and KUNKEL WAGNER Germany GmbH for their support. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Zumsande, Johannes; Kortmann, Karl-Philipp; Wielitzka, Mark; Ortmaier, Tobias
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1261
• Page End: 1267
• Publication Date: 2020
• Subjects: Bayesian filter -- data fusion -- probabilistic models -- stochastic approximation -- estimation algorithms; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1852
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In dry bulk and fluid processing, the composites are usually stored in hoppers, tanks, or other containers. Due to the economic advantages, binary point level sensors, which detect fill level exceeding, are widely used for process monitoring and control. In this paper, we propose different filters for estimating the probability distribution of the fill volume based on a time-variant measurement distribution and a stochastic physical model with white process noise. A filter based on the model prediction with separated measurement update and two Bayesian particle filters are proposed and compared with a simulated ground truth. The performance measures are the root-mean-square error, the precision of the 95 % and 75 % credible intervals, and the average value of the estimated probability density function at the simulated fill volumes.