Nonlinear Filters for State Estimation of UV Flash Processes

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We describe four algorithms for state estimation of stochastic differential-algebraic equations. We consider the extended Kalman filter, the unscented Kalman filter, the particle filter, and the ensemble Kalman filter. The differential-algebraic equations that we consider are in a semi-explicit index-l form. Models of dynamic UV flash processes are in such a form. The UV flash is relevant to rigorous models of many chemical phase equilibrium processes because it is a mathematical representation of the second law of thermodynamics. State estimation is relevant to model predictive control, model identification, fault detection, monitoring, and prediction. State estimation of UV flash processes is therefore important to safe and economical operation of processes such as flash separation, distillation, multiphase flow in pipelines, and oil production. We compare the accuracy and efficiency of the four filters using a numerical example that involves a UV flash separation process. Furthermore, we demonstrate that the filters can be used as soft sensors that estimate the vapor-liquid composition of the separation process based on temperature and pressure measurements.
Original languageEnglish
Title of host publication 2018 IEEE Conference on Control Technology and Applications (CCTA)
Publication date2018
ISBN (Electronic)978-1-5386-7698-1
Publication statusPublished - 2018
Event2018 IEEE Conference on Control Technology and Applications - Scandic Hotel, Copenhagen, Denmark
Duration: 21 Aug 201824 Aug 2018


Conference2018 IEEE Conference on Control Technology and Applications
LocationScandic Hotel

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  • Mathematical model
  • Kalman filters
  • Stochastic processes
  • Covariance matrices
  • State estimation
  • Biological system modeling
  • Optimization


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