6533b834fe1ef96bd129e146
RESEARCH PRODUCT
Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
Anca Sipossubject
Observer (quantum physics)Computer science020209 energyGeography Planning and DevelopmentTJ807-83002 engineering and technology010501 environmental sciencesManagement Monitoring Policy and LawTD194-19501 natural sciencesRenewable energy sourcesExtended Kalman filterControl theory0202 electrical engineering electronic engineering information engineeringGE1-350state estimation0105 earth and related environmental sciencesEnvironmental effects of industries and plantsBasis (linear algebra)Renewable Energy Sustainability and the EnvironmentProcess (computing)Kalman filterFilter (signal processing)batch fermentation processExponential functionEnvironmental sciencessustainable control systemNorm (mathematics)description
Winemaking is concerned about sustainable energy availability that implies new methods for process monitoring and control. The aim of this paper is to realize a comparative analysis of the possibilities offered using estimation techniques, balances, and filtering techniques such as the Kalman filter (KF) and the extended Kalman filter (EKF), to obtain indirect information about the alcoholic fermentation process during winemaking. Thus, an estimation solution of the process variables in the exponential growing phase is proposed, using an extended observer. In addition, two estimation solutions of this process with the EKF and an estimation of the decay phase of the fermentation process are presented. The difference between the two EKF variants consisted of taking into consideration the indicator of the integral of the error norm square for the second EKF, for which the performance criterion was the statistical average of this indicator. Results from the simulation of the estimation programs of the two EKF variants were more than satisfactory. This research provides a basis for using an EKF designed for advanced control of the alcoholic fermentation batch process as a knowledge-based system.
year | journal | country | edition | language |
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2020-08-31 | Sustainability |