Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation

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...

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Main Author: Anca Sipos
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/17/7105
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spelling doaj-f1ad1fad5e4748da9f01676df5a4dd092020-11-25T03:37:48ZengMDPI AGSustainability2071-10502020-08-01127105710510.3390/su12177105Sustainable Method Using Filtering Techniques for a Fermentation Process State EstimationAnca Sipos0Faculty of Food Industry and Environmental Protection, Lucian Blaga University of Sibiu, 550012 Sibiu, RomaniaWinemaking 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.https://www.mdpi.com/2071-1050/12/17/7105state estimationbatch fermentation processsustainable control system
collection DOAJ
language English
format Article
sources DOAJ
author Anca Sipos
spellingShingle Anca Sipos
Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
Sustainability
state estimation
batch fermentation process
sustainable control system
author_facet Anca Sipos
author_sort Anca Sipos
title Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
title_short Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
title_full Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
title_fullStr Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
title_full_unstemmed Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
title_sort sustainable method using filtering techniques for a fermentation process state estimation
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2020-08-01
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.
topic state estimation
batch fermentation process
sustainable control system
url https://www.mdpi.com/2071-1050/12/17/7105
work_keys_str_mv AT ancasipos sustainablemethodusingfilteringtechniquesforafermentationprocessstateestimation
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