Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River
This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Si...
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doaj-babbb3b1263c41899cd234efa6bb4a632020-11-25T01:38:20ZengMDPI AGApplied Sciences2076-34172019-05-0199188310.3390/app9091883app9091883Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted RiverPrzemysław Hawro0Tadeusz Kwater1Robert Pękala2Bogusław Twaróg3Institute of Technical Engineering, State University of Technology and Economics in Jaroslaw, Czarnieckiego Str. 16, 37-500 Jaroslaw, PolandInstitute of Technical Engineering, State University of Technology and Economics in Jaroslaw, Czarnieckiego Str. 16, 37-500 Jaroslaw, PolandInstitute of Technical Engineering, State University of Technology and Economics in Jaroslaw, Czarnieckiego Str. 16, 37-500 Jaroslaw, PolandDepartment of Computer Engineering, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Pigonia Str. 1, 35-959 Rzeszow, PolandThis paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Simulation studies of a biochemically polluted river, for which the water quality was represented by biochemical oxygen demand (BOD) indices and the dissolved oxygen (DO) deficit, were carried out. The algorithm concept uses only online measurements of the object, and adaptive changes in the gain coefficient are determined based on the adaptation error adopted for this purpose. Simulation results indicated the correct functioning of the soft sensor even for inaccurately identified parameters of the mathematical model and for unknown values and intensity of disturbances affecting the object. The quality of the signals monitored via a soft sensor implemented in this way was determined with the root-mean-squared error (RMSE) and mean percentage error (MPE) indicators and compared with the Kalman filter.https://www.mdpi.com/2076-3417/9/9/1883soft sensoradaptive algorithmnonlinear differential equationsmathematical modelmonitoring quality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Przemysław Hawro Tadeusz Kwater Robert Pękala Bogusław Twaróg |
spellingShingle |
Przemysław Hawro Tadeusz Kwater Robert Pękala Bogusław Twaróg Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River Applied Sciences soft sensor adaptive algorithm nonlinear differential equations mathematical model monitoring quality |
author_facet |
Przemysław Hawro Tadeusz Kwater Robert Pękala Bogusław Twaróg |
author_sort |
Przemysław Hawro |
title |
Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River |
title_short |
Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River |
title_full |
Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River |
title_fullStr |
Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River |
title_full_unstemmed |
Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River |
title_sort |
soft sensor with adaptive algorithm for filter gain correction in the online monitoring system of a polluted river |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-05-01 |
description |
This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Simulation studies of a biochemically polluted river, for which the water quality was represented by biochemical oxygen demand (BOD) indices and the dissolved oxygen (DO) deficit, were carried out. The algorithm concept uses only online measurements of the object, and adaptive changes in the gain coefficient are determined based on the adaptation error adopted for this purpose. Simulation results indicated the correct functioning of the soft sensor even for inaccurately identified parameters of the mathematical model and for unknown values and intensity of disturbances affecting the object. The quality of the signals monitored via a soft sensor implemented in this way was determined with the root-mean-squared error (RMSE) and mean percentage error (MPE) indicators and compared with the Kalman filter. |
topic |
soft sensor adaptive algorithm nonlinear differential equations mathematical model monitoring quality |
url |
https://www.mdpi.com/2076-3417/9/9/1883 |
work_keys_str_mv |
AT przemysławhawro softsensorwithadaptivealgorithmforfiltergaincorrectionintheonlinemonitoringsystemofapollutedriver AT tadeuszkwater softsensorwithadaptivealgorithmforfiltergaincorrectionintheonlinemonitoringsystemofapollutedriver AT robertpekala softsensorwithadaptivealgorithmforfiltergaincorrectionintheonlinemonitoringsystemofapollutedriver AT bogusławtwarog softsensorwithadaptivealgorithmforfiltergaincorrectionintheonlinemonitoringsystemofapollutedriver |
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1725054417313464320 |