2019
DOI: 10.3390/app9091883
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Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River

Abstract: 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 conce… Show more

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Cited by 5 publications
(7 citation statements)
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References 19 publications
(33 reference statements)
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“…At temperature 20 Dissolved oxygen is one of the most important indicators of water quality: it influences the chemical and biochemical processes that occur in water. DO is an important indicator for the living organisms that make up a river ecosystem [24][25][26]. Aerobic processes occurring in natural waters are essential for reducing pollution in water.…”
Section: Water Quality Indicatorsmentioning
confidence: 99%
“…At temperature 20 Dissolved oxygen is one of the most important indicators of water quality: it influences the chemical and biochemical processes that occur in water. DO is an important indicator for the living organisms that make up a river ecosystem [24][25][26]. Aerobic processes occurring in natural waters are essential for reducing pollution in water.…”
Section: Water Quality Indicatorsmentioning
confidence: 99%
“…The application of virtual (or soft) sensing for surface and groundwater quality assessment is emerging. This is evidenced by the low number of publications (16) and the fact that 63% of the articles [22,29,55,85,[94][95][96][97][98][99] were published in the last three years (2019-2021). Therefore, as seen in Figure 4, it is not surprising that artificial neural network (ANN) related algorithms are the most applied ML techniques.…”
Section: Commonly Used Modeling Approachesmentioning
confidence: 99%
“…Consideration of seasonal effects in WQ assessment is vital since WQ variables are often subjected to high variation in concentration based on the time of the year. Based on the papers reviewed, it is observed that four studies [22,95,97,100] did not report on the data collection interval used in building their predictive models. This points to the need for a more rigorous scientific reporting of data collection in data-driven modeling papers.…”
Section: Data Collection Time Scalementioning
confidence: 99%
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“…For the quantitative effect of noise on the extracted parameters, we can perform some numerical analysis using the mean percentage error (MPE) (Hawro et al, 2019 Here, n is the number of frequency points, j is the frequency point, and j S and j R are the values synthesized and retrieved at the frequency point j , respectively. Accordingly, Table 1 shows the MPE values among the synthesized and retrieved y  values of a sample when there is no noise (Fig.…”
Section:  mentioning
confidence: 99%