Characterization of alum floc in water treatment by image analysis and modeling
In many water treatment plants, flocculation is the key unit concerning the performance of water treatment. For this reason, monitoring the flocculation (i.e. floc size) is a crucial issue to achieve the acceptable performance for the process. Generally, flocculation is monitored by naked eye or usi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2014-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2014.944767 |
| _version_ | 1797764316602040320 |
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| author | Petri Juntunen Mika Liukkonen Markku Lehtola Yrjö Hiltunen |
| author_facet | Petri Juntunen Mika Liukkonen Markku Lehtola Yrjö Hiltunen |
| author_sort | Petri Juntunen |
| collection | DOAJ |
| description | In many water treatment plants, flocculation is the key unit concerning the performance of water treatment. For this reason, monitoring the flocculation (i.e. floc size) is a crucial issue to achieve the acceptable performance for the process. Generally, flocculation is monitored by naked eye or using complex, sample-based methods. This is laborious and expensive, however, and should be either automated or alternative methods for estimating the floc quality should be developed if possible. In this paper, we present an online characterization system for estimating the most essential quality parameters of floc using digital images taken in the flocculation unit. In addition, we compare the surface area of the floc particles defined using the images with other measurement data collected from the process, and create a multivariable regression model for it. We also illustrate the dependencies between the floc properties and other process variables using a self-organizing map. |
| first_indexed | 2024-03-12T19:54:41Z |
| format | Article |
| id | doaj.art-74438fe2a1dd472c8282ca7ea95c5f3a |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T19:54:41Z |
| publishDate | 2014-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-74438fe2a1dd472c8282ca7ea95c5f3a2023-08-02T02:53:48ZengTaylor & Francis GroupCogent Engineering2331-19162014-12-011110.1080/23311916.2014.944767944767Characterization of alum floc in water treatment by image analysis and modelingPetri Juntunen0Mika Liukkonen1Markku Lehtola2Yrjö Hiltunen3University of Eastern FinlandUniversity of Eastern FinlandUniversity of Eastern FinlandUniversity of Eastern FinlandIn many water treatment plants, flocculation is the key unit concerning the performance of water treatment. For this reason, monitoring the flocculation (i.e. floc size) is a crucial issue to achieve the acceptable performance for the process. Generally, flocculation is monitored by naked eye or using complex, sample-based methods. This is laborious and expensive, however, and should be either automated or alternative methods for estimating the floc quality should be developed if possible. In this paper, we present an online characterization system for estimating the most essential quality parameters of floc using digital images taken in the flocculation unit. In addition, we compare the surface area of the floc particles defined using the images with other measurement data collected from the process, and create a multivariable regression model for it. We also illustrate the dependencies between the floc properties and other process variables using a self-organizing map.http://dx.doi.org/10.1080/23311916.2014.944767water treatmentwater qualityimage analysisflocculationregressionself-organizing map |
| spellingShingle | Petri Juntunen Mika Liukkonen Markku Lehtola Yrjö Hiltunen Characterization of alum floc in water treatment by image analysis and modeling Cogent Engineering water treatment water quality image analysis flocculation regression self-organizing map |
| title | Characterization of alum floc in water treatment by image analysis and modeling |
| title_full | Characterization of alum floc in water treatment by image analysis and modeling |
| title_fullStr | Characterization of alum floc in water treatment by image analysis and modeling |
| title_full_unstemmed | Characterization of alum floc in water treatment by image analysis and modeling |
| title_short | Characterization of alum floc in water treatment by image analysis and modeling |
| title_sort | characterization of alum floc in water treatment by image analysis and modeling |
| topic | water treatment water quality image analysis flocculation regression self-organizing map |
| url | http://dx.doi.org/10.1080/23311916.2014.944767 |
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