Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle
This paper presents the construction and working principles of an intelligent fiber-optic intensity sensor used for examining the concentration of a mixture in conjunction with water. It can find applications e.g. in waste-water treatment plant for selection of a treatment process. The sensor head i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2007-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/7/3/384/ |
| _version_ | 1797999449634504704 |
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| author | Michal Borecki |
| author_facet | Michal Borecki |
| author_sort | Michal Borecki |
| collection | DOAJ |
| description | This paper presents the construction and working principles of an intelligent fiber-optic intensity sensor used for examining the concentration of a mixture in conjunction with water. It can find applications e.g. in waste-water treatment plant for selection of a treatment process. The sensor head is the end of a large core polymer optical fiber, which constitutes one arm of an asymmetrical coupler. The head works on the reflection intensity basis. The reflected signal level depends on the Fresnel reflection from the air and from the mixture examined when the head is immersed in it. The sensor head is mounted on a lift. For detection purposes the signal can be measured on head submerging, submersion, emerging and emergence. Therefore, the measured signal depends on the surface tension, viscosity, turbidity and refraction coefficient of the solution. The signal coming from the head is processed electrically in an opto-electronic interface. Then it is fed to a neural network. The novelty of the proposed sensor lies in that it contains an asymmetrical coupler and a neural network that works in the generalization mode. The sensor resolution depends on the efficiency of the asymmetrical coupler, the precision of the opto-electronic signal conversion and the learning accuracy of the neural network. Therefore, the number and quality of the points used for the learning process is very important. By way of example, the paper describes a sensor intended for examining the concentration of liquid soap in water. |
| first_indexed | 2024-04-11T11:04:48Z |
| format | Article |
| id | doaj.art-0f0a06454f2e4a72a0761bd8cac76f31 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:04:48Z |
| publishDate | 2007-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0f0a06454f2e4a72a0761bd8cac76f312022-12-22T04:28:24ZengMDPI AGSensors1424-82202007-03-017338439910.3390/s7030384Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working PrincipleMichal BoreckiThis paper presents the construction and working principles of an intelligent fiber-optic intensity sensor used for examining the concentration of a mixture in conjunction with water. It can find applications e.g. in waste-water treatment plant for selection of a treatment process. The sensor head is the end of a large core polymer optical fiber, which constitutes one arm of an asymmetrical coupler. The head works on the reflection intensity basis. The reflected signal level depends on the Fresnel reflection from the air and from the mixture examined when the head is immersed in it. The sensor head is mounted on a lift. For detection purposes the signal can be measured on head submerging, submersion, emerging and emergence. Therefore, the measured signal depends on the surface tension, viscosity, turbidity and refraction coefficient of the solution. The signal coming from the head is processed electrically in an opto-electronic interface. Then it is fed to a neural network. The novelty of the proposed sensor lies in that it contains an asymmetrical coupler and a neural network that works in the generalization mode. The sensor resolution depends on the efficiency of the asymmetrical coupler, the precision of the opto-electronic signal conversion and the learning accuracy of the neural network. Therefore, the number and quality of the points used for the learning process is very important. By way of example, the paper describes a sensor intended for examining the concentration of liquid soap in water.http://www.mdpi.com/1424-8220/7/3/384/opto-electronicfiber opticintensity sensor. |
| spellingShingle | Michal Borecki Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle Sensors opto-electronic fiber optic intensity sensor. |
| title | Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle |
| title_full | Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle |
| title_fullStr | Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle |
| title_full_unstemmed | Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle |
| title_short | Intelligent Fiber Optic Sensor for Estimating the Concentration of a Mixture-Design and Working Principle |
| title_sort | intelligent fiber optic sensor for estimating the concentration of a mixture design and working principle |
| topic | opto-electronic fiber optic intensity sensor. |
| url | http://www.mdpi.com/1424-8220/7/3/384/ |
| work_keys_str_mv | AT michalborecki intelligentfiberopticsensorforestimatingtheconcentrationofamixturedesignandworkingprinciple |