Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance
Abstract Automation and reliability are the crucial elements of any advance reverse osmosis plant to meet the environmental and economic demands. Early fault indication, diagnosis and regular maintenance are the key challenges with most of the reverse osmosis plants in the Indian scenario. The prese...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-09-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s13201-018-0821-8 |
| _version_ | 1818491569824595968 |
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| author | Satyam Srivastava Saikrishna Vaddadi Pankaj Kumar Shashikant Sadistap |
| author_facet | Satyam Srivastava Saikrishna Vaddadi Pankaj Kumar Shashikant Sadistap |
| author_sort | Satyam Srivastava |
| collection | DOAJ |
| description | Abstract Automation and reliability are the crucial elements of any advance reverse osmosis plant to meet the environmental and economic demands. Early fault indication, diagnosis and regular maintenance are the key challenges with most of the reverse osmosis plants in the Indian scenario. The present work introduces a modern reverse osmosis (RO) plant status monitoring unit to monitor different plant parameters in real time and early prediction for faults and maintenance. Developed RO plant status monitoring unit consists of a touch screen-based embedded monitoring unit, water quality sensors (pH, TDS), sampling chamber for controlled water flow, flow sensors, pressure and level sensors. The present system has been developed in a modular fashion so that it could be integrated with any capacity of RO plant units. Developed embedded system monitors various parameters of the plant such as input power, efficiency of the plant, level of input and output water tank and also guides operator with instructions for plant operation. Other than this, a dedicated smartphone app interface has been developed for the operator to acquire data from status monitoring unit, storage on smartphone, and transfer it to the cloud. The developed smartphone-based app also provides facility to integrate plant data with Google map with location information for easy understanding and quick action. The system has also a backup facility to transfer data to the server using 2G GSM module during the unavailability of the operator. A dedicated centralized Web server has been developed for real-time visualization of all installed RO plant status monitoring units. Different machine learning techniques have been implemented on acquired sensors data to predict early warnings related to power failure, membrane fouling and scaling, input water shortage, pipe, tank leakage, water quality sensors damage, non-operation or wrong operation of the plant along with different maintenance actions such as membrane water and chemical wash. Developed RO status monitoring unit has been tested with various RO plants having capacity from 500 LPH to 2000 LPH and deployed at various nearby villages of Rajasthan. |
| first_indexed | 2024-12-10T17:32:46Z |
| format | Article |
| id | doaj.art-7b687f02edc34522a49ed76b377a3cdd |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-12-10T17:32:46Z |
| publishDate | 2018-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-7b687f02edc34522a49ed76b377a3cdd2022-12-22T01:39:37ZengSpringerOpenApplied Water Science2190-54872190-54952018-09-018611010.1007/s13201-018-0821-8Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenanceSatyam Srivastava0Saikrishna Vaddadi1Pankaj Kumar2Shashikant Sadistap3Academy of Scientific and Innovative Research (AcSiR), CSIR-CEERIAcademy of Scientific and Innovative Research (AcSiR), CSIR-CEERICSIR-CEERIAcademy of Scientific and Innovative Research (AcSiR), CSIR-CEERIAbstract Automation and reliability are the crucial elements of any advance reverse osmosis plant to meet the environmental and economic demands. Early fault indication, diagnosis and regular maintenance are the key challenges with most of the reverse osmosis plants in the Indian scenario. The present work introduces a modern reverse osmosis (RO) plant status monitoring unit to monitor different plant parameters in real time and early prediction for faults and maintenance. Developed RO plant status monitoring unit consists of a touch screen-based embedded monitoring unit, water quality sensors (pH, TDS), sampling chamber for controlled water flow, flow sensors, pressure and level sensors. The present system has been developed in a modular fashion so that it could be integrated with any capacity of RO plant units. Developed embedded system monitors various parameters of the plant such as input power, efficiency of the plant, level of input and output water tank and also guides operator with instructions for plant operation. Other than this, a dedicated smartphone app interface has been developed for the operator to acquire data from status monitoring unit, storage on smartphone, and transfer it to the cloud. The developed smartphone-based app also provides facility to integrate plant data with Google map with location information for easy understanding and quick action. The system has also a backup facility to transfer data to the server using 2G GSM module during the unavailability of the operator. A dedicated centralized Web server has been developed for real-time visualization of all installed RO plant status monitoring units. Different machine learning techniques have been implemented on acquired sensors data to predict early warnings related to power failure, membrane fouling and scaling, input water shortage, pipe, tank leakage, water quality sensors damage, non-operation or wrong operation of the plant along with different maintenance actions such as membrane water and chemical wash. Developed RO status monitoring unit has been tested with various RO plants having capacity from 500 LPH to 2000 LPH and deployed at various nearby villages of Rajasthan.http://link.springer.com/article/10.1007/s13201-018-0821-8Water quality sensorsReverse osmosisEmbedded systemEarly warningPredictive maintenance |
| spellingShingle | Satyam Srivastava Saikrishna Vaddadi Pankaj Kumar Shashikant Sadistap Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance Applied Water Science Water quality sensors Reverse osmosis Embedded system Early warning Predictive maintenance |
| title | Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance |
| title_full | Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance |
| title_fullStr | Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance |
| title_full_unstemmed | Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance |
| title_short | Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance |
| title_sort | design and development of reverse osmosis ro plant status monitoring system for early fault prediction and predictive maintenance |
| topic | Water quality sensors Reverse osmosis Embedded system Early warning Predictive maintenance |
| url | http://link.springer.com/article/10.1007/s13201-018-0821-8 |
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