Summary
This project seeks to revolutionise fault detection, diagnosis, and evaluation (FDDE) by designing an innovative, knowledge-based and cost-effective method.
Currently, FDDE for VCCS is performed only for large plants. For small plants, instead, only FDD is run, while the Evaluation is not affordable, according to current patents, allowing eventually only one fault per time. The innovation beyond the SoA of our proposal lies in the method's ability not only to detect but also to precisely identify their types and to assess the severity of up to three concurrent SOFT-faults (complete FDDE), in terms of refrigerant charge and heat exchangers fouling, which are the most insidious among the frequent ones. Through data processing the method allows to collect energetic and environmental knowledge of the system, and to elaborate economic calculations, making possible to provide a powerful and innovative decision-support service of interest for: energy service providers, final users or policy makers. Beyond the project, the method could be easily implemented in small-size vapor compression systems (VCCS, the most common kind of system) preserving the original hardware except for some cheap extra measurements and data processing, that can be easily done and managed with IOT devices and ICT infrastructures.
The research proposed will investigate a method through the proof of concept about the possibility to determine univocally the FDDE running an appropriate set of values to the degrees of freedom of the machine. The research requires the development and experimental validation of a model; the production of a huge synthetic database for several machines; the demonstration of the method by a virtual test and the effective experimental validation.
The project is relevant to the Challenge scope, aligning with the objectives of exploring new methods and processes to reduce operational costs, increase efficiency, and enhance environmental performance in cooling systems.
Currently, FDDE for VCCS is performed only for large plants. For small plants, instead, only FDD is run, while the Evaluation is not affordable, according to current patents, allowing eventually only one fault per time. The innovation beyond the SoA of our proposal lies in the method's ability not only to detect but also to precisely identify their types and to assess the severity of up to three concurrent SOFT-faults (complete FDDE), in terms of refrigerant charge and heat exchangers fouling, which are the most insidious among the frequent ones. Through data processing the method allows to collect energetic and environmental knowledge of the system, and to elaborate economic calculations, making possible to provide a powerful and innovative decision-support service of interest for: energy service providers, final users or policy makers. Beyond the project, the method could be easily implemented in small-size vapor compression systems (VCCS, the most common kind of system) preserving the original hardware except for some cheap extra measurements and data processing, that can be easily done and managed with IOT devices and ICT infrastructures.
The research proposed will investigate a method through the proof of concept about the possibility to determine univocally the FDDE running an appropriate set of values to the degrees of freedom of the machine. The research requires the development and experimental validation of a model; the production of a huge synthetic database for several machines; the demonstration of the method by a virtual test and the effective experimental validation.
The project is relevant to the Challenge scope, aligning with the objectives of exploring new methods and processes to reduce operational costs, increase efficiency, and enhance environmental performance in cooling systems.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101162436 |
| Start date: | 01-09-2024 |
| End date: | 31-08-2028 |
| Total budget - Public funding: | 1 181 750,00 Euro - 1 181 750,00 Euro |
Cordis data
Original description
This project seeks to revolutionise fault detection, diagnosis, and evaluation (FDDE) by designing an innovative, knowledge-based and cost-effective method.Currently, FDDE for VCCS is performed only for large plants. For small plants, instead, only FDD is run, while the Evaluation is not affordable, according to current patents, allowing eventually only one fault per time. The innovation beyond the SoA of our proposal lies in the method's ability not only to detect but also to precisely identify their types and to assess the severity of up to three concurrent SOFT-faults (complete FDDE), in terms of refrigerant charge and heat exchangers fouling, which are the most insidious among the frequent ones. Through data processing the method allows to collect energetic and environmental knowledge of the system, and to elaborate economic calculations, making possible to provide a powerful and innovative decision-support service of interest for: energy service providers, final users or policy makers. Beyond the project, the method could be easily implemented in small-size vapor compression systems (VCCS, the most common kind of system) preserving the original hardware except for some cheap extra measurements and data processing, that can be easily done and managed with IOT devices and ICT infrastructures.
The research proposed will investigate a method through the proof of concept about the possibility to determine univocally the FDDE running an appropriate set of values to the degrees of freedom of the machine. The research requires the development and experimental validation of a model; the production of a huge synthetic database for several machines; the demonstration of the method by a virtual test and the effective experimental validation.
The project is relevant to the Challenge scope, aligning with the objectives of exploring new methods and processes to reduce operational costs, increase efficiency, and enhance environmental performance in cooling systems.
Status
SIGNEDCall topic
HORIZON-EIC-2023-PATHFINDERCHALLENGES-01-01Update Date
19-11-2024
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