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ROCOCO2HP

Financiado

Cerrado

Real time optimal control of the CO2 heat pump system for residential use

The aim of this project is to develop efficient real-time optimal control (RTOC) for the carbon dioxide (CO2) heat pump as a part of a building energy supply system and validate its reliability experimentally. This is necessary to... ver más

31/05/2022

SINTEF

202K€

Presupuesto del proyecto: 202K€

Líder del proyecto

SINTEF AS No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-05-31

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

SINTEF

202.16K€ | Lider

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Duración del proyecto: 27 meses Fecha Inicio: 2020-02-25
Fecha Fin: 2022-05-31

Líder del proyecto

SINTEF AS No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 202K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The aim of this project is to develop efficient real-time optimal control (RTOC) for the carbon dioxide (CO2) heat pump as a part of a building energy supply system and validate its reliability experimentally. This is necessary to increase the system efficiency. For a high system efficiency with CO2 heat pumps for heating purpose, a low water return temperature from building heating systems is crucially important. However, this is still difficult to achieve due to well-established heating solutions and control strategies that are not suitable for CO2 heat pumps. CO2 is considered as one natural refrigerant, which has the merit of nonflammability, non-toxicity, and low price when compared with traditional refrigerants. Current well-functioning control methods are developed for heat pumps based on HFCs. Current RTOCs have the disadvantage of large computational load, which makes them difficult to operate with real building energy systems. Furthermore, experimental validations for the developed RTOC cannot be conducted due to lack of advanced experimental conditions. A reliable and experimental-validated RTOC for CO2 heat pump systems is urgently needed. This project will be developed by combining my scientific expertise on RTOCs with advanced experimental conditions with the CO2 heat pump for residential heating use at the host laboratory. Model-based predictive control (MPC) will be used to develop the RTOC. Machine learning methods will be used to develop the non-linear system model. Further, the RTOC with multiplexed optimization strategy (MOS) will be implemented in simulation environment. After the reliability of the developed RTOC in simulation environment is validated, the RTOC will be tested in experimental conditions. Finally, the reliability of the developed RTOC will be validated in experimental conditions. This project will bring new knowledge and theories to develop the RTOC for CO2 heat pump systems and will help me become an independent researcher.

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