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Data center excess heat recovery: A case study of apple drying
RISE Research Institutes of Sweden, Digitala system, Datavetenskap. Lulea University of Technology, Sweden.ORCID-id: 0000-0003-4293-6408
RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
RISE Research Institutes of Sweden, Digitala system, Datavetenskap.ORCID-id: 0000-0001-7381-9154
2020 (engelsk)Inngår i: ECOS 2020 - Proceedings of the 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020 Local Organizing Committee , 2020, s. 2165-2174Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden. 

sted, utgiver, år, opplag, sider
ECOS 2020 Local Organizing Committee , 2020. s. 2165-2174
Emneord [en]
Energy efficiency; Energy utilization; Fruits; Predictive control systems; Processed foods; Waste heat, Conventional drying; Exhaust airflow; Experiment set-up; Experimental set up; Food processing industry; Machine learning models; Model predictive controllers; Northern sweden, Drying
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Identifikatorer
URN: urn:nbn:se:ri:diva-67540Scopus ID: 2-s2.0-85095775160OAI: oai:DiVA.org:ri-67540DiVA, id: diva2:1805631
Konferanse
33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020. 29 June 2020through 3 July 2020
Merknad

This study was supported by Interreg Nord, under grant NYPS 20201839, Arctiq-DC.

Tilgjengelig fra: 2023-10-17 Laget: 2023-10-17 Sist oppdatert: 2023-10-17bibliografisk kontrollert

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Brännvall, RickardVesterlund, Mattias

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