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Electrical energy prediction using a surface fitting model for an on-farm direct expansion bulk milk cooler (DXBMC) in South Africa

Electrical energy prediction using a surface fitting model for an on-farm direct expansion bulk... This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion bulk milk cooler (DXBMC). The study reveals that milk volume and the temperature gradient between the room and the final milk temperature can effectively be used for predicting the energy consumption within 95% confidence bounds.Design/methodology/approachA data acquisition system comprised a Landis and Gyr E650 power meter, TMC6-HE temperature sensors, and HOBO UX120-006M 4-channel analog data logger was designed and built for monitoring of the DXBMC. The room temperature where the DXBMC is housed was measured using a TMC6-HE temperature sensor, connected to a Hobo UX120-006M four-channel analog data logger which was configured to log at one-minute intervals. The electrical energy consumed by the DXBMC was measured using a Landis and Gyr E650 meter while the volume of milk was extracted from on the farm records.FindingsThe results showed that the developed model can predict the electrical energy consumption of the DXBMC within an acceptable accuracy since 80% of the variation in the electrical energy consumption by the DXBMC was explained by the mathematical model. Also, milk volume and the temperature gradient between the room and final milk temperature in the BMC are primary and secondary contributors, respectively, to electrical energy consumption by the DXBMC. Based on the system that has been monitored the findings reveal that the DXBMC was operating within the expected efficiency level as evidenced by the optimized electrical energy consumption (EEC) closely mirroring the modelled EEC with a determination coefficient of 0.95.Research limitations/implicationsOnly one system was monitored due to unavailability of funding to deploy several data acquisition systems across the country. The milk blending temperatures, effects of the insulation of the DXBMC, were not taken into account in this study.Practical implicationsThe developed model is simple to use, cost effective and can be applied in real-time on the dairy farm which will enable the farmer to quickly identify an increase in the cooling energy per unit of milk cooled.Social implicationsThe developed easy to use model can be used by dairy farmers on similar on-farm DXBMC; hence, they can devise ways to manage their energy consumption on the farm during the cooling of milk and foster some energy efficiency initiatives.Originality/valueThe implementation of the developed model can be useful to dairy farmers in South Africa. Through energy optimization, the maintenance of the DXBMC can be determined and scheduled accordingly. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Engineering Design and Technology Emerald Publishing

Electrical energy prediction using a surface fitting model for an on-farm direct expansion bulk milk cooler (DXBMC) in South Africa

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References (33)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1726-0531
DOI
10.1108/jedt-05-2020-0198
Publisher site
See Article on Publisher Site

Abstract

This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion bulk milk cooler (DXBMC). The study reveals that milk volume and the temperature gradient between the room and the final milk temperature can effectively be used for predicting the energy consumption within 95% confidence bounds.Design/methodology/approachA data acquisition system comprised a Landis and Gyr E650 power meter, TMC6-HE temperature sensors, and HOBO UX120-006M 4-channel analog data logger was designed and built for monitoring of the DXBMC. The room temperature where the DXBMC is housed was measured using a TMC6-HE temperature sensor, connected to a Hobo UX120-006M four-channel analog data logger which was configured to log at one-minute intervals. The electrical energy consumed by the DXBMC was measured using a Landis and Gyr E650 meter while the volume of milk was extracted from on the farm records.FindingsThe results showed that the developed model can predict the electrical energy consumption of the DXBMC within an acceptable accuracy since 80% of the variation in the electrical energy consumption by the DXBMC was explained by the mathematical model. Also, milk volume and the temperature gradient between the room and final milk temperature in the BMC are primary and secondary contributors, respectively, to electrical energy consumption by the DXBMC. Based on the system that has been monitored the findings reveal that the DXBMC was operating within the expected efficiency level as evidenced by the optimized electrical energy consumption (EEC) closely mirroring the modelled EEC with a determination coefficient of 0.95.Research limitations/implicationsOnly one system was monitored due to unavailability of funding to deploy several data acquisition systems across the country. The milk blending temperatures, effects of the insulation of the DXBMC, were not taken into account in this study.Practical implicationsThe developed model is simple to use, cost effective and can be applied in real-time on the dairy farm which will enable the farmer to quickly identify an increase in the cooling energy per unit of milk cooled.Social implicationsThe developed easy to use model can be used by dairy farmers on similar on-farm DXBMC; hence, they can devise ways to manage their energy consumption on the farm during the cooling of milk and foster some energy efficiency initiatives.Originality/valueThe implementation of the developed model can be useful to dairy farmers in South Africa. Through energy optimization, the maintenance of the DXBMC can be determined and scheduled accordingly.

Journal

Journal of Engineering Design and Technology – Emerald Publishing

Published: Jun 7, 2021

Keywords: Regression; Direct expansion bulk milk cooler; Milk cooling energy; ReliefF algorithm; Surface fitting model

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