The energy efficiency of ‘Conference’ pear storage was assessed for different storage strategies, including dynamic controlled atmosphere (DCA) at different temperatures and controlled atmosphere (CA) at varying temperatures and O2 levels. Storage at -1 °C in 3 kPa O2 and 0.7 kPa CO2 was used as a benchmark. Direct respiration measurements during the storage period showed that DCA reduced respiratory heat by 30–40 % compared with the benchmark, even at slightly elevated temperatures. A simulation-based energy assessment revealed that DCA could reduce the total heat load in a storage room by 8–16 %. Fan operation was found to account for the largest share of the total heat load (up to 50 %), while the respiratory heat contributed around 10–30 %. Among all experimental strategies, DCA at -1 °C reduced the total heat load by ∼8 %, and maintained good firmness and skin colour without inducing internal browning after long-term storage. This makes it the most optimal approach to balance fruit quality and energy savings.
@article{PHAN2026447,title={Improving energy efficiency in pear storage through dynamic controlled atmosphere (DCA)},journal={International Journal of Refrigeration},volume={182},pages={447-455},year={2026},issn={0140-7007},doi={https://doi.org/10.1016/j.ijrefrig.2025.12.015},url={https://www.sciencedirect.com/science/article/pii/S0140700725004761},author={Phan, Hoang Minh and Verlinden, Bert E. and Hertog, Maarten L.A.T.M. and Verboven, Pieter and Nicolai, Bart M.},keywords={Energy efficiency, Pear, Storage, Dynamic controlled atmosphere, Postharvest, Supply Chain}}
2024
Science outreach
Digitalisering van ethyleenproductie - de polsslag van kwaliteitsdynamiek Digitalization of ethylene production - the pulse of quality dynamics
Hoang Minh Phan
In Annual report of the Flanders Center of Postharvest Technology (VCBT), 2024
A particular challenge for food quality inspection remains the quantification of internal defects. This work addresses the challenge of internal defects segmentation in pear fruit (cv. Conference) based on high-throughput inline X-ray radiography images in combination with deep learning. Unlike previous approaches, which focused on healthy vs defect classification, the current segmentation approach is able to determine the type of defect, its dimensions, and location. To this end, a novel simulation method was designed to obtain input-target image pairs of radiography data, and more diverse defect pears were generated using a conditioned generator model. The obtained data contributed to the design of a segmentation model that labeled every pixel in the X-ray radiographs of pear fruit as ‘external air’, ‘healthy tissue’, ‘core’, ‘browning’, or ‘cavity’. We demonstrated that additional synthetic data in the learning process drastically improved the model performance. For instance, the mean IoU increased from 0.781 ± 0.112 to 0.883 ± 0.088 for consumable pears with minor cavities and browning. In terms of utility, our segmentation maps provide more detailed information about the type, size, and location of the disorders compared to the heatmaps produced by existing benchmark classifiers.
@article{TEMPELAERE2023108142,title={Non-destructive internal disorder segmentation in pear fruit by X-ray radiography and AI},journal={Computers and Electronics in Agriculture},volume={212},pages={108142},year={2023},issn={0168-1699},doi={https://doi.org/10.1016/j.compag.2023.108142},url={https://www.sciencedirect.com/science/article/pii/S0168169923005306},author={Tempelaere, Astrid and {Phan}, Hoang Minh and {Van De Looverbosch}, Tim and Verboven, Pieter and Nicolai, Bart}}
Conference paper
Building blocks for a digital twin of large cool store complexes.
Hoang Minh Phan, Hans Van Cauteren, Maarten Hertog, and 3 more authors
In Proceedings of the 26th IIR International Congress of Refrigeration, 2023
Digital twins are virtual representations that serve as real-time digital counterparts of physical objects spanning their full lifecycle. In the context of postharvest horticulture, these represent infrastructure (storage plants including refrigeration and controlled atmosphere systems) and the produce being handled (fruit and vegetables). The aim of the digital twin is to be used in real-time to support decision-making throughout the logistic handling chain, considering targets for, among others, product quality, food losses, energy use, emissions, and costs. Working with biological produce involves accounting for the inevitable biological variance, but also uncertainty exists in terms of unforeseen temporal and spatial variations within cold rooms. This contribution will present a review of the state of the art of models that can be used for the digital twin for large cool store complexes, including fully resolved as well as reduced order models of heat and mass transfer, as well as kinetics models while addressing issues with uncertainty propagation at both the theoretical and applied levels. Challenges and solutions for sensor-digital twin integration are discussed.
@inproceedings{phan2023digitaltwin,title={Building blocks for a digital twin of large cool store complexes.},author={Phan, Hoang Minh and Van Cauteren, Hans and Hertog, Maarten and Verlinden, Bert and Verboven, Pieter and Nicolai, Bart},booktitle={Proceedings of the 26th IIR International Congress of Refrigeration},year={2023},address={Paris, France},publisher={International Institute of Refrigeration},doi={10.18462/iir.icr.2023.0952},url={https://iifiir.org/en/fridoc/building-blocks-for-a-digital-twin-of-large-cool-store-complexes-147426},}
Science outreach
Digitale tweeling voor bewaarinfrastructuur Digital twin for storage infrastructure
Hoang Minh Phan
In Annual report of the Flanders Center of Postharvest Technology (VCBT), 2023