Dataset of air velocity and temperature fields inside an insulated box equipped with phase change material under several operating conditions.

This article contains a description of protocol to measure air velocity field (by Particle Image Velocimetry - PIV) and temperature field (by T-type thermocouples) in an insulated box equipped with Phase Change Material (PCM) of melting point 0 °C. The influence of various conditions was studied: i) PCM position (at sidewall and at top), ii) aspect ratio of the box (height/width ∼ 1 and 1.7), iii) ambient temperature (10 °C, 20 °C and 30 °C), iv) test product initial temperature (4 °C and 10 °C) and vi) spacing beneath the load (0 mm and 20 mm). This article is related to a published research paper, it provides the dataset of all experiments which can be useful for experimenter to understand the phenomena and for expert in numerical model to validate the developed model e.g., by Computational Fluid Dynamic.

Dataset of experimental study investigation of airflow and heat transfer in an insulated box equipped with a phase change material.

This article contains a detailed description of the experimental protocol of air velocity (by particle image velocimetry - PIV) and temperature measurement (by T-type thermocouples) in an insulated box equipped with a Phase Change Material (PCM). The study was conducted in an empty box and a loaded box with extruded polystyrene slabs (XPS) and methylcellulose slabs (test product). The measurement was conducted at the middle plane and lateral plane. This article contains a complete dataset along with the illustrated figures of conducted experiment. They lead to more understanding of phenomena inside a closed cavity with a cold source and can be useful for validating numerical models, e.g., the results computed by computational fluid dynamic.

Combining Quantitative Risk Assessment of Human Health, Food Waste, and Energy Consumption: The Next Step in the Development of the Food Cold Chain?

The preservation of perishable food via refrigeration in the supply chain is essential to extend shelf life and provide consumers with safe food. However, electricity consumed in refrigeration processes has an economical and an environmental impact. This study focuses on the cold chain of cooked ham, including transport, cold room in supermarket, display cabinet, transport by consumer, and domestic refrigerator, and aims to predict the risk for human health associated with Listeria monocytogenes, the amount of food wasted due to the growth of spoilage bacteria, and the electrical consumption to maintain product temperature through the cold chain. A set of eight intervention actions were tested to evaluate their impact on the three criteria. Results show that the modification of the thermostat of the domestic refrigerator has a high impact on food safety and food waste and a limited impact on the electrical consumption. Inversely, the modification of the airflow rate in the display cabinet has a high impact on electrical consumption and a limited impact on food safety and food waste. A cost-benefit analysis approach and two multicriteria decision analysis methods were used to rank the intervention actions. These three methodologies show that setting the thermostat of the domestic refrigerator to 4 °C presents the best compromise between the three criteria. The impact of decisionmaker preferences (criteria weight) and limitations of these three approaches are discussed. The approaches proposed by this study may be useful in decision making to evaluate global impact of intervention actions in issues involving conflicting outputs.

Identification of the significant factors in food safety using global sensitivity analysis and the accept-and-reject algorithm: application to the cold chain of ham.

Deterministic models describing heat transfer and microbial growth in the cold chain are widely studied. However, it is difficult to apply them in practice because of several variable parameters in the logistic supply chain (e.g., ambient temperature varying due to season and product residence time in refrigeration equipment), the product's characteristics (e.g., pH and water activity) and the microbial characteristics (e.g., initial microbial load and lag time). This variability can lead to different bacterial growth rates in food products and has to be considered to properly predict the consumer's exposure and identify the key parameters of the cold chain. This study proposes a new approach that combines deterministic (heat transfer) and stochastic (Monte Carlo) modeling to account for the variability in the logistic supply chain and the product's characteristics. The model generates a realistic time-temperature product history , contrary to existing modeling whose describe time-temperature profile Contrary to existing approaches that use directly a time-temperature profile, the proposed model predicts product temperature evolution from the thermostat setting and the ambient temperature. The developed methodology was applied to the cold chain of cooked ham including, the display cabinet, transport by the consumer and the domestic refrigerator, to predict the evolution of state variables, such as the temperature and the growth of Listeria monocytogenes. The impacts of the input factors were calculated and ranked. It was found that the product's time-temperature history and the initial contamination level are the main causes of consumers' exposure. Then, a refined analysis was applied, revealing the importance of consumer behaviors on Listeria monocytogenes exposure.