The Phase Space as a New Representation of the Dynamical Behaviour of Temperature and Enthalpy in a Reefer monitored with a Multidistributed Sensors Network

Abstract

The study of temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport, as well as for minimizing losses. The objective of this work is to develop a new methodology of data analysis based on phase space graphs of temperature and enthalpy, collected by means of multidistributed, low cost and autonomous wireless sensors and loggers. A transoceanic refrigerated transport of lemons in a reefer container ship from Montevideo (Uruguay) to Cartagena (Spain) was monitored with a network of 39 semi-passive TurboTag RFID loggers and 13 i-button loggers. Transport included intermodal transit from transoceanic to short shipping vessels and a truck trip. Data analysis is carried out using qualitative phase diagrams computed on the basis of Takens which characterizes the cyclic behaviour of temperature. Areas within the enthalpy phase diagram computed for the short sea shipping transport were 5 times higher than those computed for the long sea shipping, with coefficients of variation above 100 % for both periods. This new methodology for data analysis highlights the significant heterogeneity of thermohygrometric conditions at different locations in the container.

Exploring ambient loading of citrus fruit into reefer containers for cooling during marine transport using computational fluid dynamics

“…This is rather surprising as huge amounts of perishable foods are transported overseas, where refrigerated containers have almost fully replaced bulk reefer vessels in the past decade (Arduino et al, 2013;Fitzgerald et al, 2011). Most container cooling studies relied on basic calculations or experiments Hoang et al, 2012;Jedermann et al, 2013;Jiménez-Ariza et al, 2014;Rodríguez-Bermejo et al, 2007;Smale et al, 2006;Tanner and Amos, 2003) and, to the best of our knowledge, only very few dealt with ambient loading Jedermann et al, 2014Jedermann et al, , 2013. In particular, fullscale container experiments are expensive and time-consuming and only allow for a rather limited number of Defraeye T., Cronjé P., Verboven P., Opara U.L., , Exploring ambient loading of citrus fruit into reefer containers for cooling during marine transport using computational fluid dynamics, Postharvest Biology and Technology 108,[91][92][93][94][95][96][97][98][99][100][101] http://dx.doi.org/10.1016/j.postharvbio.2015.06.004 5 measuring points (~ 10 -100 sensors), compared to the amount of fruit in the container (~ 10 5 individual fruit), which inhibits an in-depth parametric evaluation of the factors influencing the cooling process.…”

Advanced Characterisation of a Coffee Fermenting Tank by Multi-distributed Wireless Sensors: Spatial Interpolation and Phase Space Graphs

“…Such attractors correspond to identified regions in the vat and are characterised by their shape and location in the phase graph. Pattern recognition based on phase graphs was also used in other research areas to identify different behaviours in time series (Huang et al 2009;Jiménez-Ariza et al 2013).…”

“…temperature) to act as multi-distributed miniaturised loggers (Abad et al 2007;Vergara et al 2007). As an example of thermal monitoring, wireless intelligent sensors have been used inside refrigerated vehicles during international transport (Jiménez-Ariza et al 2013;Lang et al 2011), demonstrating that they can be used for the prediction of the emergence of warm spots (Jedermann et al 2013). Only a few studies, such as those carried out by Avallone et al (2001), Jackels and Jackels (2005) and Peñuela-Martínez et al (2010), are related to the control and supervision of coffee fermentation.…”

Phase Space Analysis of Pig Ear Skin Temperature during Air and Road Transport

“…The same analysis carried out in the road transport stages caused an average increase in the area from 49.5% when going from considering 75% to 100% of the points (Figure 9b), showing that both the event number and its effect on the thermal variability was much higher during air transport compared with road transport. Previous work by other authors has shown that the visualization using phase spaces allows easy identification of events such as the opening of a door in cold rooms or when refrigeration equipment is turned on or off [27]. The event detected in Stage1-Air, with an abrupt decrease in temperature that affected all the animals, could be explained by a local change in parameters of air-conditioning.…”

“…From these phase space diagrams, two variables of interest were extracted: (1) the centroid or center of gravity ( • C) of the cloud of points that represent the arithmetic average of all ESTs registered, weighted by the local density of points or specific weight; and (2) the areas ( • C 2 ) that include all the points in the phase space as a measure of the total variability in EST during the selected periods, i.e., total area for the whole journey as well as for each stage. Since [27] showed how a phase space of time series can be useful to point out intense but short events, we calculated the area described by 100% of the points, as well as 75% of the points around the centroid. That helped us to identify any unique events in 25% of points furthest away from the centroid.…”