Trade-off between minimum number of wireless sensors and the accuracy of temperature profile in cold rooms: a model-based framework

Evaluation of temperature distribution in cold rooms is an important consideration in the design of food storage solutions. Two common approaches used in both industry and academia to address this question are the deployment of wireless sensors, and modelling with Computational Fluid Dynamics (CFD). However, for a realworld evaluation of temperature distribution in a cold room, both approaches have their limitations. For wireless sensors, it is economically unfeasible to carry out large-scale deployment (to obtain a high resolution of temperature distribution); while with CFD modelling, it is usually not accurate enough to get a reliable result. In this paper, we propose a model-based framework which combines the wireless sensors technique with CFD modelling technique together to achieve a satisfactory trade-off between minimum number of wireless sensors and the accuracy of temperature profile in cold rooms. A case study is presented to demonstrate the usability of the framework.

Model and visualise the relationship between energy consumption and temperature distribution in cold rooms

In the area of food and pharmacy cold storage, temperature distribution is considered as a key factor. Inappropriate distribution of temperature during the cooling process in cold rooms will cause the deterioration of the quality of products and therefore shorten their life-span. In practice, in order to maintain the distribution of temperature at an appropriate level, large amount of electrical energy has to be consumed to cool down the volume of space, based on the reading of a single temperature sensor placed in every cold room. However, it is not clear and visible that what is the change of energy consumption and temperature distribution over time. It lacks of effective tools to visualise such a phenomenon. In this poster, we initially present a solution which combines a visualisation tool with a Computational Fluid Dynamics (CFD) model together to enable users to explore such phenomenon.

Photocatalytic activities of tin(IV) oxide surface-modified titanium(IV) dioxide show a strong sensitivity to the TiO 2 crystal form

Surface modification of rutile TiO2 with extremely small SnO2 clusters gives rise to a great increase in its UV light activity for degradation of model organic water pollutants, while the effect is much smaller for anatase TiO2. This crystal form sensitivity is rationalized in terms of the difference in the electronic modification of TiO2 through the interfacial Sn−O−Ti bonds. The increase in the density of states near the conduction band minimum of rutile by hybridization with the SnO2 cluster levels intensifies the light absorption, but this is not seen with modified anatase. The electronic transition from the valence band to the conduction band causes the bulk-to-surface interfacial electron transfer to enhance charge separation. Further, electrons relaxed to the conduction minimum are smoothly transferred to O2 due to the action of the SnO2 species as an electron transfer promoter.