Using a small cooling differential around the setpoint temperature allows to spare energy and get better postharest quality

The paper was published 2018, and the results are still useful for the management of cool chambers? After harvest, pomefruit are often stored at low temperature?in combination with controlled atmosphere (CA) conditions?(reduced O2 and increased CO2 concentrations) to extend their?storage life whilst preserving a high quality and reducing postharvest losses (Thompson et al., 2008) (*). The respiration rate of?pomefruit and, their process of senescence and deterioration?are tightly linked to temperature (Prusky, 2011). It is,?therefore, essential to store the produce within a narrow range of low temperatures during the total length of the storage period which can last up to ten months after harvest, depending on the cultivar and CA conditions (ASHRAE, 2010). For apples, the optimal storage temperature is between 0 ?C and 3 ?C (Rees et al., 2012). In combination with 1-MCP, a gaseous suppressor of ethylene action and fruit ripening, it is possible to store apples at even higher temperatures without a noticeable effect on the quality (McCormick et al., 2010). Gwanpua et al. (2013) developed and validated a stochastic kinetic model of the firmness breakdown of apples as a function of CA conditions, storage time and temperature. This model was used to assess the effect of three different storage temperatures (1 ?C, 2 ?C, and 3 ?C) during long-term storage on apple firmness and subsequently on the consumer acceptance. From the simulations it appeared that the final firmness of apples after four months of storage at 1 ?C or 3 ?C was comparable and not below the minimum threshold for export. This seemed to provide a clear benefit for a flexible cold chain management to reduce energy costs by storing fruit at higher temperatures. The fruit tolerance to temperature fluctuations during storage, however, has rarely been considered. East et al. (2013) devised an experiment to analyse quality changes by changing temperature setpoints from 0.5 to 4.0 ?C with increasing fluctuations up to 2.0 ?C during CA storage of apples in experimental containers. Differences in quality were also found negligible for different apple cultivars after several months of storage. The effect of differences in fluctuations around the optimal temperature setpoint was not considered. Growers, cooperatives and wholesalers are looking for alternative cooling strategies to reduce the high refrigeration costs which can contribute up to 60% to the total variable costs (Kart and Demircan, 2014). By using a lumped-model analysis, East et al. (2013) showed that avoiding cooling during peak price hours and concentrating cooling during off-peak hours may result in a 40% cost reduction, but does not save on actual energy use while allowing more temperature fluctuations. By?reducing the working time of the fans with an on/off cycling regime, Ambaw et al. (2016) found that there was a significant decrease in heat load, showing promising possibilities for energy reduction compared to continuous cooling without compromising the product temperatures. However, the impact of the temperature fluctuations on the quality of the apples was not investigated. Dedicated temperature management strategies should prevent chilling injury and maintain quality. In general, a temperature differential range around the optimal storage temperature (i.e., the setpoint temperature of the coolstore) is allowed but the permissible limits around the optimal storage temperature are not yet known. The value of the differential may also affect energy consumption, by changing the number and duration of cooling actions and intermediate non-cooling periods. Yet, one should also consider the effects on temperature and product quality uniformity in the storage room. Developing numerical models based on computational fluid dynamics (CFD) is a cost-effective alternative to study cooling processes both on a small and large scale (Ambaw et al., 2013a; Defraeye et al., 2015). Furthermore, by solving the different transport equations of a fluid (conservation of energy, mass and momentum) with the help of numerical techniques, it is possible to study the possible interactions of a fluid and its environment in a high spatial as well as temporal resolution (Blazek, 2015; Versteeg and Malalasekera, 2007). With CFD, one has the potential to gain a more fundamental insight into the cooling process and obtain all the relevant information that are otherwise very difficult to obtain experimentally in a fast manner. The objective of this work was to study the effect of changing the temperature differential range for cooled storage of apples (Malus ? domestica Borkh, cv. ?Jonagold?) at an optimal temperature of 0.95 ?C within a commercial CA coolstore. Different scenarios were evaluated with a validated porous medium CFD model. To properly take into account the heat transfer dynamics, the CFD model was extended with an evaporator and controller model to implement the differential temperature strategies as well as extended with a quality change kinetics model. The spatiotemporal temperature distributions at various positions within the fruit bin stack, and the corresponding evolution of the product quality were computed together with the energy use during long-term storage in each storage scenario. ConclusionsThis study evaluated an adaptive on-off cooling strategy based on modelling different temperature differentials. Increasing the?differential around a setpoint temperature causes larger product time-temperature fluctuations, and consumes more energy that increases exponentially with increasing differentials. Although no effects on apple firmness of the larger differentials were observed, using a large cooling differential during longterm storage of apples appears not to be an appropriate strategy to reduce energy. Lowering the evaporator outlet air temperature to lower values than 0 ?C could reduce the cooling times and thus also the energy consumption, at the risk of chilling injury. In the storage room, a ?hot? zone develops in the middle of the stack and underneath the cooling unit where the product temperature remains higher and rather constant in comparison to the outer regions of the stack that respond quickly to a change in supply air temperature. This resulted in a spatial difference in the final quality of the apples throughout the coolstore. Solving the continued spatial temperature heterogeneity will be more beneficial for better quality preservation and reducing energy costs. To this end, alternative airflow designs should be developed and investigated in which this model can be employed. AbstractThe main energy costs of long-term apple storage are associated with cooling. Reducing these costs without compromising product quality may be possible with minor room temperature increases. This paper presents a transient CFD model to evaluate automatic on-off cooling control based on different temperature differentials (0.4,0.5 and 0.7 ?C around a setpoint). Effects on temperature uniformity, quality changes and energy consumption during longterm storage of apples were calculated.A model for apple firmness change kinetics was coupled to the CFD model and applied to calculate changes in quality uniformity in a coolstore affected by spatiotemporal fluctuations in temperature. Large temperature fluctuations were observed near the outer edges of the stack and were more pronounced with a higher cooling differential. Using a small cooling differential around the setpoint temperature showed a better overall performance in terms of energy consumption and final product quality. ? Picture is Fig. 5 of the original paper ? Air/product temperature contour plots (outside/inside the stack) in the middle vertical plain of the coolstore for the?three different cooling differentials with a) at the end of the ?on? phase and b) at the end of the ?off? phase. Red colour?indicates a warm temperature; blue colour indicates a cold temperature. (For interpretation of the references to colour in?this figure legend, the reader is referred to the web version of this article.) (*) See references in the Source, below. NomenclatureA area [m2]CA controlled atmosphereCFD computational fluid dynamicsCp specific heat capacity [J kg?1 K?1]h heat transfer coefficient [W m?2 K?1]k thermal conductivity [W m?1 K?1]m mass [kg]qa volume airflow rate [m?3 s?1]SST shear stress transportT temperature [K]Greek symbols? density [kg m?3]Subscripta airc evaporator tubes inside the cooling uniti inlet SourceA numerical evaluation of adaptive on-off?cooling strategies for energy savings during?long-term storage of applesW. Gruyters,?P. Verboven,?M. Delele,?S.G. Gwanpua,?A. Schenk &?B. Nicola?International Journal of Refrigeration 85 (2018) 431-440https://www.sciencedirect.com/science/article/abs/pii/S0140700717304024