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Thermal Aspects of Refrigerated Vehicles

Discuss about the Optimize Thermal Performance Of Refrigerated Vehicles In The Distribution Of Perishable Goods.

The literature review has gone through a detailed research on the optimization of the thermal performances of the refrigerated vehicles which are required in the distribution process of a production unit. The perishable goods are always a matter of concern for the production department of an organization. However, the performance of the refrigerated vehicles can be measured with various mathematical and analytical models and the total quality control strategies need to be maintained in the systems of production (Pellicer et al, 2016).

According to the studies of the production and inventory management, the temperature of the refrigerated products should be kept under control and the limits should be maintained in the production units (Novaes et al, 2015). This will ensure the optimum safety levels for the products and the product quality level will be enhanced. Along with that, the variation in the product temperatures along with the vehicles used in the production unit are presented by various non-linear functions (Techer et al 2014). In the traditional cargo distribution the vehicle optimization with the routing process is employed and the distribution problems of the vehicles are depended on) the Travelling Salesman procedures.


The process of cargo unloading and the distribution influences the thermal aspects of the vehicles (Andreji?, Bojovi?&Kilibarda, 2016). The thermal quality and other aspects are needed to be evaluated in the analysis of PCI (Process Capability Indices). However, the thermal aspects and the temperature does not vary with the time in a linear relation, thus a Simulated Annealing Algorithm is required for this purpose to get the solution in which the temperature can be maintained easily along with the travelling of the vehicles should be minimum in the production inventory (Benedito et al 2015).

The production unit of an organization needs to ensure the quality of the production and the products along with the health safety levels by controlling the temperature all through the cold chain process. It is necessary to maintain the balancing situation over the refrigerated vehicles in the production floor. There are number of some factors which affect upholding of the eminence aspects and the perishability of the good during production (Kayansayan, Alptekin & Ezan, 2014). The factors are such as the use of proscribed atmospheres throughout the cargo space or in the transit, the chemical treatments to control the physiological disorders and the heat treatments for that purpose. The packaging and handling systems influences the perishability of the foods items and the initial quality of the commodity also affects the perishability of those food items in the inventory. The temperature maintenance of the product during the distribution, storage and transit influences the perishability too (Houdek et al 2014). However, the risk factors during the transit may influence the quality of these products and it may rapidly change in the storage and lead to quality losses.

Analysis of the Thermal Performance

The thermal aspects of the refrigerated vehicles are controlled with the Process Control Indices and it serves the benchmarking elements to constitute the refrigerated transport systems. The use of PCIs ensure the minimum level of operating effectiveness by the system. The international ISO rules are compiled in the refrigerated freight transport (Kolda et al, 2014). There are many countries who have their specific rules regarding the refrigerated freight transport. Every country use to serve some specific legislations for the temperature control requirements for the inventory management of the production units in that country.

The storage and the transport of perishable goods are taken care with some strict guidelines to the industrial aspects. However, the hygiene of the food products are major requirements in this process. Thus, the temperature maintenance should be adequately controlled and monitored. The Australian Government has put some regulation regarding this purpose. The regulation states that, the refrigerated vehicles need to have adequate cooling facility to equipoise the heat load permeated through the insulated body (Kinnear, Rose & Rolfe, 2015).

Along with that, it should have some extra capacity for the heat leakage purposes. The rules of the extra cooling capacity is states as a percentage of 25% (Dalk?l?ç et al 2015). However, the extra capacity can be stated to increase up to 200% for the trucks which have around 31 to 35 doors opening in a day. The reservation of the power depends on the time based deterioration of the insulation of the body of such vehicles along with the refrigeration plants. The heat of respiration during the transportation should produce the extra cooling capacity is there is a necessity to eliminate the deforestation heat (Laguerre, Hoang & Flick, 2013).In some cases, it is seen that, the 6-9 years old refrigerated vehicles shows a increase in energy consumption along with CO2 emissions. Thus, the refrigeration equipments of the vehicles are required to the operated reliably.

The models that discourse the evaluation of the heat and the mass transference during the time of transport can be divided in that factors which regard as the atmosphere of the transport unit and concentrate on the temperature of the products. Some of the models combine such aspects top deal with transportation. However, the fluctuations in the ambient condition, door openings and the loading of the product also influence the maintenance of the temperature of the products which are transported. There are few other models too that specifies the effects of the transportation temperatures on the food safety parameters (Li, Hwang &Radermacher, 2017).

In a research, it is found that, the models of the fluid dynamics and the techniques of the analysis of airflow in cold environments are used for the purpose of the control on the temperatures of the foods and the management of the refrigerated vehicles. The main purpose of controlling the temperature is stated as to decrease the microbial growth and the increase in quality (Muratore et al, 2015). The studies on the refrigerated vehicles enlisted that, there are few conditions which should be adopted by various production and inventory units to limit the losses and falls in the quality of foods.

In the short distanced transportation the refrigerated products are observed with more efficiency.In such cases the pre-established limits can be maintained easily and the quality controls can be implemented successfully. For this purposes, the focus should be given to the logistics application and the theoretical models can be employed to implement the dynamic nature of the TQM (Fikiin et al, 2017). The technical data should be allowed by the thermal laboratory tests and the in-the-field aspects of the behavioral characteristics should be considered in such situations. The accuracy in the data analysis for this purpose is needed to be given more preferences since the rigid technological and experimental controls are necessity for the maintenance of the refrigerated vehicles (Defraeye et al, 2016).

In the context of the thermal performance there are various problems arise due to the mishandling and the misconducts of the time-temperature indicators. However, the situation can be overcome with the proper TTI (Time- Temperature Indicators) evaluations aligned with the cold chain process. According to the studies, one of the most systematic attempts in the prediction of the temperature of the refrigerated vehicles and the foods during the transit and multidrop is the CoolVan Research programme. It is a kind of software which is established by the Food Refrigeration and Process Engineering Research Centre at the University of Bristol, UK to implement the systematic distribution in the transportation of the refrigerated vehicles and the units.

The Process Capability Indices (PCI) are the numerical elements which are used to compare the characteristics of a production. Values of such indices are equated with a large or small pre-established level along with the present requirements. The PCI are used in the statistical control of the control of the process quality and the productivity. However, the indices are convenient since it reduces the complexity of the information in a single value or number. The application of the PCI is related with the TTI values and the presentation is regarding the distribution of the data collected for the parameters of the temperature.

According to studies, the capability indices are required to relate the process parameters to the engineering specifications. This relative aspects may include the bilateral tolerances and the target value may or may not be used for this purpose (Gowreesunker, Tassou & Raeisi, 2014).

The capability indices are such dimensionless measures which can be easily understood for the purpose of quantifying the process quality in a production. During the application, the observed variable is temperature with x parameter inside the vehicle.  Along with that, a distribution passage of frozen food products is used for the same purpose. The mean m and the standard deviation σ are also measurable parameters in this purpose. If there are two sided specification limits for the observed temperature parameter x, the four capability indices are used for this purpose namely Dp, Dpm, Dpk and the Dpmk (Chen & Zhang, 2014).

The definition of the Dpindex is as –

Dp = (USL – LSL) / 6σ

The purpose of adopting the Dp index is to take control of the production process. The aim of the use is to make the index as larger as possible. The variable x is supposed to be normally distributed and the mean m is equalized to the specified targeted value xT. The coefficient of Dpis defined as the ratio of the tolerance spread and the actual spread of the process. There are few indication for the index which directs the calculation towards the perceived results of the process control (Aslam et al, 2013).

  • If Dp< 1, it indicates that the process does not meet the limits.
  • If Dp> 1, it indicates the fitted temperature variation in the indicated limits.
  • If 1<Dp< 1.33, it indicates that there is some probability for the process to meet the limits. This situation need to be taken for serious attention in the process.
  • If Dp≥33, it indicates that the process is fully accomplished.

The six-sigma coverage here represents the data base with normal distribution along with the spread of 99.73%. Along with that, the Dp≥1.33 situation represents the eight sigma coverage and it practically covers the spread of 100% with in the similar circumstances. In some practical cases the process is not centralized with the targeted value xT. However, the drawbacks in the process can be avoided with the index Dpk that is defined as –

Dpk = min {

Along with that, the Dpm can be used to consider the difference between the mean m and the target value xT.

Dpm =

However, the sample estimators can be used for the calculation of the above indices.

There are few approximated methods for the evaluation purpose of the non0normal situations in the production process. The suggested methods for this purpose reflects the accurate numbers in the sample of the non-conforming items. Through these methods, the Process Capability Indices values can be adjusted according to the degree of skewness of the population taken in this purpose. The specific factors in the computation of the deviations and the mean may vary during the adjustments of the Process Capability Indices(Chen & Zhang, 2014). However, these methods are grounded on the idea of dividing the σ into upper and the lover deviations such as σU and σL. The division can be represented with the dispersions of the upper and the lower sides around the value of the mean m respectively.

The asymmetric probability density function F(x) should be approximated with the two formulae such as –

And  

Here, the same means are used but the standard deviation are different as 2σUand 2σL. Along with that, the function G represents the standard normal pdfs with the upper and lower bounds. The upper and the lower bounds of F(x) are approximated with the functions F1(x) and the F2(x)respectively. The values of the standard deviations can be computed as 

σU= Px σ and σL = (1-Px) σ, where Px = Pr { x ≤ m}

In the evaluation of the process control and the quality, the non-normal data can be calculated with the capability indices as similar with the normal data(Aslam et al, 2013). The formulae for the calculation of the indices are as follows –

Dp = min {

Dp = min {

= min {

Setting Cx as equals to 1+ ?1 – 2Px ?, the above expression can be simplified as –

Dp =*(1/Cx)

Here, the (1/Cx) is a corrective coefficient due to the skewness of the distribution of parameter x. Along with that, the value of the Dpk should be corrected for the skewness and this can be estimated as below –

The upper and the lower capability indices can be defines as –

DpkU =  = ………….(1)

DpkL =  = …………. (2)

Using the above two equation the PCI of the parameter of the temperature x can be formulated with the both sided specifications as –

Dpk =min {DpkU, DpkL} = min {

…………… (3)

In the above equations 1 and 2, the 2σUand 2σLare used in the place of the standard deviation σto reflect the degree of skewness in the distribution of the parameter x. The underlying distribution of x is symmetric and the value of the Px is equals to 0.5. However, the distribution is skewed then the value of the Dpkwill became smaller than the value of.


The estimation of the values of the Dpand Dpk can be performed along with the calculation of the mean of the population, the standard deviation of the sample and the probability of the parameter x in this purpose.

Let, the n values of the temperature parameter x be x1, x2, x3, x4, x5, x6 …… xn. The mean of the sample be msand the standard deviation of the sample be σs. The probability of the parameter be P(x) which can be estimated with (number of observation ≤ms).

That is, P(x) ms–xi ), where the for x< 0. Then the Dpk can be evaluated by the substitution of ms, σs and P(x)for the m, σ and Pxin the equation 3.

The fourth capability index Dpmkis used when the mean m departs from the targeted value of the parameter xT. In this application, there is target value only if there are the USL and the LSL used. Consequently the Dpkindex is adopted (Chen, Yang & Chen, 2015).

Conclusion

In the production floor, the temperature of the food items largely determines the rate of the microbial activities which can be the cause of the spoilage of the most of the fresh food products. Along with that, lack of continuous monitoring of the temperature can hamper the distribution of the units. Such interruption during the transit and control can lead to the deterioration of the quality of products during the production process. The required product temperatures are needed to be maintained from the production to the consumptions stages.

References

Andreji?, M., Bojovi?, N., &Kilibarda, M. (2016). A framework for measuring transport efficiency in distribution centers. Transport Policy, 45, 99-106.

Aslam, M., Wu, C. W., Azam, M., & Jun, C. H. (2013). Variable sampling inspection for resubmitted lots based on process capability index Cpk for normally distributed items. Applied Mathematical Modelling, 37(3), 667-675.

Cárcel, J. A., Benedito, J., Cambero, M. I., Cabeza, M. C., &Ordóñez, J. A. (2015). Modeling and optimization of the E-beam treatment of chicken steaks and hamburgers, considering food safety, shelf-life, and sensory quality. Food and Bioproducts Processing, 96, 133-144.

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Chen, K. S., Yang, S. L., & Chen, H. T. (2015). Process improvement capability index with cost–A modeling method of mathematical programming. Applied Mathematical Modelling, 39(5), 1577-1586.

Dalk?l?ç, A. S., Celen, A., Çebi, A., &Wongwises, S. (2016). Effect of Refrigerant Type and Insulation Thickness on Refrigeration Systems of Land and Sea Vehicles. Strojniškivestnik-Journal of Mechanical Engineering, 62(4), 252-259.

Defraeye, T., Nicolai, B., Kirkman, W., Moore, S., van Niekerk, S., Verboven, P., &Cronjé, P. (2016). Integral performance evaluation of the fresh-produce cold chain: A case study for ambient loading of citrus in refrigerated containers. Postharvest Biology and Technology, 112, 1-13.

Fikiin, K., Stankov, B., Evans, J., Maidment, G., Foster, A., Brown, T., ...& Alvarez, G. (2017). Refrigerated warehouses as intelligent hubs to integrate renewable energy in industrial food refrigeration and to enhance power grid sustainability. Trends in Food Science & Technology, 60, 96-103.

Gowreesunker, B. L., Tassou, S. A., &Raeisi, A. H. (2014). Numerical study of the thermal performance of well freezer cabinets. In Proceedings of the 3rd IIR International Conference on Sustainability and the Cold Chain, London.

Houdek, P., Rajtmajer, V., Kolda, M., Kopecka, M., &Hegar, M. (2014). U.S. Patent Application No. 14/268,239.

Kayansayan, N., Alptekin, E., &Ezan, M. A. (2014). Flow and thermal characteristics of airflow inside a refrigerated container. International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics.

Kinnear, S., Rose, A., & Rolfe, J. (2015). Emissions reporting in the Australian road freight transport sector: is there a better method than the default option?.International Journal of Sustainable Transportation, 9(2), 93-102.

Kolda, M., Kopecka, M., Hegar, M., Rajtmajer, V., Houdek, P., Forejt, L., &Ghiraldi, A. (2014). U.S. Patent Application No. 14/888,652.

Laguerre, O., Hoang, H. M., & Flick, D. (2013). Experimental investigation and modelling in the food cold chain: Thermal and quality evolution. Trends in Food Science & Technology, 29(2), 87-97.

Li, G., Hwang, Y., &Radermacher, R. (2017). Cold Thermal Energy Storage Materials and Applications Toward Sustainability. In Energy Solutions to Combat Global Warming (pp. 67-117). Springer International Publishing.

Muratore, G., Restuccia, C., Licciardello, F., Lombardo, S., Pandino, G., &Mauromicale, G. (2015). Effect of packaging film and antibrowning solution on quality maintenance of minimally processed globe artichoke heads. Innovative Food Science & Emerging Technologies, 31, 97-104.

Novaes, A. G., Lima Jr, O. F., Carvalho, C. C. D., &Bez, E. T. (2015). Thermal performance of refrigerated vehicles in the distribution of perishable food. PesquisaOperacional, 35(2), 251-284.

Pellicer, E. L., García, J. B. A., Marín, V. M., &Albesa, B. (2016). Procedure for Optimization of a Modular Set of Batteries in a High Autonomy Electric Vehicle Regarding Control, Maintenance and Performance. In Advanced Microsystems for Automotive Applications 2016 (pp. 239-251). Springer International Publishing.

Techer, C., Baron, F., Delbrassinne, L., Belaïd, R., Brunet, N., Gillard, A., ...&Andjelkovic, M. (2014). Global overview of the risk linked to the Bacillus cereus group in the egg product industry: identification of food safety and food spoilage markers. Journal of applied microbiology, 116(5), 1344-1358.

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