On the Refrigeration Loads Analyzer (RLA) food product
thermal properties bug
Simon J Lovatt
24 April 1998
Summary
A bug has been discovered in RLA versions 1.00.730 and earlier
that results in product heat loads being underpredicted in some
circumstances. The bug only affects predictions of product
freezing heat loads and is noticeable when simulating rooms of
the continuous type with a long daily loading period.
As of version 1.00.807 and onwards, this bug has been
corrected.
Description
Users of both our Food Product Modeller (FPM) and
Refrigeration Loads Analyzer (RLA) software products have always
found some differences between the product heat loads predicted
by the two programs. These differences were due to the different
algorithms used by the two programs to simulate cooling food
products, differences in the thermal properties used in the two
programs (FPM allows the user to choose from a wide range of
thermal properties, while RLA assumes a common set for each
product), differences in the heat transfer coefficient
correlations used by the two programs, and the inclusion in RLA
of methods to estimate latent heat transfer from some products.
All of these differences generally resulted in the estimated heat
loads being within the roughly ±10% (two standard deviations
from the mean) expected uncertainties of both the finite
difference method used in FPM and the ordinary differential
equation method used in RLA.
However, a client recently brought to my attention a case
where the product heat load predicted by RLA for a continuous
carton freezer was lower than one would expect when it was
compared with a hand-calculated average heat load. When I
investigated this problem, I found that a bug in RLA's frozen
product thermal property prediction method was causing the
enthalpy at the freezing temperature to be underpredicted. This
had little effect (0 to 5% error) on the heat load during
(roughly) the first half of the freezing process for an
individual product item, but it caused the heat load later in the
process to fall away more quickly than it should have done. The
total amount of heat released by the product during a freezing
cycle was around 19% less than it should have been for a
conventional freezing process.
The consequences to the heat load prediction are illustrated
for a simplified freezing situation in Figure 1. The FPM heat
load line used the typical lean meat thermal properties, and
predicted a greater heat load than that calculated by the
corrected version of RLA because RLA assumes that the product to
be frozen has some fat content.
Impact on analyses
This bug caused no difference in the heat load predicted by an
analysis of a batch, fixed holding time or continuous chilling
process, or of any type of coldstore.
The heat load that is typically considered when using RLA to
estimate equipment sizes for a batch freezing process or fixed
holding time room is the heat load during the first half of the
freezing cycle. Figure 1 shows that the heat load during this
period was very similar both with and without the bug.
For a continuous freezing process with a long loading time,
the predicted heat load at any given moment is the sum of the
heat loads from each of many batches of freezing product. As a
result, the predicted heat load at any given time should have
been close to the average load for the process. In this case, the
bug would have a noticeable impact on the analysis, since the
average heat load would have been underpredicted. For a process
where product items are loaded 24 hours per day, the predicted
heat load should have been the average load for the process, and
this would have been underpredicted in this case by the same
amount that the total heat released was underpredicted (i.e.
around 19%). For a more typical process where product items are
loaded (for instance) 12 hours per day, the predicted heat load
during the loading period would have comprised about 50% the
initial heat loads of recently-loaded product items and 50% the
heat loads of product items loaded during the last loading
period. Thus, for in this example, the heat load during product
loading would have been underpredicted by about 9%.
When one considers the heat load on a whole freezer, the
product heat load might typically amount to 50 to 65% of the
total room load. Thus, the room heat load might have been
underpredicted by perhaps 5 to 10% for the two cases examined
above.
Future plans
Examining the thermal property models in RLA has resulted in the suggestion by users at MIRINZ that RLA users should be able to choose the thermal properties to use in their RLA simulations as they do in FPM. They have also suggested that RLA users would find it useful to be able to change carton material types, air gaps in the tops of cartons, and so on. It has also been suggested that RLA users would find it useful if RLA displayed the mass average temperatures of products during cooling processes. All of these suggestions have been recorded and may be implemented in the upcoming 32-bit version of RLA.
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