Review of existing bench-to-large scale comparisons
There are few large-scale test research published in literature. As smoke toxicity is unregulated outside of mass transport industry, there is little agreement on the best methods of quantifying it on a large-scale. The available literature has therefore been reviewed in terms of the methodologies used and the data presented, and how reliable the comparison is.
The US National Institute for Science and Technology (NIST) undertook a series of large-scale experimental studies on different products: a bookcase a sofa and domestic cables. Material from each product was tested in the radiant test apparatus (NFPA 269 and ASTM E 1678) (NIST Technical Note 1760 [
7]), SSTF (NIST Technical Note 1761 [
8]), CACC (NIST Technical Note 1762 [
9]) and SDC (included in NIST Technical Note 1763 [
10]). The 3 materials were tested under conditions designed to simulate pre-flashover and post-flashover. NIST Technical note 1763 focused on comparing the data obtained from each apparatus to large-scale room corner data. None of the test data from bench or large-scale had CO yields representative of under-ventilated flaming (around 0.2 g/g).
Table 1 shows example data taken from NIST Technical Note 1761 [
8]. The data showed large variation between repeat tests for each condition shown. In addition, only the “Sofa” material came close to the CO yield typical of under-ventilated flaming. Average gas concentrations were taken “over a period of time between 4 min and 12 min from the beginning of the experiment”. Average gas concentrations were taken for approximately 3 minutes (200 seconds).
The findings from NISTs experiments were in direct contradiction to the recommendations made by Sandinge and Blomqvist [
11]. NIST suggested the use of the CACC, operated at 50 kW m
- [
2], with reduced oxygen atmosphere as the best apparatus for testing under-ventilated flaming. However, under-ventilated conditions were never reached. The study recommends altering the CO yields obtained to 0.2 g/g for use in physical fire models pertaining to under-ventilated conditions.
The TOXFIRE project [
12] tested 5 different materials in the ISO 9705 room corner test, using FTIR for gas analysis. The equivalence ratio was monitored during the tests using a phi meter, and the size of the door opening was varied in attempt to simulate under-ventilated flaming. Gas sampling was taken from the upper layer of the room close to the doorway as well as in the main ventilation duct.
The TOXFIRE project is one of the few large-scale tests that monitored the equivalence ratio throughout. During large-scale testing, there is much less control over the burning conditions your material is subject to in comparison to bench-scale assessments. The project used a phi-meter, an apparatus specifically designed to quantify equivalence ratio, to monitor this parameter [
13]. This allowed the project to identify the fire scenarios throughout their tests and allow the yields of toxicants measured to be compared directly to bench-scale data. The equivalence ratio measurements were taken from the upper layer of the room close to the doorway, as this was the only air inlet into the room, with a probe of varying length being placed in the doorway to sample from the fire plume.
In 3 of the 5 tests conducted, flashover was reached. The toxicants measured at this point were representative of under-ventilated conditions, with high yields of CO and HCN being produced. Although flash-over was achievable in these cases, it proved difficult to maintain a constant equivalence ratio once under-ventilation was reached. In some tests, additional burning outside of the opening occurred. In these cases, a lower equivalence was measured with more products of complete combustion being measured. The additional flaming will cause further changes to the chemistry of the effluent, causing the measurements to be less reliable than in tests where no secondary flaming occurred. Large-scale tests conducted must be careful to control the occurrence of secondary flaming where possible as this could produce misleading data whereby the measured toxicity could be lower than it actually is.
The uncertainties and variation observed in some results of this study can be explained by the high intensity of the fires. The intensity of the fire caused turbulence in the air flow both in and out of the room, affecting the air flow measurements and also the mass loss measurements. In tests where the fire was noticeably intense, a very noisy mass loss signal was obtained. This turbulence would have an effect on the yields measured as the yields are dependant on both the air flow and the mass loss throughout the test. It would also have an effect on how representative the gas sampling was throughout the tests. Tests where there was significant water and soot production were detrimental to the analysis and could not be included in the overall data set.
The data reported for these tests were used to make bench-scale comparisons to highlight the bench-scale test that is most representative of large-scale fire behaviour. As the equivalence ratio was monitored during these large-scale tests, it was used as a basis for the comparison of the data.
The CO yields obtained in the project were plotted as a function of equivalence ratio, as seen in
Figure 1 [
14]. The data was compared to that obtained using: the SSTF, CACC (as a function of equivalence ratio) and the FPA. Four data sets were obtained from the SSTF by a) Blomqvist using PA 6.6 (the same material and batch as used in the TOXFIRE project) [
15], b) Stec using PA 6.6 (the same material but different batch used to the TOXFIRE project) [
16] and c) Purser using PA 6 (a different material). All methods showed an increased scatter as the fire reaches higher equivalence ratios but show a general trend of increasing CO yields as the equivalence ratio increases.
The SSTF seemed to consistently under-predict the CO yield at lower equivalence ratios, and over-predict at equivalence ratios surpassing 1. The ISO room data stops at an equivalence ratio of 1.2, and so comparisons for under-ventilated flaming (where the equivalence ratio is greater than 1.2) are not possible using the available graphs. The FPA produced the highest yields of CO, but was not representative of the ISO room data shown. The CACC over-predicted CO yields below 1, and had very low yields of CO at higher equivalence ratios. This has also been shown in previous literature [
17].
While the comparison presented in
Figure 1 is good for well-ventilated flaming, as the ISO room data did not surpass 1.2, comparisons for under-ventilated flaming are not possible. It would be beneficial to view the data to a maximum value of 1.2 to prevent the excess data from skewing the results presented. When studying the data closely, the similarity between bench and large-scale data is much less prevalent.
Figure 2 shows the same comparison of HCN yields produced in the ISO room with those produced by the SSTF and CACC. The CACC fails to replicate the yields produced in the ISO room, with very small changes in HCN yield occurring as the reported equivalence ratio increases. At φ=1, the CACC was able to predict similar HCN yields as produced in the ISO room. While the SSTF showed better agreement with the ISO room data than the CACC, the data from the SSTF consistently under-predicts the ISO room data at equivalence ratios below 1. at equivalence ratios above 1, the SSTF mostly under-predicted HCN yields, with the exception of 2 data points over-predicting the yields. No comparison can be made above equivalence ratio 1.2 as the large-scale test data ends here.
NO
x (a) and HCN (b) yields produced by the SSTF were also plotted as a function of equivalence ratio as a means of bench-to-large-scale comparison between different large-scale fire tests shown in
Figure 3 [64]. The data show a poor correlation between the SSTF and the large-scale tests. At low equivalence ratios, the SSTF greatly over-predicted the yield of NO
x. The data is not comparable for equivalence ratios over 1.2. The use of an extended axis potentially skews the data, making comparison difficult. When comparing the HCN yields, a better correlation between the SSTF and the large-scale tests is observed, however this could be due to the axis being much higher than needed and again, skewing the results. The data in the equivalence ratio range of 0.5 to 1 shows good agreement. No comparison can be made at equivalence ratios above 1.2 as there is no large-scale data provided for comparison.
A series of large-scale tests were compared to the SSTF by Purser et. al [
18]. Of the 8 materials tested in the TOXFIRE project, two were selected for comparison: Polyamide 6.6 (PA66) and Polypropylene (PP). Data obtained for comparison from bench-scale assessments on the same materials, with the PP sourced separately [
19,
20]. The LDPE had the same empirical formula as PP but a different structure [
21,
22].
Figure 4 shows a comparison of CO yields as a function of the equivalence ratio using data taken from literature [
12] and compared to bench-scale SSTF data [
16], where points marked ‘X’ indicate PA6 was used. Graph a is shown using a least squares fit curve as a trendline.
At higher equivalence ratios, the SSTF data showed good agreement. At lower equivalence ratios, the SSTF under-predicts CO yield compared to the large-scale ISO room data. No data shown reached CO yields of 0.2 g/g, which would be expected of under-ventilated flaming conditions. Graph b shows more scattered data, however at lower equivalence ratios, the SSTF under-predicts CO yields. On few occasions the data shows good agreement, but overall the SSTF data compared shows under-predicted CO yields. On no occasion did the CO yields reach 0.2 g/g suggesting that while the calculated equivalence ratio was indicative of under-ventilated flaming, the CO yields were not. A further set of comparisons were made using other materials, such as wood, MDF and PIR.
However, the comparisons made in this paper have been presented after extreme statistical processing rather that using a direct comparison of gas yields as a function of equivalence ratio, as recommended in ISO 29903 [
2]. This was likely done to present the data in such a way that the comparison seems representative, while discussing the statistical processing in a way that is irreproducible and difficult to follow. The report did not define the certain variables of the statistical functions or parameters used, or discuss the selection process for the statistical manipulations.
The comparison did not directly compare gas yields. For unknown reasons, the comparison shown in this paper used a least squares best fit curve to the compartment fire data set. A least squares best fit curve is used to find the best fitting curve to a given set of data points by minimising the sum of the squares of the offsets (the residuals) of the points from the curve created. The point of a bench-to-large-scale comparison study is to directly compare the toxicant yields as a function of equivalence ratio. If the data is in good agreement, there should be no need to fit curves to data points for direct comparison. To appropriately assess if the bench-scale data is representative of large-scale data, the absolute values should be presented.
Additionally, the comparison applied “best fit curves” that were “right shifted where appropriate to align with SSTF curves before measuring the deviations”. The shifts applied were not consistent and seemingly chosen at random. There was an equivalence max shift on MDF and PIR, PMMA had a 0.053 shift applied, wood had a 0.13 shift applied, and no shift was applied to PA66 or PP. The only reasoning for the adjustments is to adjust the data to more closely align and show better agreement.
In particular, the use of different functions for best fit curves for comparisons using the same toxicant for the basis of comparison is unnecessary, particularly as ISO 29903 requires a direct comparison to be made. An example of this is the studies use of a Weibull function on select data sets. A Weibull function is used when the data is a broad range of random variables, not for continuous interval data sets such as those used for comparison [
23,
24]. A Weibull function is only used on data comparisons when using extreme value theory. This is a branch of statistics used for extreme deviations [
25], a direct contradiction to what the study claims. This is reinforced by the use of different expression functions on different materials, suggesting the functions were chosen to make the data appear less deviated and random. This may be why the study presented the values after processing rather than using a direct comparison.
While a bench-to-large-scale comparison has been made, it is unfortunate that the results presented were statistically processed and manipulated rather than directly compared.