Fire and Polymers - ACS Publications - American Chemical Society

Chapter 25

Combustion Toxicity and Chemometrics 1

Ed Metcalfe and John Tetteh

2

Downloaded by COLUMBIA UNIV on September 17, 2012 | http://pubs.acs.org Publication Date: September 15, 2001 | doi: 10.1021/bk-2001-0797.ch025

1

School of Chemical and Life Sciences, University of Greenwich, Wellington Street, London SE18 6PF, United Kingdom DiKnow Ltd., 22 St. James Close, London SE18 7LE, United Kingdom 2

FTIR spectroscopy was used to monitor gas evolution during combustion. Spectral overlap and other interferences were efficiently modeled by a chemometric methodology called Target Testing. All the important toxic gases can be directly analyzed as a function of time. The results obtained serve as vital input for toxicity modeled such as the Fractional Effective Dose (FED) model, which was used to demonstrate the methodology. Samples studied include wood, P V C and polyurethane foam for which the evolution of key toxic gases has been studied.

Introduction Toxic fire gases are currently very difficult to measure due to the complexity of the combustion atmosphere and the multiplicity of instrumentation required. To measure the main gases can require various analytical methods including continuous gas monitors such as non-dispersive infrared spectrometry and chemilumineseence, or cumulative methods such as H P L C , G C , ion chromatography, gravimetry, titrimetry. These latter methods do not yield time-dependent data, and only capture integrated values of the concentrations during the combustion process. A n important parameter in fire toxicity is the time for the atmosphere to become toxic, which is a function of concentration and the number of gases evolving as a function of exposure time. To capture the true evolution as a function of time, appropriate instrumentation and methods capable of monitoring all the relevant gases are needed. Fourier transform infrared (FTIR) spectroscopy is a technique capable of monitoring most fire gases, since they are typically small molecules with welldefined infrared spectra, and the technique can be used with a time resolution of a few seconds. However it is not usually possible to use a single wavenumber to

© 2001 American Chemical Society

In Fire and Polymers; Nelson, G., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2001.

321


322 monitor a given fire gas, since there are many spectra which are complex and overlap substantially (Figure 1). Using appropriate chemometric methods it should be possible to deal with such complex spectral data sets. Unfortunately traditional multivariate data analysis techniques such as Classical Least Squares, Principal Components Regression and Partial Least Squares are limited i n dealing with fire gases due to strict mixture calibration requirements. In this report we used a newly developed multivariate method based on Target Factor Analysis (TFA)(1). This approach eliminates the difficult task of mixture calibration but enables simultaneous monitoring of as many gases as needed. This approach enabled the flexible monitoring of gases for various samples in both real-time and post-run studies. To demonstrate these capabilities combustion profiles of wood, P V C and flame retarded polyurethane foam have been monitored and compared. The results were fed into an F E D model to estimate combined toxicity trends as a function of time. The applicability of the methodology to other spectroscopic methods i n fire chemistry is also considered.

4000

3421

2842

2264

1685

1107

528

wavenumber /cm-1

Figure 1. A typical FTIR spectral profile of a gas mixture evolved during combustion offlame retarded polyurethane foam.

Experimental and Data Analysis Calibration or reference gases are either supplied at known concentrations from standard gas cylinders or can be generated directly from diffusion or permeation tubes (2). Spectra were obtained from standard fire test equipment such as the Cone Calorimeter or the Purser Furnace. Evolved gases were monitored by a Bomem FTIR spectrometer at 4cm" resolution. A full spectrum in the range 4500 to 500cm' was acquired every 7 seconds. A l l the PTFE sample lines to the heated gas cell were maintained at 150°C. A n average gas flow rate of 4 L/min was also maintained. The gas cell used had a total volume of 0.42 L and a path length of 7.2 meters with K B r windows. Grams32 software by Galactic Industries (4) was used to control the spectrometer to acquire the spectral data. The data was saved as ASCII files and exported to the T F A software developed in the Matlab (5) environment where all the 1


1

323


analysis was performed. Details of the chemometric software method have been provided in previous publications (6). A schematic overview of the data analysis process is showed in figure 2.

Figure 2. Spectral data block containing gases A, Β and C are deconvoluted by chemometrics (Target Factor Analysis) into individual components. Using target references and their known concentrations, the identity (qualitative) and amount (quantitative) information are obtained.

Overview of Target Transformation Factor Analysis (TFA) T F A is a mathematical technique used to determine whether or not a hypothetical vector, gleaned from chemical principles or heuristic intuition, lies inside the factor space and thus contributes to the phenomenon. A summary of the mathematical theory of factor analysis in chemistry is described in the equations below. More detailed theoretical analysis may be found in reference 1. Essentially the data matrix is first decomposed into abstract factors in row and column space, and the number of significant factors is determined. These significant factors can now be subjected to various form of mathematical scrutiny to directly determine i f these factors have real chemical or physical meaning. The number of factor should generally correspond to the number of components absorbing in the spectral window under consideration.

Summary of Data Analysis A selected wavenumber window of the spectral data was subjected to factor analysis by Singular Value Decomposition (SVD) to obtain abstract factors, also generally called scores and loadings, in both row and column domains of the data matrix. Non-random factors are identified graphically and statistically. This In Fire and Polymers; Nelson, G., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2001.

324 information is used to elucidate the true physical and chemical phenomena contributing to the data and in the process filter out the noise in the data, since the factors arising from noise in the data are separated from those factors that contain significant information. The key equations are given below. In equations (l)-(4), R is the inverse matrix composed of the significant factors identified after the decomposition of the raw data matrix D into R and C . This matrix is tested with a set of expected factors X by a least squares calculation to generate a transformation matrix T. Where a target reference is not known several methods have been proposed in the literature (1) to estimate these unknowns. They include iterative key search and so called needle or uniqueness vector search. The iterative approach used here to estimate X where it is unknown is explained in detail elsewhere (1,2). Essentially where a target is not present, a prototype test vector consisting of zeros for all wavenumber points except one or more wavenumber positions where the factor profile show significant absorbance activity is used. The predicted spectrum is resubmitted as a new test vector after putting all negative absorbance values with zero. Typically the spectrum of a likely factor is estimated after five or six iterations. Τ is then used to operate on R the inverse of the significant factors in the row (spectral) domain to obtain the factors in the spectral domain, X . To calculate the profiles in the time domain the least squares fit is performed between the significant factors C in the column (time) domain and the inverse of the transformation matrix T to obtain matrix Y , which contains the evolution (concentration) profiles of the factors. +

t


t

+

n

+

D=RC

(1)

R eX = T

(2)

Ε·Τ =Χ

η

(3)

TV: =Y

(4)

+

t

Modeling Toxicity (e.g. FED) A typical input configuration for a set of gases to be analyzed is shown in Table I. The table also provides input fields for L C values required to calculate the fractional effective dose (FED) values. The F E D is calculated based on the ISO 13344 protocol (7). The F E D represents the toxic effects of various gases as linearly additive: 5 0

n

FED=I [Qjt]/LC5 Where Q is the quantity (grn , or ppmv) of species j at a given time t, and L C j is the concentration of species j to produce lethality in 50% of test animals within a specified exposure and post-exposure time. The L C values used in this study based on the ISO 13344 values are listed in Table I. A value of FED=1 means thaf, at 50% probability level, the lethal level has been reached. The equation below was used to calculate the F E D values presented in this report. H

0 j

3

jt

50

5 0

FED=[CO]/LC o 5

(CO)

+[HCN]/LC5o

+[NO]/LC

5 0 f N O

(HCN)

+[HCl]/LC

+ [N0 ]/LC 2

50(HC1)

+[HBr]/LC o

5 ( W


5

(HBr)

325 Table I. Typical F T I R wavenumber ranges and toxicity data used for automatic gas analysis. Name cm" [Low] MWt cm iHigh] LC50 Acrolein 1680 56.07 1770 CO 5700 28.01 2180 2110 C0 44.01 2410 2390 HCI 36.46 2600 3800 2850 HCN 165 27.06 3400 3200 NO 1900 1000 30.01 1940 170 N0 46.01 1600 1575 S0 64.07 1340 1360 H 0 18.02 3540 3420 HBr 80.92 2450 3800 2550 HF 20.01 4000 4200 Formaldehyde 30.03 1790 1650 Methane 16.04 2900 3050 Propane 44.10 2850 3000 Ammonia 17.03 1210 980 Methanol 32.04 980 1080 Toluene 92.14 3150 2850 Ozone 48.00 1000 1080 Acetaldehyde 48.05 1800 1680 Acetic A c i d 60.05 1400 1000 LC values from ISO 13344 protocol. 2

2


2

2

50

Results and Discussion Figure 3 shows the spectral window for H C N for a sample of flame retarded polyurethane foam. A spectral window of 3200-3400cm" was chosen for the stretching vibration. Note that the overall absorbances are quite low (

Fire and Polymers - ACS Publications - American Chemical Society

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