A Fourier Transform Infrared Study of Mineral Matter in Coal: The Application of a Least Squares Curve-Fitting Program

PAUL C. PAINTER, SUSAN M. RIMMER, RANDY W. SNYDER, and ALAN DAVIS College of Earth and Mineral Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802

The appl icat ion of Fourier transform infrared spectroscopy to the quanti tat ive determinat ion of mineral matter in coal is discussed. The use of a least squares curve-f itt ing program a l lows a choice be tween s tandards to be made. The results of an analys i s of mineral mixtures and a coal l ow temperature ash are presented. The results are in good agreement wi th k n o w n concentrat ions and those obtained by other methods of analys is .

Index Headings: Analys is , for mineral matter in coal; Instrumen- tation, FT-IR; Methods, analyt ical .

INTRODUCTION

Coal is a complex assembly of organic and inorganic material, the structure and composition of which vary considerably according to rank and geographic locality.1 The inorganic component can exist as a separate phase or as inorganic elements chelated or ionically bound to the organic component. Mineral matter is often detri- mental to many of the processes that utilize coal, either for the direct production of energy by burning or as a source of other fuels. For example, the high pyritic con- tent of certain coals is an obvious source of sulfur dioxide pollution in combustion, while in liquefaction processes the presence of minerals leads to problems in filtration, abrasion, reactor solids build-up, and catalyst poisoning. Accordingly, the quantitative determination of mineral matter in coal is an important analytical problem and one we will address in this paper. Specifically, we will discuss the application of Fourier transform infrared (FT- IR) spectroscopy to the analysis of the inorganic com- ponents that exist in coal as strictly defined entities in the mineralogical sense.

Mineral matter in coal is usually determined after the organic phase has been removed by the low temperature ashing method. This procedure apparently results in only minimal changes in the mineral component, but it has recently been demonstrated that organic sulfur and ni- trogen can be fixed as inorganic sulfate and nitrate, respectively, in the ashing. 2-4 Nevertheless, the formation of these minerals can be taken into account; the real difficulty lies in the limitations of the techniques used to determine the mineralogical composition of the low tem- perature ash (LTA). Estep et a l ) and O'Gorman and Walker ~ applied traditional infrared and x-ray diffraction techniques but could accurately determine only those minerals with isolated well-resolved infrared bands or diffraction lines. In both techniques there are problems with overlapping peaks. Furthermore, those minerals that show preferred orientation are extremely difficult to

Received 12 February 1980; revision received 5 May 1980.

102 Volume 35, Number 1, 1981

measure quantitatively using x-ray diffraction. 7 Recent work in this laboratory has indicated that FT-IR offers considerable potential for solving this type of problem through the routine application of spectral subtraction s Essentially, the procedure consists of the successive sub- traction of the spectra of mineral "standards" from the spectrum of the LTA. As the bands of the most prevalent or most highly absorbing minerals are removed, those of the weakly absorbing or less prevalent components are revealed, allowing a more complete and accurate analysis. It was found that all major components (those constitut- ing at least 3 to 4% by weight) could be determined, providing that appropriate mineral "standards" are avail- able.

Despite the obvious potential and advantages of the FT-IR method, there are still major problems. Perhaps the most critical of these is the availability of suitable standards. This problem is particularly acute in the anal- ysis of clays, but not one unique to FT-IR since other methods also rely on standards for calibration of band and line intensities. One solution to this problem that is particularly suited to FT-IR is the compilation of an extensive mineral library, because spectra of these ma- terials can be routinely and conveniently stored on disk or magnetic tape and recalled at any future time. We are in the process of building such a library, but it is already apparent that we have in some respects substituted one problem for another. How do we choose the "correct" or most appropriate standard for a particular analysis? For example, we have kaolinite samples from different geo- graphic localities that differ subtly in their spectra ac- cording to parameters such as degree of crystallinity. Finally, even if by luck or judgment we choose an appro- priate mineral spectrum for a particular analysis, the accuracy of the FT-IR method is limited by the essen- tially subjective judgment of when bands have been exactly subtracted from a spectrum. Such errors are not large for major components having strong well resolved bands, but can become critical in determining low con- centrations of certain species.

In this paper we will examine these problems in some detail and present what we believe is at least a partial solution. Essentially, this study involves the application of least squares curve-fitting programs for FT-IR, as described by Koenig and co-workers. 9 We will demon- strate that this technique provides a powerful method for quick and reliable mineralogical analysis.

I. E X P E R I M E N T A L

The importance of meticulous and consistent sample preparation for quantitative infrared analysis cannot be

APPLIED SPECTROSCOPY

overemphasized. Estep e t a l . ~ discussed opt imum condi- tions for sample preparat ion as established by grinding studies, a point also discussed in a previous FT-IR paper, s It should also be pointed out tha t in studies using dis- persive instruments it has been customary to use approx- imately 1 mg of sample in making KBr pellets. This procedure is satisfactory if weak isolated bands are being used for calibration but leads to major errors in FT-IR work. This is because subtraction methods use the whole spectrum and 1 mg of sample often results in the strong- est bands being outside the range where the Beer-Lam- bert law holds. Typically, for most clays we have to use about 0.1 to 0.2 mg of sample and for other minerals about 0.5 to 0.8 mg. Obviously, the accurate weighing of such small quantities is critical and in our laboratory a Perkin-Elmer AD-2 autobalance is used. With these fac- tors in mind we have established the following procedure for sample preparation. 1. Weigh out precisely 300 mg of KBr and place in a

Perkin-Elmer Wig-L-Bug capsule. 2. Weigh out on a micro-balance (to three places of

decimals in milligrams) the appropriate amount of mineral, say X mg. Opt imum results are usually ob- tained with between 0.1 to 0.2 mg for clays and about 0.5 to 0.8 mg for other minerals. (The absorbance values in the final spectrum should be less than 1.0.) Place sample in capsule with the KBr.

3. Grind for 30 min in a Perkin-Elmer Wig-L-Bug. To prevent overheating this is done in three separate 10- min sequences. Hard samples such as quartz require a full 30 min, but for clays 20 min is often sufficient. Nevertheless, for consistency we grind all samples for a total of 30 min.

4. Make the KBr pellet using the standard triple press method. (Cloudy pellets should be remade.)

5. Reweigh the pellet. If the final weight of the pellet is Y mg then we assume the final weight of material in the pellet is W = X • Y/300 mg. In other words we are assuming that after grinding the mixture is homoge- neous and any losses (e.g., material stuck to the sides of the capsule) are proportional.

6. Allow the disks to stand overnight in a desiccator before recording the spectra. Infrared spectra were recorded on a Digilab model

FTS 15B Fourier t ransform spectrometer. Four hundred "scans" (interferograms) were accumulated at resolution 2 cm -l to obtain each spectrum. Each spectrum was stored on magnetic tape. We prepared a special magnetic tape containing "normalized" mineral standards. Each spectrum is normalized by multiplying by a factor equal to 1/W, where W is the corrected weight of mineral in the KBr pellet, as discussed above. Even though this procedure results in spectra where absorbance values are greater than 1.0, this does not introduce errors since we are simply multiplying a digital spectrum obtained within the Beer-Lamber t law range by a number. The conveni- ence of using such "normalized" spectra in the analysis of mineral mixtures will become apparent further below.

II. RESULTS AND DISCUSSION

For expository purposes it is convenient to first con- sider a simple example of the application of FT-IR to the

analysis of a mineral mixture before proceeding to an examination of the utility and advantages of the least squares curve-fitting program. The spectrum of a 1:1 mixture (by weight) of two clays, kaolinite and illite, is shown in Fig. 1A. Because kaolinite absorbs much more strongly, this spectrum is very similar to that of pure kaolinite, shown in Fig. lB. In fact, inspection of spec- t rum 1A with no prior knowledge of the composition would indicate tha t the sample consisted solely of kaolin- ite. However, we can use the computer to digitally sub- t ract the spectrum of the known kaolinite s tandard from that of the mixture to give the difference spectrum 1C. This difference spectrum can now be identified as illite. The spectrum of this clay is also shown in Fig. 1D for comparison. It should be noted that all spectra shown in this paper are the result of using the computer to auto- matically scale expand the spectra, so that the strongest band is plotted to full scale. The absolute absorbance values obtained for the difference spectrum are naturally much lower than those of the mixture.

As discussed in a previous communication, s this method also provides a quanti tat ive measure of the amount of the kaolinite in the above mixture from a knowledge of the weight of material in each KB r pellet and the subtraction parameters. A determinat ion of ex- t inction coefficients is not required. Applied to a complex mixture such as an LTA the procedure consists of a tedious succession of subtractions involving subjective judgments of the choice of an appropriate "s tandard" for each type of mineral identified and a corresponding trial- and-error determinat ion of subtraction parameters. In contrast, the least squares curve-fitting program provides a fast and surprisingly selective analysis. The details of this program have been described by Koenig and co- workers. ~) Essentially, spectra of up to 15 standards can be fitted to the spectrum of a mixture for a specified spectral range. The utility of the method is again best

D. Illite

C. D ~

A. Mixture (1:1) .ijf~j~._.S/llt. II ~000 500 cm "1

, L i ~ i L , b i i L ~ i , i J

Fro. 1. Scale expanded FT- IR spectra in the range 500 to 2000 cm J. A. Mixture of kaolinite and illite (l:1 by weight). B. Kaolinite. C. Difference spectrum (a - b). D. Illite.

APPLIED SPECTROSCOPY 103

i l lustrated by a simple example. Fig. 2 compares the infrared spectra of three individual clays to the spec t rum of a mixture of these materials.

As ment ioned previously, such a mixture of clays is ext remely difficult to quant i ta t ively analyze by other methods. The least squares p rogram was asked to fit the spectra of seven mineral s tandards to the spec t rum of the three component mixture. We deliberately included spectra of mineral s tandards tha t we knew were not contained in the synthet ic mixture in order to test the utility and accuracy of the procedure. The results are presented in Table I. Not only did the program pick the right clays in spite of the similarity in their spectra, but it was also able to distinguish between two different kaolinite standards. This la t ter result was ext remely en- couraging because the spectra of the two kaolinites are ext remely similar, as can be seen from Fig. 3, differing only in the relative intensities and perhaps bread th of one or two bands. X-ray diffraction analysis suggests tha t these two s tandards differ in tha t the Georgia kaolinite exhibits a higher degree of crystallinity.

In addition to identifying the right components , the program also gave a quant i ta t ive measure of the clays present tha t is in good agreement with the weighed quantities. The calculated results indicate the presence of minor amounts of iUite, probably a result of close similarities in the spectra of illite, montmoril lonite , and mica/montmori l loni te . However, negative concentra- tions for certain minerals are also predicted. This result can be explained by the fact tha t clay "s tandards" are seldom pure and often contain trace amounts of o ther minerals. The curve-fit t ing program will probably always predict small and even negative amounts of components

tha t may not be there in an a t t emp t to account for these and other variations. For the simple mineral mixtures of known concentrat ion used in this prel iminary s tudy we can eliminate the minor and negative components and repeat the least squares fit. The results are improved somewhat , as can be seen f rom Table I. Naturally, when analyzing an unknown complex mixture we mus t ensure tha t we are not inadvertent ly eliminating a minor com- ponent tha t is actually present. However, before consid- ering this problem in more detail it is worth noting tha t the p rogram output provides pa ramete r s tha t can be used to construct a composi te spec t rum by adding to- gether the spectra of the s tandards in the calculated proportions. Such a composi te spec t rum is compared to tha t of the mixture in Fig. 4, and it is apparen t tha t they are essentially identical, indicating the accuracy of the fit. The results of a corresponding analysis of o ther clay mixtures are shown in Table II, and it can be seen tha t the calculated results are in good agreement with the known weight fractions.

There are obvious problems in applying this procedure to the analysis of a coal LTA, perhaps the most impor tan t being tha t we cannot be sure tha t we have included all the necessary s tandards in the analysis. Fur thermore , we would need to determine whether those components calculated to be present at low concentrat ions are arti- facts of the least squares fit or true minor components . A solution to these problems revolves around the correct use of the program output, which comes in two forms. The program first prints a solution vector of least squares coefficients, which corresponds to the pa ramete r s used

MIXTURE

KAOLINITE

MONTMORILLONITE

1Ll00 l ~ 0 0 ~0100 ' S ; ~ I ' cm "1 S05

FIG. 2. Scale expanded FT-IR spectra in the range 500 to 1500 cm i of three clays, miea/montmorillonite, montmorillonite, and kaolinite. The spectrum shown at the top is that of 1:1:2 mixture by weight of the three clays, respectively.

TABLE I. A n a l y s i s o f minera l mixture by leas t squares FT-IR.

Mineral Wt. fraction Least squares FT-IR as prepared analysis

(%) Initial Final Kaolinite (Illinois) 50 47 46 Kaolinite (Georgia) 0 -0.3 0 Illite 0 3 0 Montmorillonite 25 31 24 Mica/montmorillonite 25 29 30 Quartz 0 -9 0 Calcite 0 -0.2 0

Fin. 3. FT-IR spectra in the range 500 to 1500 em ~ of kaolinite standards. Top: Illinois. Bottom: Georgia.

q~@ 12100 t 0100 S~@ ~00 cm "1

Fro. 4. Scale expanded FT-IR spectra in the range 500 to 1500 cm ~. Top: mineral mixture of mica/montmorillonite, montmorillonite, and kaolinite (1:I:2 by weight). Bottom: composite spectrum synthesized from the least squares fitting program.

1 0 4 V o l u m e 35 , N u m b e r 1, 1981

T A B L E II. F T - I R analysis of mineral mixtures. Mixture 1 Mixture 2 Mixture 3

Mineral Wt. Wt. Wt. Wt, Wt. Wt. fraction as fraction by fraction as fraction by fraction as fraction by prepared l)repared

prepared FT-IR (%) FT- IR (%) FT-IR (%) (%) (%) (%l

Kaolinite 25 19(19)" 25 23(23) 50 54(48) Montmorillon- 25 21(23) 0 - I 25 29(24)

ire Mica/montmo- 0 6 25 20(21) 25 27(30)

rdlonite Quartz 0 3 0 - 2 0 - 8 Calcite 0 2 0 -1 0 0 mite 50 48(58) 50 62(56l (1 3

Figures in parentheses are the results of a second analysis which includes only major

components.

for absorbance subtraction purposes. 9 These parameters are then used to calculate the percent contribution of each component spectrum to the spectrum of the mix- ture. This latter figure will correspond to the percent weight fraction of each component in the mixture only if two conditions are observed. First, the weights of all material in each KBr pellet used to obtain spectra have to be precisely the same. As we mentioned in the exper- imental section, we effectively observe this condition by normalizing all spectra by multiplying by a factor 1/W, where W is the corrected weight of material present. Second, the spectra of all components present in the mixture have to be included in the least squares fit. When we apply this procedure to the analysis of an LTA, we cannot be sure tha t we have observed this condition. However, if we simply use sequential subtraction of standards, the subtraction parameters give a direct mea- sure of the concentrat ion of each mineral, again provided that we use normalized spectra. 9 These subtraction pa- rameters are provided directly by the program output as the solution vector. Consequently, when analyzing an LTA we can use the least squares curve-fitting procedure to first pick the "best" standards from a given set. As in the analysis of the simple mixtures we can then reject those mineral spectra tha t have negative subtraction coefficients (but not necessarily those with the small positive contributions) and repeat the fit. The subtrac- tion parameters determined by this final analysis are then a quanti tat ive measure of the contribution of each mineral. Finally, a check on the accuracy of the results can be obtained by sequentially subtracting the spectrum of each component. This ensures that minor components have not been inadvertent ly ignored.

Although this procedure sounds tedious, these tasks are in fact performed routinely and quickly by the FT- IR mini-computer. As an example we will consider the results of the analysis of the LTA of an Illinois #6 coal, as presented in Table III. The percent weight fraction figures were taken directly from the solution vector. Two clays were determined to have a negative contribution, one of them, to our initial surprise, was the Illinois kaolinite. Our preconceptions were that an Illinois ka- olinite would be the best s tandard for an analysis of an Illinois coal. However, as we mentioned above, the fun- damental difference in these two standards is probably the degree of crystallinity. Consequently, the kaolinite in this sample appears to have a degree of crystallinity tha t is bet ter approximated by the Georgia kaolinite. The least squares fit was repeated after the removal of the

Illinois kaolinite and illite spectra from the refinement. These results are also presented in Table III where they are compared to the results taken from traditional in- frared and x-ray methodsJ -7 It can be seen that there is reasonable agreement for those minerals determined by both techniques. However, traditional procedures were not capable of accurately determining clays, whereas the FT-IR method does provide what appears to be a rea- sonable analysis of these materials. (A good x-ray analysis of clays using oriented slides has recently been reported, l° and we are in the process of comparing the two methods.) In addition, it has been shown previously ~ that FT-IR can accurately determine the composition of mineral mixtures, while the accuracy of traditional methods is limited by band overlap and other factors. Consequently, we place a high degree of confidence in the FT-IR anal- ysis of this LTA.

We did not determine pyrite by FT-IR because this mineral does not have absorption bands in the spectral range (1500 to 500 cm -1) used in this study. This mineral can be routinely determined by FT-IR techniques in the far infrared region. ~ Nevertheless, the amount of material unaccounted for by the FT-IR analysis is of the same order of magnitude as the concentrat ion of pyrite deter- mined by x-ray diffraction, strongly suggesting that by extending the spectral range of the analysis a complete determinat ion of the major mineral components of this coal is possible.

In order to check the accuracy of the FT-IR least-

T A B L E III. Analysis of low temperature ash (Illinois #6 coal, "round robin" sample).

Mineral

Wt. fraction by FT- IR Wt. fraction by least squares x-ray and

convent ional IR me thods Analysis I Analysis 2 (%)

Kaolinite (Illinois) - 1 2 0 Kaolini te (Georgia) 20 13 Quartz 24 25 Calcite 6 7 Pyri te N/D" N / D Montmori l loni te 21 18 Mica /montmor i l lon i te 16 9 Illite - 6 0

13.5

2O 6

2O

N / D

Total 69% 72% 59.5% Unaccounted for 31% 28% 40.5%

" N o t determined.

i03S

10|0

"14 '~g " ' , 2 ' ~ g " ' 1 g a g ' " 8 ; ~ " ' " G ; o ' , . ~ - '

FIc. 5. Scale expanded FT-IR spectra in the range 500 to 1500 cm ~. A. Kaolinite. B. Low tempera tu re ash (LTA) of an Illinois # 6 coal. B - A. Difference spectrum: LTA - kaolinite.

APPLIED SPECTROSCOPY 105

t~80 I

"789 ~79

S, LTR - K ~ j ~

Fro. 6. Scale expanded FT- IR spectra in the range 500 to 1500 cm-~, A. Quartz. B. Difference spectrum, LTA - kaolinite. B - A. Difference spectrum, LTA - kaolini te - quartz.

squares curve fitting analysis we subtracted the spectra of the mineral standards from that of the LTA using the subtraction parameters provided by the solution vector. The successive subtraction of kaolinite and quartz is shown in Figs. 5 and 6, respectively. The final difference spectrum obtained prior to elimination of the remaining clays is compared to the spectrum of montmorillonite and the mixed layer clay mica/montmorillonite in Fig. 7. The final major component is clearly a clay that is either a true mixture of these types or, more probably, has a structure and hence spectrum that can be modeled by such a mixture.

III. SUMMARY AND CONCLUSIONS

The least squares curve-fitting FT-IR program appears to be a powerful method for mineralogical analysis. One of the key problems in this type of work is the selection of standards for a particular analysis. In this study the program was able to distinguish between the contribu- tions of various clays to the spectra of known mineral mixtures and an LTA. The method is extremely fast

LTS DIFFERENCE

FIG. 7. Scale expanded FT- IR spectra in the range 500 to 1500 cm ~. Top: LTA difference spectrum. Middle: Mica /montmor i l lon i te (mixed layer clay). Bottom: Montmori l loni te .

relative to other techniques and the quantitative results were in good agreement with known concentrations and the results obtained by other methods of analysis.

ACKNOWLEDGMENTS

The authors gratefully acknowledge the financial support of the Penn State Cooperative Program in Coal Research. We would like to thank Suzanne Russell for providing various samples and Norman Suhr and Henry Gong for allowing us to use the results of their analysis of the Illinois #6 coal LTA round robin sample.

1. P. H. Given and R. F. Yarzab, in Analytical Methods for Coal and Coal Products, C. Karr, Jr., Ed. (Academic Press, New York, 1978), VoL II, p. 3.

2. R. N. Miller, R. F. Yarzab, and P. H. Given, Fuel 58, 4 (1979). 3. P. C. Painter, M. M. Coleman, R. G. Jenkins, and P. L. Walker, Jr., Fuel 57,

125 (1978), 4. P. C. Painter, J. Yontcheff, and P. H. Given, Fuel 59, 523 (1980). 5. P. A. Estep, J. J. Kovach, and C. Karr, Jr., Anal. Chem. 40, 358 (1968). 6. J. V. O'Gorman and P. L. Walker, Mineral Matter and Trace Elements in

U.S. Coal, Office of Coal Research, U.S. Department of the Interior, Research and Development Report No. 61, Interim Report No. 2, 1972.

7. R. G. Jenkins and P. L. Walker, Jr., in Analytical Methods for Coal and Coal Products, C. Karr, Jr., Ed. (Academic Press, New York, 1978), Vol. II, p. 265.

8. P. C. Painter, M. M. Coleman, R. G. Jenkins, P. W. Whang, and P. L. Walker, Jr., Fuel 57, 337 (1978).

9. M. H. Antoon, J. H. Koenig, and J. L. Koenig, Appl. Spectrosc. 31,518 (1977). 10. S. J. Russell and S. M. Rimmer, in Analytical Methods for Coal and Coal

Products (Academic Press, New York, 1980), Vol. III, p. 133.

Low Temperature Air Oxidation of Caking Coals: Fourier Transform Infrared Studies

P. C. PAINTER, M. M. COLEMAN, R. W. SNYDER, O. MAHAJAN,* M. KOMATSU, t and P. L. WALKER, JR. Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania

Fourier transform infrared spectroscopy has been used to char- acterize the oxidat ion of a coking coal. The results demonstrate that the most important initial products of oxidat ion are car- bonyl and carboxyl ic acid groups. Bands associated wi th car-

Received 25 June 1980. * Present address: S tandard Off of Indiana, Mail Stat ion B-l , Amoco

Research Center, P.O. Box 400, Napervil le, IL 60540. t Present address: Nat ional Industr ia l Ins t i tu te of Kyushu, Shuku-

machi, Tosu, 841, Japan.

106 Volume 35, Number 1, 1981

bon-oxygen single bonds, as in ethers or phenols , do not become prominent until the later stages of the oxidat ive process. Upon reaction wi th potass ium in tetrahydrofuran a number of changes in the spectrum of both the oxidized and unoxidized coal become apparent. This reagent cannot be considered spe- cific for c leavage of ether bonds, but can also lead to products usual ly associated wi th air oxidation.

Index Headings: Fourier transform infrared spectroscopy; Anal- ysis, for oxidat ion of coal.

APPLIED SPECTROSCOPY