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The prediction of clay contents in oil shale using DRIFTS and TGA data facilitated by multivariate calibration
Fuel 85 (2006) 1396–1402 www.fuelfirst.com
The prediction of clay contents in oil shale using DRIFTS and TGA data facilitated by multivariate calibration Firas Awaja, Suresh Bhargava * School of Applied Sciences, RMIT University, P.O. Box 2476V, Melbourne, 3001, Victoria, Australia Received 29 August 2005; received in revised form 12 December 2005; accepted 20 December 2005 Available online 8 February 2006
Abstract The prediction of clay content in oil shale is important for the optimisation of oil shale processing conditions and process feasibility. The multivariate calibration technique of partial least squares regression (PLSR) was implemented in order to predict clay content in oil shale samples taken from the Stuart oil shale deposit, Queensland, Australia. The calibration data used were the diffuse reflectance infrared Fourier transformed spectroscopy (DRIFTS) spectra of 34 oil shale samples. DRIFTS data from another set of 20 oil shale samples were used for model validation. The data pre-processing includes the use of derivatives facilitated by the Savitsky-Golay nine-points’ method. A four components model was constructed and it showed a root mean square error of calibration (RMSEC) of 4.79% and a root mean square error of prediction (RMSEP) of 4.35%. TGA data sets were also used to construct a calibration model, which produced less accurate results than DRIFTS. DRIFTS, when combined with multivariate calibration, provided an accurate in situ method of evaluating clay content in oil shale. Clay content measured using XRD was used as a reference. q 2006 Published by Elsevier Ltd. Keywords: Oil shale; DRIFTS; Clay; Prediction
1. Introduction The constant decrease in petroleum sources and the increase in oil prices encourage the search for alternative energy sources. Finding substitute sources for petroleum-based products has been the main motive for extensive studies. The extraction and production of oil from oil shale is available as an alternative in many places in the world such as Australia. The estimated oil that can be theoretically produced worldwide is about 2.6 trillion barrels. Australia has the third largest oil shale deposit in the world of 32.4 billion tonnes of shale (about 220 billion barrels) with proven recoverable reserves of 1.725 billion tonnes of oil (about 11.7 billion barrels). Researchers are investigating ways to increase the feasibility of oil generating processes using oil shale as a feedstock. Oil shale is a sedimentary rock containing a complex mixture of minerals and an organic substance called kerogen. Kerogen can be converted into oil through a retorting process. The * Corresponding author. Fax: C61 3 9639 1321. E-mail address: [email protected] (S. Bhargava).
0016-2361/$ - see front matter q 2006 Published by Elsevier Ltd. doi:10.1016/j.fuel.2005.12.025
accurate determination of the mineral content of oil shale is important for the selection and optimisation of retorting process conditions. Clay minerals act as a catalyst for the coking reactions of kerogen. These reactions happen on the surfaces of the minerals and go together with the oil pyrolysis reaction, leading to a decrease in the conversion of kerogen to oil [1–4]. Accurate prediction of the clay minerals content is useful knowledge for operators and it potentially helps limiting its negative effect on oil yield. The amount of clay minerals present also affects process heat balance, trace element emission, gas composition, oil losses and the recovery of by-products [4]. The investigation of clay in this study is based on the collective results of clay minerals of smectite, kaolinite and illite. Diffuse reflectance infrared Fourier transformed spectroscopy (DRIFTS) is a cheaper, faster and non-destructive way of evaluating clay minerals and oil content of oil shale [5,6]. Combining multivariate calibration and DRIFTS by generating a model to predict mineral contents from oil shale samples based on spectral data has the potential to facilitate further the processing of oil shale. Although large numbers of variables are generated from DRIFTS spectra by using multivariate calibration methods, emphasis is often concentrated on just a few major ones.
F. Awaja, S. Bhargava / Fuel 85 (2006) 1396–1402
Many researchers have reported the IR assignment of clay minerals in the MID-IR region. Kaolinite is associated with the structural OH group band near 3700 cmK1 [5]. Clay components are reported to be associated with strong bands near 1040 and 470 cmK1 [7]. Strong intensities in the region between 450–1000 cmK1 are also linked to clay minerals [5,7]. The band at 800 cmK1 is the vibration of the Si–O group and it is assigned to kaolinite and illite [8]. The absorption band near the region at 900 cmK1 is assigned to the OH–Al group band that relates to montmorillonite, kaolinite and illite [8]. The broad absorption band near the 500 cmK1 region is assigned to the Si–O–Al group, which relates to montmorillonite, kaolinite and illite [8]. The best indication regarding clay content from TGA data is the weight loss in the 40–200 8C temperature range [9–10]. The weight loss in this region could be attributed to the loses of moisture, structural water from clay minerals and the decomposition of other minerals such as nahcolite and dawsonite [10,11]. It was also reported that the measurement of weight loss due to the release of structural water in clay minerals could be stretched to 5508C [12]. However, the relatively larger amount of kerogen decomposition of oil shale samples in the 400–600 8C temperature region prevents the accurate detection of free structural water. Multivariate calibration methods have been used in the past for predicting concentrations of unknown samples using the responses and variables of known samples. These methods proved successful with spectroscopic data in previous studies [13,14]. Prediction models can be obtained using regression analysis such as principal components regression (PCR) and partial least square regression (PLSR). Both methods are widely used in generating models from spectroscopic data. PLSR assumes a linear relationship between variables and response(s) and it also compensates for any nonlinearity by including more components. Furthermore, PLSR can handle noisy data with numerous variables [15,16]. Data preprocessing such as smoothing and/or derivatives are normally applied to spectral data to reduce or eliminate the information that is not related to the response. The main information (variance) in the data set is identified and the variables representing those variances are nominated as main components. Models generated using PLSR have already shown high success for interpretation and improved prediction [13]. The PLSR calibration method has also been previously used in predicting oil yield from Australian oil shale using DRIFTS in the near IR region [17]. This study aims to develop a model using a non-orthogonal PLSR method to predict clay content in oil shale samples using DRIFTS measurements in the mid-IR region. The interpretation of model components is also attempted. 2. Experimental 2.1. Samples Oil shale samples were obtained from South Pacific Petroleum (SPP) Co., Australia from their Stuart oil shale
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deposit in Queensland with measured modified Fisher assay (MFA) values in L TK1 and mineral content by XRD. The oil shale samples were mixed thoroughly and 3–4 g were separated from the mix and ground in an orbital-steel ring binder for 30 s. A total of 54 samples were used in this study. 34 samples were selected to cover the range of clay contents, and used for the construction of a calibration model and the rest of the samples (20 samples) were used for model validation. 2.2. DRIFTS Oil shale samples were put into the diffuse reflectance cup (10 mm diameter, 3.3 mm depth) in which packing density was ensured by applying a constant mass (of 30 g) on the top surface of each sample. The instrument used was a Perkin–Elmer spectrum 2000 FT-IR spectrometer (Perkin Elmer, UK) fitted with Praying Mantis diffuse reflectance attachment (Harrick Scientific, NY, USA). The spectra were scanned at 1 cmK1 resolution and collected in the mid-IR region from 4400 to 400 cmK1 relative to a KBr background recorded as pseudoabsorbance (log 1/R). The oil shale samples are represented with a scan each, which are used to construct the rows in the calibration set. The calibration set columns are the spectrum absorbance values (intensities) of the mid region section. There are 215 variables in which recordings were averaged every 16 cmK1. 2.3. TGA Thermogravimetric analysis (TGA) of the shale samples was undertaken using a Perkin Elmer TGA 7 thermogravimetric analyser connected to a TAC 7/DX thermal analysis controller. The initial TGA temperature for all the samples was 40 8C and the final temperature was 850 8C. The heating rate was 20 8C/min and the tests were carried out under nitrogen purging at a rate of 20 mL/min. In this work, 5–10 mg from each sample was thinly spread on a platinum pan and then subjected to TGA analysis. 2.4. Multivariate calibration The theory and principles of the PLSR multivariate calibration method used in this study can be found in many publications [13,18,19]. The spectrum region selected was from 4008 to 584 cmK1 that contains IR absorbances of 215 wave numbers (variables), which were treated for PLSR. The data were mean centred for both the response (Y) and the calibration matrix (X); baseline correction was conducted by using derivative spectra. Second derivatives were implemented using a Savitsky-Golay nine-points method. The algorithms for PLSR were developed in-house based on a description reported by Marten and Naes [13]. A non-orthogonal PLSR algorithm is used in all calculations. PLSR analyses were conducted using MathCAD software (version 11) based on the algorithms and programs developed in house.
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Fig. 1. (a) Typical oil shale spectrum, (b) second derivative for full spectra of the oil shale samples from the calibration set, (c) second derivative spectra of three oil shale samples at different clay contents.
3. Results and discussion A typical oil shale spectrum is shown in Fig. 1a. The XRD and MFA data for 54 oil shale samples as received from SPP Company are shown in Tables 1 and 2. Samples are randomly
selected to represent the calibration and the validation (prediction) data sets. Table 1 shows the clay composition of the shale samples that were used to construct the calibration model. Table 2 shows the clay components of the shale samples that were used to construct the prediction model.
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Table 1 The calibration set Sample
Smectite (wt%)
Illite (wt%)
Kaolin (wt%)
Clay (wt%)
MFA (L/T)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
7.3 19.7 16.7 13.9 19.2 8.3 11.2 9.3 16.1 18.5 16 36.6 9 10.1 21.1 11.6 13.8 15.2 18.1 18.7 17.8 30.5 3.2 20.0 14.2 3.9 25.7 0.6 2.1 11.7 23.2 29.5 19.9 0.6
11.7 9.3 6.6 8.7 8.2 17 17.6 9.1 14 11.9 12.5 15.9 7.7 8.1 12.5 16.8 8 9.6 10.1 11.1 19 8.7 17.4 0.0 19.3 9.2 12.4 28.3 22.7 13.3 13.2 17.9 17.5 3.9
5.6 11 8.1 8.4 6 10.6 9.9 7.7 17.1 14.9 16.5 12.2 6.6 9.1 13.2 6.3 5.5 12.9 14.4 6.5 15.7 13.3 6.6 4.0 12.6 6.5 8.1 16.6 7.2 4.7 16.6 13.6 13.8 5.9
24.6 40 31.4 31 33.4 35.9 38.7 26.1 47.2 45.3 45 64.7 23.3 27.3 46.8 34.7 27.3 37.7 42.6 36.3 52.5 52.5 27.2 24.0 46.1 19.6 46.2 45.5 32 29.7 53 61 51.2 10.4
268.97 176.91 212.74 177.23 189.37 223.77 175.23 140.13 123.24 113.80 139.96 63.54 238.44 186.09 164.34 179.94 202.64 168.72 148.11 240.70 79.93 89.78 153.70 106.0 102.19 249.56 97.39 4.02 110.31 284.47 86.09 8.40 71.46 145.81
US gal, 3.785 L; UK gal, 4.546 L.
Table 2 The prediction set Sample
Smectite (wt%)
Illite (wt%)
Kaolin (wt%)
Clay (wt%)
MFA (L/T)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
11.6 15.1 4.9 7.7 19.9 15.9 8.2 0.6 22.3 14.1 6 21.4 10.9 8.9 7.5 19.2 36.6 17 13.8 22.1
8.5 10.6 4.6 10.4 9.4 12.5 11.5 3.9 10.3 11.3 7.3 11.4 16.5 11.8 13.5 8.2 15.9 7.3 8 7.3
7.3 7.2 3.8 10.3 7.3 10.7 13.9 5.9 10.2 9.1 5.3 9.7 9.1 10 9.6 6 12.2 4.7 5.5 6.7
27.4 32.9 13.3 28.4 36.6 39.1 33.6 10.4 42.8 34.5 18.6 42.5 36.5 30.7 30.6 33.4 64.7 29 27.3 36.1
62.50 155.48 213.30 190.78 236.37 157.24 130.36 145.81 193.44 202.97 228.23 124.17 168.87 135.34 149.99 189.37 63.54 143.00 202.64 161.10
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Infrared spectroscopy is widely accepted as a useful method for characterising oil shale due to its fast and non-destructive approach [6,9]. The IR band near 800 cmK1 is assigned to illite and/or kaolinite mineral concentration and it is used in this study for the univariate calibration calculations (Fig. 1a). Clay minerals of illite and kaolinite showed no clear correlation with absorbance at their assigned IR band. Similarly, clay minerals content showed no correlations with intensities at 3700, 900 and 500 cmK1. Low correlation coefficient (R2) values for all the relationships were observed, which suggests that calibration and prediction of clay minerals using IR univariate calibration are not accurate. These results therefore put forward that more variables (spectral bands) in combination are needed in order to give more accurate calibrations. Hence, multivariate calibration is used with full spectral and different spectral data regions of oil shale samples in order to obtain accurate calibration and prediction models. The calibration data set was examined and processed in order to identify possible trends and behaviour prior to the multivariate modelling calculations. The oil shale spectra were processed and second derivatives were obtained. A SavitskyGolay nine-point method was used to generate second derivative spectra [20]. Fig. 1b shows the second derivative spectra of the investigated oil shale samples for the calibration set. The main areas of mineral contribution are in the regions 500–2000 cmK1 and 3100–4000 cmK1. The whole oil shale spectrum was used for calculations in the first stage and later four spectral regions were also used for comparison, as shown in Fig. 1b. The selected regions were in the 500–1100, 1100– 2000, 2000–3100 and 3100–4000 cmK1 ranges. The spectral region of 500–1100 cmK1 is reported to contain many of the bands assigned to minerals contribution. The high intensity band at 2935 cmK1 in the spectral region 2000–3100 is believed to be mostly dominated by organic matter [9]. This organic band contribution is large enough in this region to dominate the other bands. The oil shale samples showed different absorbance intensities at all regions. Three different oil shale samples taken from the calibration set with low (10%), medium (29.7%) and high (61%) clay contents are plotted in Fig. 1c to show intensity variation as a function of clay content, determined by XRD. Sharp differences in spectral intensity between the three samples can be seen in the bands at 3624, 2936, 1256 and 1176 cmK1. These bands were assigned previously to minerals [21,22]. The oil shale samples with high clay contents showed strong relative intensities at 3736, 3624, 1656, 1512, 1352, 1256 and 1176 cmK1 bands. These band intensities represent the original variables and therefore represent the main information about minerals concentration. Another data set was extracted from TGA analysis to show comparative calculations. Mineral contents of the oil shale samples affect TGA analysis and the relationship between weight loss and minerals that are present in the oil shale samples was reported in our previous article [9]. Oil shale samples from the calibration set were tested by TGA and Fig. 2a presents a plot of different TGA temperature regions and clay content as estimated by XRD. Vapour phase thermal
cracking of the oil shale samples is insignificant when compared with the mineral matter-induced cracking, which results from the analysis conditions of slow heating rate and purging gas. Clay minerals lose their moisture and interlayer water in the temperature region of 40–200 8C [9–10]. Bhargava et al. [9] explained the possible relationship between weight loss in temperature regions below 200 8C and clay content. Mineral reactions and reversibility during pyrolysis are also reported therein [9]. It is obvious that there is no clear trend between weight loss at different temperature regions and clay contents. Clay decomposition in the region between 200 and 800 8C is masked by the much larger decomposition amount of kerogen. Thus, the univariate calibration of clay content using TGA is not accurate. Hence, multivariate calibration analysis was conducted, in which the first derivative TGA data were used in order to construct a calibration model. Fig. 2b shows three oil shale samples’ DTA curves at different XRD-measured clay content. Samples with high clay content lose less weight. The major weight loss in oil shale samples is due to kerogen decomposition [9]. The weight loss apparent in Fig. 2b resulted from clay minerals decomposition, which is much lower in magnitude than kerogen and is too complex to detect using TGA. It is particularly difficult with
Fig. 2. (a) TGA weight loss at different temperature regions vs. clay content as measured by XRD, (b) DTA curves for three different oil shale samples with different clay contents.
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samples that contain a low clay content and a high kerogen and moisture content. Fig. 2b shows that low and medium clay content samples exhibited three isolated peaks at different temperature zones. These zones are in the range of 40–200, 350–650 and 700–850 8C. The weight loss in these regions is attributed to the loss of structural water, kerogen decomposition and carbonate mineral decompositions, respectively [9]. The high clay content samples did not show any peak in the 700–850 8 C temperature region. This could be attributed to lower carbonate and pyrite content. The catalytic effects of minerals on oil shale processes could inhibit pyrolysis mainly due to the presence of silicate minerals such as clays or promote pyrolysis due to the presence of carbonates. The amount of carbonates in the investigated oil shale samples was found to be insignificant and hence their effect was not considered. The multivariate calibration results are shown in Figs. 3–5. The IR second derivative data were variable mean-centred and the PLSR algorithm was applied. The calibration models that were generated from the mean-centred second derivative IR data for the full spectra and the four spectral region of 500– 1100, 1100–2000, 2000–3100 and 3100–4000 cmK1, respect-
Fig. 4. Calibrated (a) and predicted (b) clay content values vs. the measured clay content values.
ively, are shown in Fig 3a. Furthermore, another calibration model was generated for comparison that represents a combination of regions 1 and 2, i.e. from 500–2000 cmK1 wavenumbers. The spectral region of 500–2000 cmK1 produced the most accurate calibration model with the least RMSEC among other regions indicating stronger clay contribution. As expected, the region 2000–3100 cmK1 presented the worst results for the first three factors, which indicates a lesser clay contribution from this region. None of the separate spectral region when used in PLSR algorithm, produced better calibration accuracy than the full spectra.
Fig. 3. RMSEC vs. number of factors for full and partial regions of the calibration set oil shale samples spectra, (b) RMSEC and RMSEP of IR and TGA vs. number of factors.
Fig. 5. Regression coefficients derived from the four-component PLSR model.
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Hence, the prediction of the PLSR model was only carried out on full spectra data. The number of factors in the model required the estimation of RMSEP, which is widely used for this purpose. The results for the calibration and prediction models for mean-centred second derivative full spectra and DTA TGA data are shown in Fig 3b. The model that was generated from the DRIFTS data showed lower RMSEC than TGA in calibration; hence prediction models were not carried out using TGA results. A four-factor model is found to be accurate for the prediction of clay content in oil shale as indicated from the RMSEP results in Fig 3b. The RMSEP curve showed a change in trend towards the increase in RMSEP value when the number of factors was greater than 4. Models with more than four factors showed less prediction accuracy despite improved calibration accuracy. Fig. 4a and b shows the relationship between the calibrated and the predicted clay content using a four-factor PLSR model and the clay content concentration measured by XRD, respectively. Fig. 5 shows the regression coefficient (predictor) derived from the four-factor prediction model. The predictor serves to illustrate the major contributing ingredient of oil shale in the prediction of clay content. The PLSR model was affected by four spectral variables of 3700 cmK1 (kaolin), 3625 cmK1 (illite), 1040 cmK1 (clay minerals in general) and 800 cmK1 (kaolin and illite). The organic matter contribution shows a relatively strong effect, which possibly contributed to increasing the RMSEC and RMSEP of the model. In comparison with the original variables (bands) as shown in Fig 1, the correlation coefficients have strong representations of all the strong original clay bands. 4. Conclusions Multivariate calibration coupled with IR spectroscopy was useful and efficient for the prediction of clay content in oil shale samples. IR data proved to be more accurate in producing calibration models than TGA data. A four-factor model is
adequate for producing low RMSEP values for clay content in oil shale. Full spectral data produced more accurate calibration models than the data extracted at different spectral regions Acknowledgements The authors wish to thank the Southern Pacific Petroleum and the Australian Research Council for their financial assistance. References [1] El harfi K, Mokhlisse A, Ben Chanaa M. J Anal Appl Pyrol 2000;56: 207–18. [2] Berkovich AJ, Levy JH, Schmidt J, Young BR. Thermochim Acta 2000; 357–358:41–5. [3] Patterson JH, Hurst HJ, Levy JH, Killingley JS. Fuel 1990;69: 1119–23. [4] Patterson JH. Fuel 1994;73:321–7. [5] Solomon PR, Miknis FP. Fuel 1980;59:893–6. [6] Snyder RW, Painter PC, Conauer DC. Fuel 1983;62:1205–14. [7] Cronauer DC. Am Chem Soc Diff Fuel Chem Am Chem 1982;122–30. [8] Hussein SA. Mod Geol 1978;6:163–70. [9] Bhargava S, Awaja F, Subasinghe ND. Fuel 2005;84:707–15. [10] Burnham AK, Huss EB, Singleton MF. Fuel 1983;62:1199–204. [11] Sato S, Enomoto M. Fuel Process Technol 1997;53:41–7. [12] Grim RE. Clay mineralogy. New York: McGraw-Hill; 1968. [13] Martens H, Naes T. Multivariate calibration. London: Wiley; 1989. [14] Beebe RK, Pell RJ, Seasholtz MB. Chemometrics: a practical guide. London: Wiley; 1998. [15] Wold S, Trygg J, Berglund A, Antti H. Chemometr H Intell Lab Syst 2001;58:131–50. [16] Wold S, Sjostrom M, Eriksson L. Chemometr Intell Lab Syst 2001;58: 109–30. [17] Romeo MJ, Adams MJ, Hind AR, Bhargava SK, Grocott SC. J Near Infrared Spec 2002;10:223–31. [18] Lorber A, Wangen LE, Kowalski BR. J Chemometr 1987;1:19–31. [19] Thomas EV. Anal Chem 2000;72:2821–7. [20] Adams M, Awaja F, Bhargava S. Fuel 2005;84:1986–91. [21] Gadsden JA. Infrared spectra of minerals and related inorganic compounds. MA: Butterworth; 1975. [22] Grice K, Scouten S, Blokker P, Derenne S, Largea C, Nissenbaum A. Org Geochem 2003;34:471–82.
The prediction of clay contents in oil shale using DRIFTS and TGA data facilitated by multivariate calibration Firas Awaja, Suresh Bhargava * School of Applied Sciences, RMIT University, P.O. Box 2476V, Melbourne, 3001, Victoria, Australia Received 29 August 2005; received in revised form 12 December 2005; accepted 20 December 2005 Available online 8 February 2006
Abstract The prediction of clay content in oil shale is important for the optimisation of oil shale processing conditions and process feasibility. The multivariate calibration technique of partial least squares regression (PLSR) was implemented in order to predict clay content in oil shale samples taken from the Stuart oil shale deposit, Queensland, Australia. The calibration data used were the diffuse reflectance infrared Fourier transformed spectroscopy (DRIFTS) spectra of 34 oil shale samples. DRIFTS data from another set of 20 oil shale samples were used for model validation. The data pre-processing includes the use of derivatives facilitated by the Savitsky-Golay nine-points’ method. A four components model was constructed and it showed a root mean square error of calibration (RMSEC) of 4.79% and a root mean square error of prediction (RMSEP) of 4.35%. TGA data sets were also used to construct a calibration model, which produced less accurate results than DRIFTS. DRIFTS, when combined with multivariate calibration, provided an accurate in situ method of evaluating clay content in oil shale. Clay content measured using XRD was used as a reference. q 2006 Published by Elsevier Ltd. Keywords: Oil shale; DRIFTS; Clay; Prediction
1. Introduction The constant decrease in petroleum sources and the increase in oil prices encourage the search for alternative energy sources. Finding substitute sources for petroleum-based products has been the main motive for extensive studies. The extraction and production of oil from oil shale is available as an alternative in many places in the world such as Australia. The estimated oil that can be theoretically produced worldwide is about 2.6 trillion barrels. Australia has the third largest oil shale deposit in the world of 32.4 billion tonnes of shale (about 220 billion barrels) with proven recoverable reserves of 1.725 billion tonnes of oil (about 11.7 billion barrels). Researchers are investigating ways to increase the feasibility of oil generating processes using oil shale as a feedstock. Oil shale is a sedimentary rock containing a complex mixture of minerals and an organic substance called kerogen. Kerogen can be converted into oil through a retorting process. The * Corresponding author. Fax: C61 3 9639 1321. E-mail address: [email protected] (S. Bhargava).
0016-2361/$ - see front matter q 2006 Published by Elsevier Ltd. doi:10.1016/j.fuel.2005.12.025
accurate determination of the mineral content of oil shale is important for the selection and optimisation of retorting process conditions. Clay minerals act as a catalyst for the coking reactions of kerogen. These reactions happen on the surfaces of the minerals and go together with the oil pyrolysis reaction, leading to a decrease in the conversion of kerogen to oil [1–4]. Accurate prediction of the clay minerals content is useful knowledge for operators and it potentially helps limiting its negative effect on oil yield. The amount of clay minerals present also affects process heat balance, trace element emission, gas composition, oil losses and the recovery of by-products [4]. The investigation of clay in this study is based on the collective results of clay minerals of smectite, kaolinite and illite. Diffuse reflectance infrared Fourier transformed spectroscopy (DRIFTS) is a cheaper, faster and non-destructive way of evaluating clay minerals and oil content of oil shale [5,6]. Combining multivariate calibration and DRIFTS by generating a model to predict mineral contents from oil shale samples based on spectral data has the potential to facilitate further the processing of oil shale. Although large numbers of variables are generated from DRIFTS spectra by using multivariate calibration methods, emphasis is often concentrated on just a few major ones.
F. Awaja, S. Bhargava / Fuel 85 (2006) 1396–1402
Many researchers have reported the IR assignment of clay minerals in the MID-IR region. Kaolinite is associated with the structural OH group band near 3700 cmK1 [5]. Clay components are reported to be associated with strong bands near 1040 and 470 cmK1 [7]. Strong intensities in the region between 450–1000 cmK1 are also linked to clay minerals [5,7]. The band at 800 cmK1 is the vibration of the Si–O group and it is assigned to kaolinite and illite [8]. The absorption band near the region at 900 cmK1 is assigned to the OH–Al group band that relates to montmorillonite, kaolinite and illite [8]. The broad absorption band near the 500 cmK1 region is assigned to the Si–O–Al group, which relates to montmorillonite, kaolinite and illite [8]. The best indication regarding clay content from TGA data is the weight loss in the 40–200 8C temperature range [9–10]. The weight loss in this region could be attributed to the loses of moisture, structural water from clay minerals and the decomposition of other minerals such as nahcolite and dawsonite [10,11]. It was also reported that the measurement of weight loss due to the release of structural water in clay minerals could be stretched to 5508C [12]. However, the relatively larger amount of kerogen decomposition of oil shale samples in the 400–600 8C temperature region prevents the accurate detection of free structural water. Multivariate calibration methods have been used in the past for predicting concentrations of unknown samples using the responses and variables of known samples. These methods proved successful with spectroscopic data in previous studies [13,14]. Prediction models can be obtained using regression analysis such as principal components regression (PCR) and partial least square regression (PLSR). Both methods are widely used in generating models from spectroscopic data. PLSR assumes a linear relationship between variables and response(s) and it also compensates for any nonlinearity by including more components. Furthermore, PLSR can handle noisy data with numerous variables [15,16]. Data preprocessing such as smoothing and/or derivatives are normally applied to spectral data to reduce or eliminate the information that is not related to the response. The main information (variance) in the data set is identified and the variables representing those variances are nominated as main components. Models generated using PLSR have already shown high success for interpretation and improved prediction [13]. The PLSR calibration method has also been previously used in predicting oil yield from Australian oil shale using DRIFTS in the near IR region [17]. This study aims to develop a model using a non-orthogonal PLSR method to predict clay content in oil shale samples using DRIFTS measurements in the mid-IR region. The interpretation of model components is also attempted. 2. Experimental 2.1. Samples Oil shale samples were obtained from South Pacific Petroleum (SPP) Co., Australia from their Stuart oil shale
1397
deposit in Queensland with measured modified Fisher assay (MFA) values in L TK1 and mineral content by XRD. The oil shale samples were mixed thoroughly and 3–4 g were separated from the mix and ground in an orbital-steel ring binder for 30 s. A total of 54 samples were used in this study. 34 samples were selected to cover the range of clay contents, and used for the construction of a calibration model and the rest of the samples (20 samples) were used for model validation. 2.2. DRIFTS Oil shale samples were put into the diffuse reflectance cup (10 mm diameter, 3.3 mm depth) in which packing density was ensured by applying a constant mass (of 30 g) on the top surface of each sample. The instrument used was a Perkin–Elmer spectrum 2000 FT-IR spectrometer (Perkin Elmer, UK) fitted with Praying Mantis diffuse reflectance attachment (Harrick Scientific, NY, USA). The spectra were scanned at 1 cmK1 resolution and collected in the mid-IR region from 4400 to 400 cmK1 relative to a KBr background recorded as pseudoabsorbance (log 1/R). The oil shale samples are represented with a scan each, which are used to construct the rows in the calibration set. The calibration set columns are the spectrum absorbance values (intensities) of the mid region section. There are 215 variables in which recordings were averaged every 16 cmK1. 2.3. TGA Thermogravimetric analysis (TGA) of the shale samples was undertaken using a Perkin Elmer TGA 7 thermogravimetric analyser connected to a TAC 7/DX thermal analysis controller. The initial TGA temperature for all the samples was 40 8C and the final temperature was 850 8C. The heating rate was 20 8C/min and the tests were carried out under nitrogen purging at a rate of 20 mL/min. In this work, 5–10 mg from each sample was thinly spread on a platinum pan and then subjected to TGA analysis. 2.4. Multivariate calibration The theory and principles of the PLSR multivariate calibration method used in this study can be found in many publications [13,18,19]. The spectrum region selected was from 4008 to 584 cmK1 that contains IR absorbances of 215 wave numbers (variables), which were treated for PLSR. The data were mean centred for both the response (Y) and the calibration matrix (X); baseline correction was conducted by using derivative spectra. Second derivatives were implemented using a Savitsky-Golay nine-points method. The algorithms for PLSR were developed in-house based on a description reported by Marten and Naes [13]. A non-orthogonal PLSR algorithm is used in all calculations. PLSR analyses were conducted using MathCAD software (version 11) based on the algorithms and programs developed in house.
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Fig. 1. (a) Typical oil shale spectrum, (b) second derivative for full spectra of the oil shale samples from the calibration set, (c) second derivative spectra of three oil shale samples at different clay contents.
3. Results and discussion A typical oil shale spectrum is shown in Fig. 1a. The XRD and MFA data for 54 oil shale samples as received from SPP Company are shown in Tables 1 and 2. Samples are randomly
selected to represent the calibration and the validation (prediction) data sets. Table 1 shows the clay composition of the shale samples that were used to construct the calibration model. Table 2 shows the clay components of the shale samples that were used to construct the prediction model.
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Table 1 The calibration set Sample
Smectite (wt%)
Illite (wt%)
Kaolin (wt%)
Clay (wt%)
MFA (L/T)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
7.3 19.7 16.7 13.9 19.2 8.3 11.2 9.3 16.1 18.5 16 36.6 9 10.1 21.1 11.6 13.8 15.2 18.1 18.7 17.8 30.5 3.2 20.0 14.2 3.9 25.7 0.6 2.1 11.7 23.2 29.5 19.9 0.6
11.7 9.3 6.6 8.7 8.2 17 17.6 9.1 14 11.9 12.5 15.9 7.7 8.1 12.5 16.8 8 9.6 10.1 11.1 19 8.7 17.4 0.0 19.3 9.2 12.4 28.3 22.7 13.3 13.2 17.9 17.5 3.9
5.6 11 8.1 8.4 6 10.6 9.9 7.7 17.1 14.9 16.5 12.2 6.6 9.1 13.2 6.3 5.5 12.9 14.4 6.5 15.7 13.3 6.6 4.0 12.6 6.5 8.1 16.6 7.2 4.7 16.6 13.6 13.8 5.9
24.6 40 31.4 31 33.4 35.9 38.7 26.1 47.2 45.3 45 64.7 23.3 27.3 46.8 34.7 27.3 37.7 42.6 36.3 52.5 52.5 27.2 24.0 46.1 19.6 46.2 45.5 32 29.7 53 61 51.2 10.4
268.97 176.91 212.74 177.23 189.37 223.77 175.23 140.13 123.24 113.80 139.96 63.54 238.44 186.09 164.34 179.94 202.64 168.72 148.11 240.70 79.93 89.78 153.70 106.0 102.19 249.56 97.39 4.02 110.31 284.47 86.09 8.40 71.46 145.81
US gal, 3.785 L; UK gal, 4.546 L.
Table 2 The prediction set Sample
Smectite (wt%)
Illite (wt%)
Kaolin (wt%)
Clay (wt%)
MFA (L/T)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
11.6 15.1 4.9 7.7 19.9 15.9 8.2 0.6 22.3 14.1 6 21.4 10.9 8.9 7.5 19.2 36.6 17 13.8 22.1
8.5 10.6 4.6 10.4 9.4 12.5 11.5 3.9 10.3 11.3 7.3 11.4 16.5 11.8 13.5 8.2 15.9 7.3 8 7.3
7.3 7.2 3.8 10.3 7.3 10.7 13.9 5.9 10.2 9.1 5.3 9.7 9.1 10 9.6 6 12.2 4.7 5.5 6.7
27.4 32.9 13.3 28.4 36.6 39.1 33.6 10.4 42.8 34.5 18.6 42.5 36.5 30.7 30.6 33.4 64.7 29 27.3 36.1
62.50 155.48 213.30 190.78 236.37 157.24 130.36 145.81 193.44 202.97 228.23 124.17 168.87 135.34 149.99 189.37 63.54 143.00 202.64 161.10
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Infrared spectroscopy is widely accepted as a useful method for characterising oil shale due to its fast and non-destructive approach [6,9]. The IR band near 800 cmK1 is assigned to illite and/or kaolinite mineral concentration and it is used in this study for the univariate calibration calculations (Fig. 1a). Clay minerals of illite and kaolinite showed no clear correlation with absorbance at their assigned IR band. Similarly, clay minerals content showed no correlations with intensities at 3700, 900 and 500 cmK1. Low correlation coefficient (R2) values for all the relationships were observed, which suggests that calibration and prediction of clay minerals using IR univariate calibration are not accurate. These results therefore put forward that more variables (spectral bands) in combination are needed in order to give more accurate calibrations. Hence, multivariate calibration is used with full spectral and different spectral data regions of oil shale samples in order to obtain accurate calibration and prediction models. The calibration data set was examined and processed in order to identify possible trends and behaviour prior to the multivariate modelling calculations. The oil shale spectra were processed and second derivatives were obtained. A SavitskyGolay nine-point method was used to generate second derivative spectra [20]. Fig. 1b shows the second derivative spectra of the investigated oil shale samples for the calibration set. The main areas of mineral contribution are in the regions 500–2000 cmK1 and 3100–4000 cmK1. The whole oil shale spectrum was used for calculations in the first stage and later four spectral regions were also used for comparison, as shown in Fig. 1b. The selected regions were in the 500–1100, 1100– 2000, 2000–3100 and 3100–4000 cmK1 ranges. The spectral region of 500–1100 cmK1 is reported to contain many of the bands assigned to minerals contribution. The high intensity band at 2935 cmK1 in the spectral region 2000–3100 is believed to be mostly dominated by organic matter [9]. This organic band contribution is large enough in this region to dominate the other bands. The oil shale samples showed different absorbance intensities at all regions. Three different oil shale samples taken from the calibration set with low (10%), medium (29.7%) and high (61%) clay contents are plotted in Fig. 1c to show intensity variation as a function of clay content, determined by XRD. Sharp differences in spectral intensity between the three samples can be seen in the bands at 3624, 2936, 1256 and 1176 cmK1. These bands were assigned previously to minerals [21,22]. The oil shale samples with high clay contents showed strong relative intensities at 3736, 3624, 1656, 1512, 1352, 1256 and 1176 cmK1 bands. These band intensities represent the original variables and therefore represent the main information about minerals concentration. Another data set was extracted from TGA analysis to show comparative calculations. Mineral contents of the oil shale samples affect TGA analysis and the relationship between weight loss and minerals that are present in the oil shale samples was reported in our previous article [9]. Oil shale samples from the calibration set were tested by TGA and Fig. 2a presents a plot of different TGA temperature regions and clay content as estimated by XRD. Vapour phase thermal
cracking of the oil shale samples is insignificant when compared with the mineral matter-induced cracking, which results from the analysis conditions of slow heating rate and purging gas. Clay minerals lose their moisture and interlayer water in the temperature region of 40–200 8C [9–10]. Bhargava et al. [9] explained the possible relationship between weight loss in temperature regions below 200 8C and clay content. Mineral reactions and reversibility during pyrolysis are also reported therein [9]. It is obvious that there is no clear trend between weight loss at different temperature regions and clay contents. Clay decomposition in the region between 200 and 800 8C is masked by the much larger decomposition amount of kerogen. Thus, the univariate calibration of clay content using TGA is not accurate. Hence, multivariate calibration analysis was conducted, in which the first derivative TGA data were used in order to construct a calibration model. Fig. 2b shows three oil shale samples’ DTA curves at different XRD-measured clay content. Samples with high clay content lose less weight. The major weight loss in oil shale samples is due to kerogen decomposition [9]. The weight loss apparent in Fig. 2b resulted from clay minerals decomposition, which is much lower in magnitude than kerogen and is too complex to detect using TGA. It is particularly difficult with
Fig. 2. (a) TGA weight loss at different temperature regions vs. clay content as measured by XRD, (b) DTA curves for three different oil shale samples with different clay contents.
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samples that contain a low clay content and a high kerogen and moisture content. Fig. 2b shows that low and medium clay content samples exhibited three isolated peaks at different temperature zones. These zones are in the range of 40–200, 350–650 and 700–850 8C. The weight loss in these regions is attributed to the loss of structural water, kerogen decomposition and carbonate mineral decompositions, respectively [9]. The high clay content samples did not show any peak in the 700–850 8 C temperature region. This could be attributed to lower carbonate and pyrite content. The catalytic effects of minerals on oil shale processes could inhibit pyrolysis mainly due to the presence of silicate minerals such as clays or promote pyrolysis due to the presence of carbonates. The amount of carbonates in the investigated oil shale samples was found to be insignificant and hence their effect was not considered. The multivariate calibration results are shown in Figs. 3–5. The IR second derivative data were variable mean-centred and the PLSR algorithm was applied. The calibration models that were generated from the mean-centred second derivative IR data for the full spectra and the four spectral region of 500– 1100, 1100–2000, 2000–3100 and 3100–4000 cmK1, respect-
Fig. 4. Calibrated (a) and predicted (b) clay content values vs. the measured clay content values.
ively, are shown in Fig 3a. Furthermore, another calibration model was generated for comparison that represents a combination of regions 1 and 2, i.e. from 500–2000 cmK1 wavenumbers. The spectral region of 500–2000 cmK1 produced the most accurate calibration model with the least RMSEC among other regions indicating stronger clay contribution. As expected, the region 2000–3100 cmK1 presented the worst results for the first three factors, which indicates a lesser clay contribution from this region. None of the separate spectral region when used in PLSR algorithm, produced better calibration accuracy than the full spectra.
Fig. 3. RMSEC vs. number of factors for full and partial regions of the calibration set oil shale samples spectra, (b) RMSEC and RMSEP of IR and TGA vs. number of factors.
Fig. 5. Regression coefficients derived from the four-component PLSR model.
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Hence, the prediction of the PLSR model was only carried out on full spectra data. The number of factors in the model required the estimation of RMSEP, which is widely used for this purpose. The results for the calibration and prediction models for mean-centred second derivative full spectra and DTA TGA data are shown in Fig 3b. The model that was generated from the DRIFTS data showed lower RMSEC than TGA in calibration; hence prediction models were not carried out using TGA results. A four-factor model is found to be accurate for the prediction of clay content in oil shale as indicated from the RMSEP results in Fig 3b. The RMSEP curve showed a change in trend towards the increase in RMSEP value when the number of factors was greater than 4. Models with more than four factors showed less prediction accuracy despite improved calibration accuracy. Fig. 4a and b shows the relationship between the calibrated and the predicted clay content using a four-factor PLSR model and the clay content concentration measured by XRD, respectively. Fig. 5 shows the regression coefficient (predictor) derived from the four-factor prediction model. The predictor serves to illustrate the major contributing ingredient of oil shale in the prediction of clay content. The PLSR model was affected by four spectral variables of 3700 cmK1 (kaolin), 3625 cmK1 (illite), 1040 cmK1 (clay minerals in general) and 800 cmK1 (kaolin and illite). The organic matter contribution shows a relatively strong effect, which possibly contributed to increasing the RMSEC and RMSEP of the model. In comparison with the original variables (bands) as shown in Fig 1, the correlation coefficients have strong representations of all the strong original clay bands. 4. Conclusions Multivariate calibration coupled with IR spectroscopy was useful and efficient for the prediction of clay content in oil shale samples. IR data proved to be more accurate in producing calibration models than TGA data. A four-factor model is
adequate for producing low RMSEP values for clay content in oil shale. Full spectral data produced more accurate calibration models than the data extracted at different spectral regions Acknowledgements The authors wish to thank the Southern Pacific Petroleum and the Australian Research Council for their financial assistance. References [1] El harfi K, Mokhlisse A, Ben Chanaa M. J Anal Appl Pyrol 2000;56: 207–18. [2] Berkovich AJ, Levy JH, Schmidt J, Young BR. Thermochim Acta 2000; 357–358:41–5. [3] Patterson JH, Hurst HJ, Levy JH, Killingley JS. Fuel 1990;69: 1119–23. [4] Patterson JH. Fuel 1994;73:321–7. [5] Solomon PR, Miknis FP. Fuel 1980;59:893–6. [6] Snyder RW, Painter PC, Conauer DC. Fuel 1983;62:1205–14. [7] Cronauer DC. Am Chem Soc Diff Fuel Chem Am Chem 1982;122–30. [8] Hussein SA. Mod Geol 1978;6:163–70. [9] Bhargava S, Awaja F, Subasinghe ND. Fuel 2005;84:707–15. [10] Burnham AK, Huss EB, Singleton MF. Fuel 1983;62:1199–204. [11] Sato S, Enomoto M. Fuel Process Technol 1997;53:41–7. [12] Grim RE. Clay mineralogy. New York: McGraw-Hill; 1968. [13] Martens H, Naes T. Multivariate calibration. London: Wiley; 1989. [14] Beebe RK, Pell RJ, Seasholtz MB. Chemometrics: a practical guide. London: Wiley; 1998. [15] Wold S, Trygg J, Berglund A, Antti H. Chemometr H Intell Lab Syst 2001;58:131–50. [16] Wold S, Sjostrom M, Eriksson L. Chemometr Intell Lab Syst 2001;58: 109–30. [17] Romeo MJ, Adams MJ, Hind AR, Bhargava SK, Grocott SC. J Near Infrared Spec 2002;10:223–31. [18] Lorber A, Wangen LE, Kowalski BR. J Chemometr 1987;1:19–31. [19] Thomas EV. Anal Chem 2000;72:2821–7. [20] Adams M, Awaja F, Bhargava S. Fuel 2005;84:1986–91. [21] Gadsden JA. Infrared spectra of minerals and related inorganic compounds. MA: Butterworth; 1975. [22] Grice K, Scouten S, Blokker P, Derenne S, Largea C, Nissenbaum A. Org Geochem 2003;34:471–82.