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 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 spec- troscopy (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 concen- trated on just a few major ones. Fuel 85 (2006) 1396–1402 www.fuelfirst.com 0016-2361/$ - see front matter q 2006 Published by Elsevier Ltd. doi:10.1016/j.fuel.2005.12.025 * Corresponding author. Fax: C61 3 9639 1321. E-mail address: suresh.bhargava@rmit.edu.au (S. Bhargava).