Rapid quantification of lignite sulfur content: Combining optical and X-ray approaches

Simple item page
dc.creatorKagiliery, Julia
dc.creatorChakraborty, Somsubhra
dc.creatorAcree, Autumn (TTU)
dc.creatorWeindorf, David C. (TTU)
dc.creatorBrevik, Eric C.
dc.creatorJelinski, Nicolas A.
dc.creatorLi, Bin
dc.creatorJordan, Cynthia (TTU)
dc.date.accessioned2023-04-06T15:30:51Z
dc.date.available2023-04-06T15:30:51Z
dc.date.issued2019
dc.description© 2019 The Authors cc-by-nc-nd
dc.description.abstractCoal is an important natural resource for global energy production. However, certain types of coal (e.g., lignite) often contain abundant sulfur (S) which can lead to gaseous sulfur dioxide (SO2) emissions when burned. Such emissions subsequently create sulfuric acid (H2SO4), thus causing highly acidic rain which can alter the pH of soil and surface waters. Traditional laboratory analysis (e.g., dry combustion) is commonly used to characterize the S content of lignite, but such approaches are laborious and expensive. By comparison, proximal sensing techniques such as portable X-ray fluorescence (PXRF) spectrometry, visible near infrared (VisNIR) spectroscopy, and optical sensors (e.g., NixPro) can acquire voluminous data which has been successfully used to elucidate fundamental chemistry in a wide variety of matrices. In this study, four active lignite mines were sampled in North Dakota, USA. A total of 249 samples were dried, powdered, then subjected to laboratory-based dry combustion analysis and scanned with the NixPro, VisNIR, and PXRF sensors. 75% of samples (n = 186) were used for model calibration, while 25% (n = 63) were used for validation. A strong relationship was observed between dry combustion and PXRF S content (r = 0.90). Portable X-ray fluorescence S and Fe as well as various NixPro color data were the most important variables for predicting S content. When using PXRF data in isolation, random forest regression produced a validation R2 of 0.80 in predicting total S content. Combining PXRF + NixPro improved R2 to 0.85. Dry combustion S + PXRF S and Fe correctly identified the source mine of the lignite at 55.42% via discriminant analysis. Adding the NixPro color data to the PXRF and dry combustion data, the location classification accuracy increased to 63.45%. Even with VisNIR reflectance values of 10–20%, spectral absorbance associated with water at 1940 nm was still observed. Principal component analysis was unable to resolve the mine source of the coal in PCA space, but several NixPro vectors were closely clustered. In sum, the combination of the NixPro optical sensor with PXRF data successfully augmented the predictive capability of S determination in lignite ex-situ. Future studies should extend the approach developed herein to in-situ application with special consideration of moisture and matrix efflorescence effects.
dc.identifier.citationKagiliery, J., Chakraborty, S., Acree, A., Weindorf, D.C., Brevik, E.C., Jelinski, N.A., Li, B., & Jordan, C.. 2019. Rapid quantification of lignite sulfur content: Combining optical and X-ray approaches. International Journal of Coal Geology, 216. https://doi.org/10.1016/j.coal.2019.103336
dc.identifier.urihttps://doi.org/10.1016/j.coal.2019.103336
dc.identifier.urihttps://hdl.handle.net/2346/92555
dc.language.isoeng
dc.subjectAcid rain
dc.subjectLignite
dc.subjectNixPro
dc.subjectProximal sensors
dc.subjectSulfur
dc.titleRapid quantification of lignite sulfur content: Combining optical and X-ray approaches
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
weindorf_article.pdf
Size:
3.27 MB
Format:
Adobe Portable Document Format
Description:
Main article with TTU Libraries cover page
Download

Collections

Faculty Research