Semi-volatile organic compounds in underground coal gasification effluents: from sample preparation to data analysis

Sampsonidis, Ioannis (2019) Semi-volatile organic compounds in underground coal gasification effluents: from sample preparation to data analysis. PhD thesis, University of Glasgow.

Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available in this service.
Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b3349695

Abstract

Effluents produced from the coal conversion industry, particularly from coal gasification, are complex mixtures, rich in semi-volatile organic compounds (SVOCs). In the case of underground coal gasification (UCG), the production of syngas is accompanied by significant amounts of wastewater and tar, the detailed characterisation of which is required for risk assessment and disposal purposes but also for identification of components of high economic value. This characterisation is challenging as these effluents are unique, both in their physico-chemical nature and their SVOC content.

Here, a close-to-exhaustive SVOC characterisation of UCG wastewater was performed, with the development of a novel micro-extraction method, which utilises three physico-chemical effects: ultrasonication, emulsification and salting-out. The delivery of ultrasound in the samples was performed using a novel system that transmits ultrasound into a vessel's contents through its wall, and was named high-intensity vessel-wall sonication (HIVS). The developed ultrasound-assisted surfactant-enhanced salting-out emulsification micro-extraction (UASESOEME) method proved to be successful in the extraction of SVOCs from UCG wastewater, overcoming limitations of previous methods. The HIVS technique was also applied in the development of a fast and precise ultrasound-assisted extraction (UAE) method for coal tar and in the development of an ultrasound-assisted derivatisation (UAD) method for derivatising extracts from both wastewater and coal tar, significantly enhancing their gas chromatographic analysis. Analytical methods for gas chromatography coupled to mass spectrometry (GC-MS) and comprehensive gas chromatography coupled to time-of-flight mass spectrometry (GCxGC-TOFMS) were also developed to analyse the extracts.

The above methods were applied in the extraction of SVOCs from three time-series of samples (two from simulated ex-situ UCG experiments and another from an in-situ field-scale UCG trial) and in the extraction of a series of leachates from tar leaching experiments performed with tars of increasing weathering/processing. Analyses yielded comprehensive datasets that were processed using a custom data processing approach and analysed using exploratory and multivariate statistical analysis techniques for sample classification and marker discovery. Time-series analysis indicated several SVOCs as markers, mostly oxygen and nitrogen containing compounds, most of which are not commonly considered in gasification studies; the differences between the two matrices was also highlighted, indicating coal tar as the most representative of the two. Analysis of leachates showed that they can be classified based on their SVOC signature according to the parent tar type; also, tars were shown to continue leaching SVOCs after weathering/processing and that their solubility is dependent on the ionic strength of the leaching medium.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Coal, underground coal gasification, coal tar, wastewater, GCxGC-TOFMS, GC-MS, liquid-liquid extraction, ultrasound in analytical chemistry, environmental analytical chemistry, multivariate statistical analysis.
Subjects: Q Science > QD Chemistry
Q Science > QE Geology
Colleges/Schools: College of Science and Engineering > School of Engineering > Infrastructure and Environment
Supervisor's Name: Gauchotte-Lindsay, Dr. Caroline and Watson, Dr. Ian and Younger, Prof. Paul
Date of Award: 2019
Embargo Date: 3 May 2022
Depositing User: Ioannis Sampsonidis
Unique ID: glathesis:2019-70947
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 08 May 2019 13:23
Last Modified: 18 Jul 2019 16:05
URI: http://theses.gla.ac.uk/id/eprint/70947

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