An in-house developed method is presented for the purity analysis of nitrogen (N2) built-in purifier (BIPTM)) gas for the trace contaminant gases carbon dioxide (CO2), oxygen (O2)) and carbon monoxide (CO), using gas chromatography with a pulsed discharge helium ionisation detector (GC-PDHID). Nitrogen BIPTM gas is used as a “matrix” gas or diluent gas for the gravimetric preparation of binary reference materials of CO, CO2), sulphur dioxide (SO2)) and nitric oxide (NO) at the CSIR NML gas metrology laboratory. Purity analysis of nitrogen BIPTM is required to decrease the measurement uncertainty of the calculated gravimetric concentrations of the gaseous reference materials produced. The aim of the research was to find a method where amounts <0.25 x 10-6 mol•mol-1 of CO2), O2) and CO could be simultaneously analysed in high purity nitrogen within a short time, with minimum cost and on a routine basis. Gas mixtures of trace amounts of CO2), O2) and CO in N2) were separated and quantified using a parallel dual capillary column configuration with temperature and pressure programming and a pulsed discharge helium ionisation detector (PDHID). The detection limits were 9 x 10-9 mol•mol-1 for CO2), 7 x 10-9 mol•mol-1 for O2) and 37 x 10-9 mol•mol-1 for CO with repeatability precision of 1% for carbon dioxide, 1% for oxygen and 10% for carbon monoxide for a 0.2 x 10-6 mol•mol-1 standard. The detection limits obtained were lower than those reported previously by other investigators for similar methods and the validation for the method as set out in this investigation seems to be the first for trace amounts of CO2), O2) and CO in nitrogen. The method was validated by comparison of the CO2) and CO results with results obtained using a flame ionisation detector and methanisation. The technique of sequence reversal was used to improve the peak shape of CO but there was no improvement on the results obtained with temperature and pressure programming. Although no helium purging was used to reduce atmospheric contamination, it was shown that the main source of contamination from the air was through the sampling system which was reduced to a level of ± 20 x 10-9 mol•mol-1 oxygen simply by using a higher sample flow rate. It was also found that even when large amounts of CO2) were adsorbed onto the molecular sieve column, this made no difference to the column performance at trace levels. The method has also been validated for the analysis of nitrogen in high purity oxygen and may also be used to analyse carbon dioxide and carbon monoxide in oxygen as well. / Dissertation (MSc (Chemistry))--University of Pretoria, 2008. / Chemistry / unrestricted

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Gas Chromatography (GC) is a technique for separating volatile compounds by relying on adherence differences in the chemical components of the compound. As conditions within the GC are changed, components of the mixture elute at different times. Sensors measure the elution and produce data which becomes chromatograms. By analyzing the chromatogram, the presence and quantity of the mixture's constituent components can be determined. Machine Learning (ML) is a field consisting of techniques by which machines can independently analyze data to derive their own procedures for processing it. Additionally, there are techniques for enhancing the performance of ML algorithms. Feature Selection is a technique for improving performance by using a specific subset of the data. Feature Engineering is a technique to transform the data to make processing more effective. Data Fusion is a technique which combines multiple sources of data so as to produce more useful data. This thesis applies machine learning algorithms to chromatograms. Five common machine learning algorithms are analyzed and compared, including K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Convolutional Neural Network (CNN), Decision Tree, and Random Forest (RF). Feature Selection is tested by applying window sweeps with the KNN algorithm. Feature Engineering is applied via the Principal Component Analysis (PCA) algorithm. Data Fusion is also tested. It was found that KNN and RF performed best overall. Feature Selection was very beneficial overall. PCA was helpful for some algorithms, but less so for others. Data Fusion was moderately beneficial. / Master of Science / Gas Chromatography is a method for separating a mixture into its constituent components. A chromatogram is a time series showing the detection of gas in the gas chromatography machine over time. With a properly set up gas chromatographer, different mixtures will produce different chromatograms. These differences allow researchers to determine the components or differentiate compounds from each other. Machine Learning (ML) is a field encompassing a set of methods by which machines can independently analyze data to derive the exact algorithms for processing it. There are many different machine learning algorithms which can accomplish this. There are also techniques which can process the data to make it more effective for use with machine learning. Feature Engineering is one such technique which transforms the data. Feature Selection is another technique which reduces the data to a subset. Data Fusion is a technique which combines different sources of data. Each of these processing techniques have many different implementations. This thesis applies machine learning to gas chromatography. ML systems are developed to classify mixtures based on their chromatograms. Five common machine learning algorithms are developed and compared. Some common Feature Engineering, Feature Selection, and Data Fusion techniques are also evaluated. Two of the algorithms were found to be more effective overall than the other algorithms. Feature Selection was found to be very beneficial. Feature Engineering was beneficial for some algorithms but less so for others. Data Fusion was moderately beneficial.

In order to make gas chromatography (GC) more widely accessible, considerable effort has been made in developing miniaturized GC systems. Thermal gradient gas chromatograpy (TGGC), one of the heating methods used in GC, has recieved attention over the years due to it's ability to enhance analyte focusing. The present work seeks to develop high performance miniaturized GC systems by combining miniaturized GC technology with thermal gradient control methods, creating miniaturized thermal gradient gas chromatography (µTGGC) systems. To aid in this development a thermal control system was developed and shown to successfully control various µTGGC systems. DAQ functionality was also included which allowed for the recording of temperature and power data for use in modeling applications. Thermal models of the various µTGGC systems were developed and validated against the recorded experiemental data. Thermal models were also used to aid in decisions required for the development of new µTGGC system designs. The results from the thermal models were then used to calibrate and validate a stochastic GC transport model. This transport model was then used to evaluate the effect of thermal gradient shape on GC separation performance.

Call number: LD2668 .T4 1961 S53