Methane, Volume 2, Issue 1 (March 2023) – 8 articles

Since WO₃ is a relatively abundant metal oxide and features the ability to absorb in the visible spectrum, this non-toxic semiconductor is a promising photocatalyst among sustainable materials. These properties have delivered intriguing catalytic results in the conversion of methane to methanol; however, initial investigations indicate low photocatalytic efficiency resulting from fast recombination of photogenerated charges. To explore this aspect of inefficiency, five different morphologies of WO₃ consisting of micron, nanopowder, rods, wires, and flowers were obtained and characterized. In addition, several electron capture agents/oxidizers were investigated as a means of improving the separation of photogenerated charges. The photocatalytic activity of different morphologies was assessed via CH₃OH formation rates. Based on our results, WO₃ flowers produced the highest methanol productivity (38.17 ± 3.24 µmol/g-h) when 2 mM H₂O₂ was present, which is approximately four times higher in the absence of H₂O₂. This higher methanol production has been attributed to the unique structure-related properties of the flower-like structure. Photoluminescence emission spectra and diffuse reflectance data reveal that flower structures are highly catalytic due to their reduced electron/hole recombination and multiple light reflections via petal-like hollow chambers. Full article

CO₂ methanation was studied on Ni-based yttria-stabilised zirconia (Ni/YSZ) catalysts. The catalysts were prepared by the wet impregnation method, where the amount of Ni content was varied from 5% to 75%. Thereafter, the prepared catalysts were analysed by BET, XRD, SEM and H₂-TPR. BET results showed an initial increase in the surface area with an increase in Ni loading, then a decrease after 30% Ni loading. The XRD results revealed that the Ni crystallite size increased as the Ni loading increased, while the H₂-TPR showed a shift in reduction peak temperature to a higher temperature, indicating that the reducibility of the catalysts decreased as the Ni loading increased. The activity of the synthesised catalysts for CO₂ methanation was studied by passing a mixture of H₂, CO₂ and N₂ with a total flow of 135 mL min⁻¹ and GHSV of 40,500 mL h⁻¹ g⁻¹ through a continuous flow quartz tube fixed-bed reactor (I.D. = 5.5 mm, wall thickness = 2 mm) containing 200 mg of the catalyst at a temperature range of 473 to 703 K under atmospheric pressure and a H₂:CO₂ ratio of 4. The tested Ni/YSZ catalysts showed an improvement in activity as the reaction temperature increased from 473 K to around 613 to 653 K, depending on the Ni loading. Beyond the optimum temperature, the catalyst’s activity started to decline, irrespective of the Ni loading. In particular, the 40% Ni/YSZ catalyst displayed the best performance, followed by the 30% Ni/YSZ catalyst. The improved activity at high Ni loading (40% Ni) was attributed to the increase in hydrogen coverage and improved site for both H₂ and CO₂ adsorption and activation. Full article

This paper presents the implementation of natural gas vehicles (NGVs) in Tanzania’s road transportation sector. The peculiarity of this analysis is the evaluation of the technical and economic performance of the converted gasoline and diesel engines to use compressed natural gas (CNG) as the cleanest-burning hydrocarbon. The technical performance involved vehicle mileage (MiCNG), fuel consumption (Fcons), speed drop, engine fuel enhancement (Fenh), and fuel saving, while the economic performance involved conversion cost (Cc), fuel cost saving (FCsaving), and payback (PB). Considering the conversion of gasoline vehicles, the MiCNG could reach an average of 100 to 500 km per filling, depending on the CNG cylinder size. The Fenh and fuel saving were ranging between 1.9 and 3.9 and 71 and 78%. With a proportion of 30:70 diesel-CNG fuel, the heavy-duty truck with 180 kg of CNG could reach 1300 km, saving about 440 L, which is 78.6% per roundtrip, while the medium passenger car with 15 kg of CNG could reach 350 km, presenting a fuel saving of about 75%. From an economic point of view, gasoline retrofitted NGVs cost about 50 to 200 TZS/km, yielding a fuel cost saving of up to 79% and starting to pay off between 2 and 7 months or 10,000 and 40,000 km, depending on the engine capacity. Considering dual fuel, the heavy-duty truck consumes about 496 TZS/km, saving about 62.3% of diesel fuel and starting to pay off after 2.5 months or 29,304 km. To conclude, NGV technologies have been successfully implemented in Tanzania’s road transportation sector, presenting significant fuel savings and reducing reliance on imported oil. While taking measures, this study paves a way for Tanzania and other sub-Saharan countries to promote NGV growth. Full article

The reforming of methane with CO₂ was carried out efficiently in a fluidized bed reactor at 973 K under atmospheric pressure, taking advantage of the nickel catalyst efficiency achieved with a bed of particulate fines. The fluidization operation was characterized by determining a minimum velocity of 3.11 × 10⁻³ ms⁻¹ and higher velocities. The reactor worked with surface speeds of up to 1.84 × 10⁻² ms⁻¹, providing conversions from 45% to 51% and a syngas yield of 97%. The control base of the operation focused on the use of CO₂ was established through the reaction steps assumed for the process, including methane cracking, reverse Boudouard reaction, and RWGS (reverse reaction of water gas-shift). The reactor designed to operate in two zones was able to simultaneously process surface reactions and catalyst regeneration using feed with 50% excess CO₂ in relation to methane. Predictions indicating the production of syngas of different compositions quantified with the H₂/CO ratio from 2.30 to 0.91 decreasing with space-time were validated with the results available for process design. Full article

Here, Pd nanoparticles supported on ZnO were prepared by the alcohol-reduction and the borohydride-reduction methods, and their efficiency towards the photocatalytic conversion of methane under mild conditions were evaluated. The resulting Pd/ZnO photocatalysts were characterized by X-ray fluorescence, X-ray diffraction, X-ray photoelectron spectroscopy, UV–Vis, and transmission electron microscopy. The reactions were performed with the photocatalysts dispersed in water in a bubbling stream of methane under UV-light illumination. The products formed were identified and quantified by gas chromatography (GC-FID/TCD/MSD). The principal products formed were C₂H₆ and CO₂ with minor quantities of C₂H₄ and CO. No H₂ production was observed. The preparation methods influenced the size and dispersion of Pd nanoparticles on the ZnO, affecting the performance of the photocatalysts. The best performance was observed for the photocatalyst prepared by borohydride reduction with 0.5 wt% of Pd, reaching a C₂H₆ production rate of 686 µmol·h⁻¹·g⁻¹ and a C₂H₆ selectivity of 46%. Full article

The interest in Gas-to-Liquid technology (GTL) is growing worldwide because it involves a two-step indirect conversion of natural gas to higher hydrocarbons ranging from Liquefied Petroleum Gas (LPG) to paraffin wax. GTL makes it possible to obtain clean diesel, naphtha, lubes, olefins, and other industrially important organics from natural gas. This article is a brief review discussing the state-of-the-art of GTL, including the basics of syngas manufacturing as a source for Fischer-Tropsch synthesis (FTS), hydrocarbons synthesis (Fischer-Tropsch process), and product upgrading. Each one is analyzed, and the main characteristics of traditional and catalysts technologies are presented. For syngas generation, steam methane reforming, partial oxidation, two-step reforming, and autothermal reforming of methane are discussed. For Fischer–Tropsch, we highlight the role of catalysis and selectivity to high molecular weight hydrocarbons. Also, new reactors technologies, such as microreactors, are presented. The GTL technology still faces several challenges; the biggest is obtaining the right H₂:CO ratio when using a low steam-to-carbon ratio. Despite the great understanding of the carbon formation mechanism, little has been made in developing newer catalysts. Since 60–70% of a GTL plant cost is for syngas production, it needs more attention, particularly for developing the catalytic partial oxidation process (CPO), given that modern CPO processes using a ceramic membrane reactor reduce the plant’s capital cost. Improving the membrane’s mechanical, thermal, and chemical stability can commercialize the process. Catalytic challenges accompanying the FTS need attention to enhance the selectivity to produce high-octane gasoline, lower the production cost, develop new reactor systems, and enhance the selectivity to produce high molecular weight hydrocarbons. Catalytically, more attention should be given to the generation of a convenient catalyst layer and the coating process for a given configuration. Full article

Methane (CH₄) hydrate dissociation and CH₄ release are potential geohazards currently investigated using X-ray computed tomography (XCT). Image segmentation is an important data processing step for this type of research. However, it is often time consuming, computing resource-intensive, operator-dependent, and tailored for each XCT dataset due to differences in greyscale contrast. In this paper, an investigation is carried out using U-Nets, a class of Convolutional Neural Network, to segment synchrotron XCT images of CH₄-bearing sand during hydrate formation, and extract porosity and CH₄ gas saturation. Three U-Net deployments previously untried for this task are assessed: (1) a bespoke 3D hierarchical method, (2) a 2D multi-label, multi-axis method and (3) RootPainter, a 2D U-Net application with interactive corrections. U-Nets are trained using small, targeted hand-annotated datasets to reduce operator time. It was found that the segmentation accuracy of all three methods surpass mainstream watershed and thresholding techniques. Accuracy slightly reduces in low-contrast data, which affects volume fraction measurements, but errors are small compared with gravimetric methods. Moreover, U-Net models trained on low-contrast images can be used to segment higher-contrast datasets, without further training. This demonstrates model portability, which can expedite the segmentation of large datasets over short timespans. Full article