Raman Spectroscopic Study of Coal Samples during Heating

Vibrational spectroscopy characterization of impregnated activated carbon adsorption of H2S

Activated carbon filters are used for the removal of hazardous gases from the air. This research applied vibrational spectroscopy methods, including Fourier-transform infrared spectroscopy and Raman spectroscopy to characterize hydrogen sulfide adsorption on impregnated carbon materials with metals having reactivity toward hydrogen sulfide. The Fourier-transform infrared spectroscopy results demonstrated the formation of a new chemical bond between the impregnating metals and the sulfur, indicated by the appearance of a new band at 618 cm-1. The Raman spectra results showed that for the copper-impregnated activated carbon with the highest hydrogen sulfide adsorption capacity, a new vibrational band at 475 cm-1 evolved, indicating a copper-sulfur bond. In addition, upshifts in the carbon D sub-bands were observed after efficient hydrogen sulfide adsorption, along with a larger area of the approximately 1500 cm-1 band. Therefore, Fourier-transform infrared spectroscopy and Raman spectroscopy combination can potentially indicate H2S adsorption on impregnated activated carbon filters.

Analysis of Structural Heterogeneity in Low-Rank Coal and Its Pyrolyzed Char Using Multi-Point Scanning Micro-Raman Spectroscopy

Understanding the changes in carbon structure during the mid-low-temperature pyrolysis of low-rank coal is important for efficient utilization. Raman spectroscopy is commonly used to analyze the structural order of carbonaceous materials, but traditional methods may overlook the heterogeneity of coal/char. This research explores the heterogeneity of char structure derived from low-rank coal at 700 °C through multi-point micro-Raman analysis. The analysis of parameters such as area (A), intensity (I), full width at half maximum (FWHM/W), and peak position (P) reveals that the carbon structure becomes less ordered as coal transforms into char due to the deposition of small molecules on the surface. The study emphasizes the benefits of multi-point detection for gaining in-depth insights into the structural evolution of carbonaceous materials. The increased standard deviation of Raman parameters indicates diverse structural characteristics resulting from pyrolysis at this temperature, which traditional methods may not capture effectively. The mapping method used in this research visually illustrates the distribution of carbon structures in the region.

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

CO2 Hydrogenation to Methane over Ni-Catalysts: The Effect of Support and Vanadia Promoting

Within the Waste2Fuel project, innovative, high-performance, and cost-effective fuel production methods are developed to target the “closed carbon cycle”. The catalysts supported on different metal oxides were characterized by XRD, XPS, Raman, UV-Vis, temperature-programmed techniques; then, they were tested in CO2 hydrogenation at 1 bar. Moreover, the V2O5 promotion was studied for Ni/Al2O3 catalyst. The precisely designed hydrotalcite-derived catalyst and vanadia-promoted Ni-catalysts deliver exceptional conversions for the studied processes, presenting high durability and selectivity, outperforming the best-known catalysts. The equilibrium conversion was reached at temperatures around 623 K, with the primary product of reaction CH4 (>97% CH4 yield). Although the Ni loading in hydrotalcite-derived NiWP is lower by more than 40%, compared to reference NiR catalyst and available commercial samples, the activity increases for this sample, reaching almost equilibrium values (GHSV = 1.2 × 104 h–1, 1 atm, and 293 K).