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Carbon sequestration in unmineable coal seams has been proposed as one of the most attractive technologies to mitigate carbon dioxide (CO 2 ) emissions in which CO 2 is stored in the microporous structure of the coal matrix in an adsorbed state. The CO 2 adsorption process is hence considered one of the more effective methodologies in environmental sciences. Thus, adsorption isotherm measurements and modelling are key important scientific measures required in understanding the adsorption system, mechanism, and process optimization in coalbeds. In this paper, three renowned and reliable adsorption isotherm models were employed including Langmuir, Freundlich, and Temkin for pure CO 2 adsorption data, and the extended-Langmuir model for multicomponent, such as flue gas mixture-adsorption data as investigated in this research work. Also, significant thermodynamics properties including the standard enthalpy change ( Δ H ∘ ), entropy change ( Δ S ∘ ), and Gibbs free energy ( Δ G ∘ ) were assessed using the van’t Hoff equation. The statistical evaluation of the goodness-of-fit was done using three (3) statistical data analysis methods including correlation coefficient ( R 2 ), standard deviation ( σ ), and standard error (SE). The Langmuir isotherm model accurately represent the pure CO 2 adsorption on the coals than Freundlich and Temkin. The extended Langmuir gives best experimental data fit for the flue gas. The thermodynamic evaluations revealed that CO 2 adsorption on the South African coals is feasible, spontaneous, and exothermic; and the adsorption mechanism is a combined physical and chemical interaction between the adsorbate and the adsorbent.

Carbon dioxide (CO2) injection into geologic formations has gained global traction, including in South Africa, to mitigate anthropogenic emissions through carbon capture, utilisation, and storage technology. These technological and technical developments require a comprehensive and reliable study of CO2 sorption equilibria under in situ unmineable coal reservoir conditions. This paper presents novel findings on the study of the equilibrium adsorption of CO2 on two South African coals measured at four temperatures between 30 and 60 °C and pressures up to 9.0 MPa using the volumetric technique. Additionally, the sorption mechanism and thermodynamic nature of the process were studied by fitting the experimental data into Langmuir–Freundlich (Sips), Tóth, and Dubinin–Astakhov (DA) isotherm models, and the Clausius–Clapeyron equation. The findings indicate that the sorption process is highly exothermic, as presented by a negative temperature effect, with the maximum working capacity estimated to range between 3.46 and 4.16 mmol/g, which is also rank- and maceral composition-dependent, with high-rank vitrinite-rich coal yielding more sorption capacity than low-rank inertinite-rich coal. The experimental data fit well in Sips and Tóth models, confirming their applicability in describing the CO2 sorption behaviour of the coals under the considered conditions. The isosteric heat of adsorption varied from 7.518 to 37.408 kJ/mol for adsorbate loading ranging from 0.4 to 3.6 mmol/g. Overall, the coals studied demonstrate well-developed sorption properties that characteristically make them viable candidates for CO2 sequestration applications for environmental sustainability.

This work introduces a methodology for the automatic unmineable inclusions detection and Bucket Wheel Excavator (BWE) collision prevention, using electromagnetic (EM) inspection and a fuzzy inference system. EM data are collected continuously ahead from the bucket wheel of a BWE and subjected to processing. Two distinct methodologies for data processing were developed and integrated into the MATLAB programming environment. The first approach, named “Simple Mode”, utilizes statistical process control to generate real-time alerts in the event of a potential collision involving the excavator’s bucket and hard rock inclusions. The advanced processing flow (“Advanced Mode”) requires accurate instrument positioning and data from successive EM scans. It incorporates techniques of local resistivity maxima detection (Position Prominence Index) as well as Neural Network-based Pattern Recognition (NNPR). A decision support process based on a Fuzzy Inference System (FIS) has been developed to assist BWE operators in avoiding collision when digging hard rock inclusions. The proposed methodology was extensively tested using synthetic EM data. Limited real data, acquired with a CMD2 (GF Instruments) EM instrument equipped with GPS, were used to control its efficiency. Increased accuracy in the automatic detection of unmineable inclusions was observed using the Advanced Mode. On the other hand, the Simple Mode processing technique offers the advantage of being independent of instrument positioning as well as it provides real-time inspection of the excavated mine slope. This work introduces a methodology for hard rock inclusion detection and can contribute to the optimization of mine operations by improving resource efficiency, safety, cost savings, and environmental sustainability.