Lawrence Koech

Sodium-based sorbents have received increasing attention for spray-dry flue gas desulphurisation (FGD). This study investigates waste black ash (a sodium-rich by-product of pulp and paper sludge incineration) and raw trona (a naturally occurring sodium sesquicarbonate mineral) as low-cost sorbents for spray dry SO₂ absorption, benchmarked against commercial-grade Na₂CO₃. Experiments were conducted in a laboratory-scale spray dryer at inlet gas temperatures of 140–200 °C, Na:S ratios of 0.5–2.0, and liquid-to-gas ratios of 0.0206 – 0.0274 L/m³, with a fixed inlet SO₂ concentration of 1000 ppm. The sorbents were characterised by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy. A semi-empirical framework consisting of an exponential temperature model, a saturating exponential Na:S model, and a linear liquid-to-gas model was fitted independently for each sorbent and combined multiplicatively. The composite yielded R² values of 0.956, 0.915, and 0.962 for Botash, black ash, and trona, respectively, with RMSE values no greater than 0.031. At the reference condition of 140 °C, Na:S = 1, and L/G = 0.024 L/m³, SO₂ removal efficiencies were 73.0% for Botash, 66.8% for black ash, and 64.1% for trona. XRD-based calculations gave Na₂O-equivalent reactive contents of 58.5 wt.% for Botash, 57.8 wt.% for black ash, and 41.1 wt.% for trona. The corresponding theoretical SO₂ uptake capacities were 60.5, 59.3, and 42.5 g SO₂ per 100 g sorbent. This capacity, combined with the overall composite model across all the operating conditions, suggests that black ash is sufficient to meet FGD performance targets at Na:S ≥ 1 while reducing industrial landfill waste.

Flue gases from combustion and industrial processes generate flue gas containing various pollutants, including sulfur dioxide (SO2), nitrogen oxides (mainly NO), heavy metals like mercury (Hg) and ...

Solid-state hydrogen storage in metal hydride reactors offers a safe, low-pressure alternative to conventional hydrogen storage, but the batch nature of the system and the prohibitive cost of solid-state hydrogen concentration sensors present a significant challenge for effective process control. This study presents an integrated modelling framework for an AB5 metal hydride reactor, progressing from a validated computational fluid dynamics model through operational sensitivity analysis to an artificial neural network digital twin encompassing both the charge and discharge states. An experimentally validated finite element porous flow and reaction model achieving regression coefficient values of 0.9 or higher was used to perform a sensitivity analysis and generate training data. The sensitivity analysis found a desorption threshold of around 90 °C for the heating fluid below which no significant desorption takes place. This threshold is absent from the charge state, which likely contributed to the differences in artificial neural network architecture performance asymmetry. This was then used to perform artificial neural network-based regression to generate digital twin models for the two respective operational states of the reactor. The best-performing models achieved a regression coefficient of 0.99 or higher and a mean squared error of 10⁻⁴ or lower. This paper serves as not only a report of the findings but also as an outline of the modelling framework implemented.

High-throughput computational screening offers a strategy to identify candidate Metal-Organic Frameworks for gas storage and separation processes without rigorous experimental testing. This paper reports the application of Grand Canonical Monte Carlo simulations to screen the computationally ready subset of the newly published CoRE MOF 2024 database for carbon dioxide and hydrogen adsorption at atmospheric pressure and room temperature. Frameworks were modelled using the Universal Force Field, with carbon dioxide represented by a single-site TraPPE potential and hydrogen by a single-site Buch potential; electrostatic interactions were not considered, and frameworks were treated as rigid. Carbon dioxide loading exhibited a peaked, non-monotonic dependence on pore size and void fraction, with optimal performance at pore diameters of 5–10 Å and void fractions of 0.55–0.65, and decreased with increasing framework density, consistent with confinement-driven subcritical adsorption. Hydrogen loading, roughly an order of magnitude lower across the dataset, increased approximately uniformly with surface area, pore size, and void fraction, consistent with weak, dispersion-only supercritical adsorption. Comparison of metal node type between the full database and the top performers for each gas showed that there was a pattern of preference of the gas type to the metal node type. This is interpreted as metal nodes generating a preferred geometry opposed to electrostatic behaviour, based on the known limitations of the methodology that were necessary for the high-throughput screening. These results provide a geometrically and compositionally informed shortlist of candidate frameworks from the most recently released experimental CoRE MOF database for further, higher-accuracy simulation.

This study investigates the effect of moisture on CO ₂ adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks ( R _o V _mr : 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa. The samples were moisture‐equilibrated at different levels (0–4.4 wt.%) and tested using a high‐pressure volumetric adsorption system (HPVAS) at 35°C and up to 90 bar. Adsorption isotherms were modelled using Langmuir, Freundlich, and Temkin models. The Langmuir model consistently provided the best fit, with high R ² values of at least 0.97 across all moisture levels in all the coals. Results showed a significant drop in CO ₂ uptake in the TN coal sample with increasing moisture, up to 77% at the highest moisture level (4.45 wt.%), confirming that water competes with CO ₂ for sorption sites and blocks access to micropores. To model the experimental data, artificial neural networks (ANNs) and other machine learning methods, including Gaussian process regression, support vector machines, and decision tree‐based models, were trained using coal properties, moisture content, and pressure as inputs. The binary decision trees ensemble model achieved the best performance with R ² above 0.99, and the lowest error metrics of 0.0174 for MSE, 0.1319 for RMSE, and 0.0728 for MARE, proving the ability of the model to accurately predict CO ₂ adsorption under varying moisture conditions. The findings from this combined experimental and modelling approach supports future assessments of deep coal seams for long‐term storage.

This study investigated sulphur dioxide (SO₂) removal efficiency of sodium bicarbonate (NaHCO₃) and sodium carbonate (Na₂CO₃) sorbents using a fixed-bed reactor, relevant to dry flue gas desulphurization (FGD) technology. A Box-Behnken experimental design was used to optimize key operational parameters, including sorbent bed mass (3 - 7g), flue gas flow rate (0.6 - 1.0 l/min), and inlet flue gas temperature (140 - 180°C), to maximize desulphurization efficiency. A simulated flue gas mixture (21% CO₂, 4% O₂, 0.03% SO₂, 74.97% N₂) was heated before passing through the reactor containing the sorbent. NaHCO₃ sorbent achieved a higher SO₂ removal efficiency of up to 81%, compared to 66% for Na₂CO₃. NaHCO₃ maintained consistent performance across varying conditions, especially with changes in flue gas flow rates and temperature levels. Na₂CO₃ exhibited sensitivity to these variables, with reduced efficiency under low flue gas temperature or high flow rates. Based on the experimental findings, a predictive model was developed correlating the operational variables and SO₂ removal efficiency. The model validation indicated a strong fit to the experimental data with R² values exceeding 0.96 for both sorbents. The analysis of variance (ANOVA) identified flue gas temperature as the most significant factor influencing SO₂ removal, followed by bed mass capacity, while flue gas flow rate had minimal impact for both sorbent materials. These findings highlight the potential of NaHCO₃ and Na₂CO₃ as reliable sorbent materials for low-temperature dry FGD applications.