On the estimation of viscosities and densities of CO 2 -loaded MDEA, MDEA + AMP, MDEA + DIPA, MDEA + MEA, and MDEA + DEA aqueous solutions

Development of Artificial Intelligence Methods for Determination of Methane Solubility in Aqueous Systems

Accurate determinations of water (H2O) content in natural gases especially in the methane (CH4) phase are highly important for chemical engineers dealing with natural gas processes. To this end, development of a high performance model is necessary. Due to importance of the solubility of methane in the aqueous solutions for natural gas industries, two novel models based on the Decision Tree (DT) and Adaptive Neuro-Fuzzy Interference System (ANFIS) have been employed. To this end, a total number of 204 real methane solubility points in aqueous solution containing NaCl under different pressure and temperature conditions have been gathered. The comparisons between predicted solubility values and experimental data points have been conducted in visual and mathematical approaches. The R2 values of 1 for training and testing phases express the great ability of proposed models in calculation of methane solubility in pure water systems.

On the evaluation of hydrogen evolution reaction performance of metal-nitrogen-doped carbon electrocatalysts using machine learning technique

Single-atom catalysts (SACs) introduce as a promising category of electrocatalysts, especially in the water-splitting process. Recent studies have exhibited that nitrogen-doped carbon-based SACs can act as a great HER electrocatalyst. In this regard, Adaptive Neuro-Fuzzy Inference optimized by Gray Wolf Optimization (GWO) method was used to predict hydrogen adsorption energy (ΔG) obtained from density functional theory (DFT) for single transition-metal atoms including Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, Hf, Ta, W, Re, Os, Ir, Pt, and Au embedded in N-doped carbon of different sizes. Various descriptors such as the covalent radius, Zunger radius of the atomic d-orbital, the formation energy of the single-atom site, ionization energy, electronegativity, the d-band center from - 6 to 6 eV, number of valence electrons, Bader charge, number of occupied d states from 0 to - 2 eV, and number of unoccupied d states from 0 to 2 eV were chosen as input parameters based on sensitivity analysis. The R-squared and MSE of the developed model were 0.967 and 0.029, respectively, confirming its great accuracy in determining hydrogen adsorption energy of metal/NC electrocatalysts.

Improving the Carbon Capture Efficiency for Gas Power Plants through Amine-Based Absorbents

Environmental concern for our planet has changed significantly over time due to climate change, caused by an increasing population and the subsequent demand for electricity, and thus increased power generation. Considering that natural gas is regarded as a promising fuel for such a purpose, the need to integrate carbon capture technologies in such plants is becoming a necessity, if gas power plants are to be aligned with the reduction of CO2 in the atmosphere, through understanding the capturing efficacy of different absorbents under different operating conditions. Therefore, this study provided for the first time the comparison of available absorbents in relation to amine solvents (MEA, DEA, and DEA) CO2 removal efficiency, cost, and recirculation rate to achieve Climate change action through caron capture without causing absorbent disintegration. The study analyzed Flue under different amine-based solvent solutions (monoethanolamine (MEA), diethanolamine (DEA), and methyldiethanolamine (MDEA)), in order to compare their potential for CO2 reduction under different operating conditions and costs. This was simulated using ProMax 5.0 software modeled as a simple absorber tower to absorb CO2 from flue gas. Furthermore, MEA, DEA, and MDEA adsorbents were used with a temperature of 38 °C and their concentration varied from 10 to 15%. Circulation rates of 200–300 m3/h were used for each concentration and solvent. The findings deduced that MEA is a promising solvent compared to DEA and MDEA in terms of the highest CO2 captured; however, it is limited at the top outlet for clean flue gas, which contained 3.6295% of CO2 and less than half a percent of DEA and MDEA, but this can be addressed either by increasing the concentration to 15% or increasing the MEA circulation rate to 300 m3/h.

Artificial Neural Networks (ANNs) for Density and Viscosity Predictions of CO2 Loaded Alkanolamine + H2O Mixtures

The physical properties, like density and viscosity, of alkanolamine + H2O (water) + CO2 (carbon dioxide) mixtures receive a significant amount of attention as they are essential in equipment sizing, mathematical modelling and simulations of amine-based post-combustion CO2 capture processes. Non-linear models based on artificial neural networks (ANNs) were trained to correlate measured densities and viscosities of monoethanol amine (MEA) + H2O, MEA + H2O + CO2, and 2-amino-2-methyl-1-propanol (AMP) + MEA + H2O + CO2 mixtures and results were compared with conventional correlations found in literature. For CO2-loaded aqueous amine mixtures, results from the ANN models are in good agreement with measured properties with less than 1% average absolute relative deviation (AARD). The ANN-based methodology shows much better agreement (R2 > 0.99) between calculated and measured values than conventional correlations.

Theoretical investigation of the use of nanocages with an adsorbed halogen atom as anode materials in metal-ion batteries

The applicability of C₄₄, B₂₂N₂₂, Ge₄₄, and Al₂₂P₂₂ nanocages, as well as variants of those nanocages with an adsorbed halogen atom, as high-performance anode materials in Li-ion, Na-ion, and K-ion batteries was investigated theoretically via density functional theory. The results obtained indicate that, among the nanocages with no adsorbed halogen atom, Al₂₂P₂₂ would be the best candidate for a novel anode material for use in metal-ion batteries. Calculations also suggest that K-ion batteries which utilize these nanocages as anode materials would give better performance and would yield higher cell voltages than the corresponding Li-ion and Na-ion batteries with nanocage-based anodes. Also, the results for the nanocages with an adsorbed halogen atom imply that employing them as anode materials would lead to higher cell voltages and better metal-ion battery performance than if the nanocages with no adsorbed halogen atom were to be used as anode materials instead. Results further implied that nanocages with an adsorbed F atom would give higher cell voltages and better battery performance than nanocages with an adsorbed Cl or Br atom. We were ultimately able to conclude that a K-ion battery that utilized Al₂₁P₂₂ with an adsorbed F atom as its anode material would afford the best metal-ion battery performance; we therefore propose this as a novel highly efficient metal-ion battery. Graphical abstract The results of a theoretical investigation indicated that Al₂₂P₂₂ is a better candidate for a high-performance anode material in metal-ion batteries than Ge₄₄ is. Calculations also showed that K-ion batteries with nanocage-based anodes would produce higher cell voltages and perform better than the equivalent Li-ion and Na-ion batteries with nanocage-based anodes, and that anodes based on nanocages with an adsorbed F atom would perform better than anodes based on nanocages with an adsorbed Cl or Br atom.