Improving CO2 adsorption efficiency of an amine-modified MOF-808 through the synthesis of its graphene oxide composites.

This research developed a novel composite of MOF-NH2 and graphene oxide (GO) for enhanced CO2 capture. Employing the response surface methodology-central composite design (RSM-CCD) for experiments design, various MOF-NH2/GO samples with GO loadings from 0 to 30 wt% were synthesized. The results of SEM, XRD, EDS, and BET analysis revealed that the materials maintained their MOF crystal structure, confirmed by X-ray diffraction, and exhibited unique texture, high porosity, and oxygen-enriched surface chemistry. The influence of temperature (25-65 °C) and pressure (1-9 bar) on CO2 adsorption capacity was assessed using a volumetric adsorption system. Optimum conditions were obtained at weight percent of 22.6 wt% GO, temperature of 25 °C, and pressure of 9 bar with maximum adsorption capacity of 303.61 mg/g. The incorporation of amino groups enhanced the CO2 adsorption capacity. Isotherm and kinetic analyses indicated that Freundlich and Fractional-order models best described CO2 adsorption behavior. Thermodynamic analysis showed the process was exothermic, spontaneous, and physical, with enthalpy changes of - 16.905 kJ/mol, entropy changes of - 0.030 kJ/mol K, and Gibs changes energy of - 7.904 kJ/mol. Mass transfer diffusion coefficients increased with higher GO loadings. Regenerability tests demonstrated high performance and resilience, with only a 5.79% decrease in efficiency after fifteen cycles. These findings suggest significant potential for these composites in CO2 capture technologies.

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network.

This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg-Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e-05 for the hydrogen mole fraction and 4.88e-05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

Impregnation of Silica Gel with Choline Chloride-MEA as an eco-friendly adsorbent for CO2 capture.

Deep eutectic solvents (DES) are a generation of ionic liquids that benefit from low cost, good stability, and environmental-friendly features. In this research, a porous silica gel was impregnated with a eutectic Choline Chloride-Monoethanolamine solvent (ChCl-MEA) to greatly improve its CO2 capture performance. In the impregnation, the weight percentages of ChCl-MEA were used in the range of 10-60 wt% at a temperature of 25 °C. The effect of ChCl-MEA loading on the structural properties of the DES-modified silica samples was studied by BET, FTIR, and TGA analyses. Investigation of the CO2 adsorption performance at different operational conditions showed that the modified silica gel with 50 wt% ChCl-MEA (Silica-CM50) presents the highest CO2 capture capacity of 89.32 mg/g. In the kinetic modeling, the fractional order model with a correlation coefficient of 0.998 resulted in the best fit with the experimental data. In addition, the isotherm data for Silica-CM50 were well-fitted with the Dual site Langmuir isotherm model with a correlation coefficient of 0.999, representing two distinct sites for the adsorption process. Moreover, the thermodynamic parameters including Enthalpy, Entropy, and Gibbs free energy at 25 °C were obtained to be - 2.770, - 0.005 and - 1.162, respectively. The results showed the exothermic, spontaneous and feasibility of the adsorption process.

Using NaOH@Graphene oxide-Fe3O4 as a magnetic heterogeneous catalyst for ultrasonic transesterification; experimental and modelling.

Burning fossil fuels causes toxic gas emissions to increase, therefore, scientists are trying to find alternative green fuels. One of the important alternative fuels is biodiesel. However, using eco-friendly primary materials is a main factor. Sustainable catalysts should have high performance, good activity, easy separation from reaction cells, and regenerability. In this study, to solve the mentioned problem NaOH@Graphene oxide-Fe3O4 as a magnetic catalyst was used for the first time to generate biodiesel from waste cooking oil. The crystal structure, functional groups, surface area and morphology of catalyst were studied by XRD, FTIR, BET, and FESEM techniques. The response surface methodology based central composite design (RSM-CCD) was used for biodiesel production via ultrasonic technique. The maximum biodiesel yield was 95.88% in the following operation: 10.52:1 molar ratio of methanol to oil, a catalyst weight of 3.76 wt%, a voltage of 49.58 kHz, and a time of 33.29 min. The physiochemical characterization of biodiesel was based to ASTM standard. The magnetic catalyst was high standstill to free fatty acid due to the five cycle's regeneration. The kinetic study results possess good agreement with first-order kinetics as well as the activation energy and Arrhenius constant are 49.2 kJ/min and 16.47 * 1010 min-1, respectively.

Experimental, RSM modelling, and DFT simulation of CO2 adsorption on Modified activated carbon with LiOH.

This research investigates the enhancement of CO2 adsorption capacity through the use of modified activated carbon (AC) with LiOH, focusing on operational conditions and adsorbent properties. Response Surface Methodology (RSM) is employed to optimize process parameters for maximizing CO2 adsorption capacity. The study considers temperature, pressure, LiOH concentration for modification, and adsorbent weight as independent variables across five levels. Analysis of Variance reveals that LiOH concentration, adsorbent quantity, pressure, and temperature significantly influence CO2 adsorption. Optimal values for temperature (30°C), pressure (9 bar), LiOH concentration (0.5 mol/L), and adsorbent weight (0.5 g) result in a maximal CO2 adsorption capacity of 154.90 mg/g. Equilibrium adsorption capacity is utilized for modeling, with the Freundlich model proving suitable for CO2 adsorption on LiOH-AC. Kinetic modeling indicates the second-order model's suitability for temperatures of 30 °C and 50 °C, while the Elovich model fits temperatures of 70 °C and 90 °C. Thermodynamic modeling at the optimized conditions (303 K and 6 bar) yields ∆H, ∆S, and ∆G values of adsorption as 12.258 kJ/mol, - 0.017 kJ/mol·K, and - 7.031 kJ/mol, respectively. Furthermore, structural considerations of AC are discussed alongside modeling and simulation, presenting the adsorption rate of CO2 and the binding energy index based on Density Functional Theory (DFT).

Optimization of simultaneous adsorption of nickel, copper, cadmium and zinc from sulfuric solutions using weakly acidic resins.

In this research, the adsorption of nickel (Ni), copper (Cu), cadmium (Cd), and zinc (Zn) from real sulfuric leaching solution with weakly acidic resins has been studied using response surface methodology (RSM). The adsorption process on two weakly acidic resins has been investigated as a function of pH, time, temperature, and resin dosage. The experimental results indicate that the amino phosphoric acid resin removed Ni, Cu, Cd, and Zn from an acidic solution very efficiently. Based on the central composite design (CCD) on the RSM, the statistical criteria of correlation coefficient (R2) values of Ni, Cu, Cd, and Zn are 0.9418, 0.9753, 0.9657, and 0.9189, respectively. The adsorption process followed the pseudo-second-order kinetic model and the thermodynamic calculations indicated the chemical interaction between the resin surface and the metal ions. Enthalpy values greater than zero indicate that the adsorption reaction of the metals is endothermic. The optimal adsorption process was carried out at time of 20 min, temperature of 30 0C, pH of 5, and resin dosage of 4 g/L. In these conditions, the adsorption capacity of nickel, copper, cadmium, and zinc were obtained 13.408, 7.087, 4.357, and 15.040 mg/g, respectively.

Comprehensive investigation of isotherm, RSM, and ANN modeling of CO2 capture by multi-walled carbon nanotube.

Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe-Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe-Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg-Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.

Facile synthesis of Persian gum-graphene oxide composite as a novel adsorbent for CO2 capture: characterization and optimization.

Burning fossil fuels releases toxic gases into the environment and has negative effects on it. In this study, Persian gum@Graphene oxide (Pg@GO) was synthesized and used as a novel adsorbent for CO2 capture. The characterization of materials was determined through XRD, FTIR, FE-SEM, and TGA analysis. The operating parameters including temperature, Pressure, and adsorbent weight were studied and optimized by response surface methodology via Box-Behnken design (RSM-BBD). The highest amount of CO2 adsorption capacity was 4.80 mmol/g, achieved at 300 K and 7.8 bar and 0.4 g of adsorbent weight. To identify the behavior and performance of the Pg@GO, various isotherm and kinetic models were used to fit with the highest correlation coefficient (R2) amounts of 0.955 and 0.986, respectively. The results proved that the adsorption of CO2 molecules on the adsorbent surface is heterogeneous. Based on thermodynamic results, as the value of ΔG° is - 8.169 at 300 K, the CO2 adsorption process is exothermic, and spontaneous.

Efficient CO2 adsorption using chitosan, graphene oxide, and zinc oxide composite.

This study was deeply focused on developing a novel CTS/GO/ZnO composite as an efficient adsorbent for CO2 adsorption process. To do so, design of experiment (DOE) was done based on RSM-BBD technique and according to the DOE runs, various CTS/GO/ZnO samples were synthesized with different GO loading (in the range of 0 wt% to 20 wt%) and different ZnO nanoparticle's loading (in the range of 0 wt% to 20 wt%). A volumetric adsorption setup was used to investigate the effect of temperature (in the range of 25-65 °C) and pressure (in the range of 1-9 bar) on the obtained samples CO2 uptake capability. A quadratic model was developed based on the RSM-BBD method to predict the CO2 adsorption capacity of the composite sample within design space. In addition, CO2 adsorption process optimization was conducted and the optimum values of the GO, ZnO, temperature, and pressure were obtained around 23.8 wt%, 18.2 wt%, 30.1 °C, and 8.6 bar, respectively, with the highest CO2 uptake capacity of 470.43 mg/g. Moreover, isotherm and kinetic modeling of the CO2 uptake process were conducted and the Freundlich model (R2 = 0.99) and fractional order model (R2 = 0.99) were obtained as the most appropriate isotherm and kinetic models, respectively. Also, thermodynamic analysis of the adsorption was done and the ∆H°, ∆S°, and ∆G° values were obtained around - 19.121 kJ/mol, - 0.032 kJ/mol K, and - 9.608 kJ/mol, respectively, indicating exothermic, spontaneously, and physically adsorption of the CO2 molecules on the CTS/GO/ZnO composite's surface. Finally, a renewability study was conducted and a minor loss in the CO2 adsorption efficiency of about 4.35% was obtained after ten cycles, demonstrating the resulting adsorbent has good performance and robustness for industrial CO2 capture purposes.

Hypercrosslinked waste polycarbonate to remove heavy metal contaminants from wastewater.

In this research, the waste polycarbonate was hypercrosslinked during the Friedel-Crafts reaction to eliminate metallic ions from the wastewater solution. The experiments for inspecting the adsorption behavior of lead and cadmium ions were conducted at the initial concentration of 20-100 mg/L, contact time of 10-80 min, temperature of 20-80 °C, and pH of 6-11. The isotherm, kinetic, and thermodynamic models have been used to explain the behavior of the metal ions removal process. The correlation coefficient and adsorption capacity of the kinetic model for cadmium ion have obtained 0.995 and 160.183 mg/g, respectively, and the correlation coefficient and adsorption capacity of the kinetic model for lead ion has obtained 0.998 and 160.53 mg/g, respectively, which declared that the cascade was not monolayer. The correlation coefficient of the Freundlich is calculated at 0.995 and 0.998 for Cd and Pb, respectively, indicating the resin plane was not homogenized. The n constant for cadmium and lead ions has been calculated at 2.060 and 1.836, respectively, confirming that the resin is not homogenized, and the process has performed well. Afterward, the values of enthalpy and Gibbs free energy changes were obtained at - 7.68 kJ/mol and - 0.0231 kJ/mol.K for lead ions, respectively, which implies the exothermic and spontaneous state of the process. The values of enthalpy and Gibbs free energy changes have been obtained at - 6.62 kJ/mol and - 0.0204 kJ/mol.K for cadmium ions, respectively, which implies the exothermic and spontaneous nature of the adsorption. Also, the optimal empirical conditions for lead and cadmium ions have been found at a time of 60 min, temperature of 20 °C, initial concentration of 100 mg/L, and pH of 10. At a time of 45 min, the diffusion coefficient and mass transfer coefficient for lead ions have been calculated at 0.1269 × 1020 m2/s and 0.2028 × 1015 m/s, respectively. In addition, at a time of 45 min, the diffusion coefficient and mass transfer coefficient for cadmium ions have been calculated at 0.1463 × 1020 m2/s and 0.1054 × 1015 m/s, respectively. Moreover, the mechanism study explains that the C-O-C and C-H in the aromatic groups have a crucial aspect in the bond formation among metallic ions and resin.

Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor.

Flue gas desulfurization (FGD) is a critical process for reducing sulfur dioxide (SO2) emissions from industrial sources, particularly power plants. This research uses calcium silicate absorbent in combination with machine learning (ML) to predict SO2 concentration within an FGD process. The collected dataset encompasses four input parameters, specifically relative humidity, absorbent weight, temperature, and time, and incorporates one output parameter, which pertains to the concentration of SO2. Six ML models were developed to estimate the output parameters. Statistical metrics such as the coefficient of determination (R2) and mean squared error (MSE) were employed to identify the most suitable model and assess its fitting effectiveness. The random forest (RF) model emerged as the top-performing model, boasting an R2 of 0.9902 and an MSE of 0.0008. The model's predictions aligned closely with experimental results, confirming its high accuracy. The most suitable hyperparameter values for RF model were found to be 74 for n_estimators, 41 for max_depth, false for bootstrap, sqrt for max_features, 1 for min_samples_leaf, absolute_error for criterion, and 3 for min_samples_split. Three-dimensional surface plots were generated to explore the impact of input variables on SO2 concentration. Global sensitivity analysis (GSA) revealed absorbent weight and time significantly influence SO2 concentration. The integration of ML into FGD modeling offers a novel approach to optimizing the efficiency and effectiveness of this environmentally crucial process.

Optimization of atmospheric leaching parameters for cadmium and zinc recovery from low-grade waste by response surface methodology (RSM).

This study is focused on the optimization of effective parameters on Cadmium and Zinc recovery by atmospheric acid leaching of low-grade waste by response surface methodology (RSM) and using the Central Composite Design (CCD) method. The effects of parameters including time (0.5-2.5 h), temperature (40-80 °C), solid/liquid (S/L) (0.05-0.09 g/cc), particle size (174-44 mic), oxygen injection (0-1%) and pH (0.5-4.5) were statistically investigated at 5 surfaces. The sample of low-grade waste used in this study was mainly zinc factory waste. Two quadratic models for the correlation of independent parameters for the maximum recovery were proposed. The properties of waste were evaluated by X-ray diffraction (XRD) and X-ray fluorescence (XRF). Atomic absorption spectroscopy was used to determine the amount of Cadmium and Zinc in the leaching solution. The correlation coefficient (R2) for the predicted and experimental data of Cadmium and Zinc are 0.9837 and 0.9368, respectively. Time, S/L and size were the most effective parameters for the recovery efficiency of cadmium and zinc. 75.05% of Cadmium and 86.13% of Zinc were recovered in optimal conditions of leaching: S/L 0.08, pH 2.5, size 88 µm, 70 °C and 2.5 h. with air injection.