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.

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.

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.

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.

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.

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.

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.

Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms.

The adsorption of carbon dioxide (CO2) on porous carbon materials offers a promising avenue for cost-effective CO2 emissions mitigation. This study investigates the impact of textural properties, particularly micropores, on CO2 adsorption capacity. Multilayer perceptron (MLP) neural networks were employed and trained with various algorithms to simulate CO2 adsorption. Study findings reveal that the Levenberg-Marquardt (LM) algorithm excels with a remarkable mean squared error (MSE) of 2.6293E-5, indicating its superior accuracy. Efficiency analysis demonstrates that the scaled conjugate gradient (SCG) algorithm boasts the shortest runtime, while the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm requires the longest. The LM algorithm also converges with the fewest epochs, highlighting its efficiency. Furthermore, optimization identifies an optimal radial basis function (RBF) network configuration with nine neurons in the hidden layer and an MSE of 9.840E-5. Evaluation with new data points shows that the MLP network using the LM and bayesian regularization (BR) algorithms achieves the highest accuracy. This research underscores the potential of MLP deep neural networks with the LM and BR training algorithms for process simulation and provides insights into the pressure-dependent behavior of CO2 adsorption. These findings contribute to our understanding of CO2 adsorption processes and offer valuable insights for predicting gas adsorption behavior, especially in scenarios where micropores dominate at lower pressures and mesopores at higher pressures.

Enhanced CO2 capture potential of UiO-66-NH2 synthesized by sonochemical method: experimental findings and performance evaluation.

The excessive release of greenhouse gases, especially carbon dioxide (CO2) pollution, has resulted in significant environmental problems all over the world. CO2 capture technologies offer a very effective means of combating global warming, climate change, and promoting sustainable economic growth. In this work, UiO-66-NH2 was synthesized by the novel sonochemical method in only one hour. This material was characterized through PXRD, FT-IR, FE-SEM, EDX, BET, and TGA methods. The CO2 capture potential of the presented material was investigated through the analysis of gas isotherms under varying pressure conditions, encompassing both low and high-pressure regions. Remarkably, this adsorbent manifested a notable augmentation in CO2 adsorption capacity (3.2 mmol/g), achieving an approximate enhancement of 0.9 mmol/g, when compared to conventional solvothermal techniques (2.3 mmol/g) at 25 °C and 1 bar. To accurately represent the experimental findings, three isotherm, and kinetic models were used to fit the experimental data in which the Langmuir model and the Elovich model exhibited the best fit with R2 values of 0.999 and 0.981, respectively. Isosteric heat evaluation showed values higher than 80 kJ/mol which indicates chemisorption between the adsorbent surface and the adsorbate. Furthermore, the selectivity of the adsorbent was examined using the Ideal Adsorbed Solution Theory (IAST), which showed a high value of 202 towards CO2 adsorption under simulated flue gas conditions. To evaluate the durability and performance of the material over consecutive adsorption-desorption processes, cyclic tests were conducted. Interestingly, these tests demonstrated only 0.6 mmol/g capacity decrease for sonochemical UiO-66-NH2 throughout 8 consecutive cycles.

Improved structure of Zr-BTC metal organic framework using NH2 to enhance CO2 adsorption performance.

Modified mesoporous NH2-Zr-BTC mixed ligand MOF nanocomposites were synthesized via the hydrothermal method as a novel adsorbent for CO2 capture. The newly modified MOF-808 with NH2 demonstrated a similar mesoporous morphology as MOF-808, whereas the specific surface area, pore volume, and average particle size, respectively, increased by 15%, 6%, and 46% compared to those of MOF-808. The characterization analyses exhibited the formation of more active groups on the adsorbent surface after modification. In addition, a laboratory adsorption setup was used to evaluate the effect of temperature, pressure, and NH2 content on the CO2 adsorption capacity in the range of 25-65 °C, 1-9 bar, and 0-20 wt%, respectively. An increase in pressure and a decrease in temperature enhanced the adsorption capacity. The highest equilibrium adsorption capacity of 369.11 mg/g was achieved at 25 °C, 9 bar, and 20 wt% NH2. By adding 20 wt% NH2, the maximum adsorption capacity calculated by the Langmuir model increased by about 4% compared to that of pure MOF-808. Moreover, Ritchie second-order and Sips models were the best-fitted models to predict the kinetics and isotherm data of CO2 adsorption capacity with the high correlation coefficient (R2 > 0.99) and AARE% of less than 0.1. The ΔH°, ΔS°, and ΔG° values were - 17.360 kJ/mol, - 0.028 kJ/mol K, and - 8.975 kJ/mol, respectively, demonstrating a spontaneous, exothermic, and physical adsorption process. Furthermore, the capacity of MH-20% sample decreased from 279.05 to 257.56 mg/g after 15 cycles, verifying excellent stability of the prepared mix-ligand MOF sorbent.

Improving the efficiency of 4A-zeolite synthesized from kaolin by amine functionalization for CO2 capture.

This study focuses on optimizing the CO2 adsorption capacity of 4A-zeolite synthesized from kaolin by employing structural modifications through impregnation with tetraethylenepentamine (TEPA) and diethanolamine (DEA). Various analytical techniques were utilized to evaluate the effectiveness of these modifications. Design expert software and response surface methodology (RSM) was employed for data analysis and operational variable optimization, leading to improved CO2 adsorption performance of the modified zeolites. The adsorption capacity of the modified zeolites was assessed under different temperatures, pressures, and amine concentrations using a test device. The optimal adsorption capacity of 4A-DEA adsorbent is found to be 579.468 mg/g, with the optimal operational variables including a temperature of 25.270 °C, pressure of 8.870 bar, and amine concentration of 11.112 wt%. The analysis shows that the adsorption process involves both physisorption and chemisorption, and the best kinetic model is the fractional-factor model.

Improving adsorption performance of L-ascorbic acid from aqueous solution using magnetic rice husk as an adsorbent: experimental and RSM modeling.

In this research, rice husk (RH) was utilized to prepare a magnetic adsorbent for adsorption of ascorbic acid (AA). The magnetic agent is iron(III) chloride (FeCl3). The impact of acid concentration in the range of 400-800 ppm, adsorbent dosage in the range of 0.5-1 g, and contact time in the range of 10-130 min were studied. The Langmuir model had the highest R2 of 0.9982, 0.9996, and 0.9985 at the temperature of 15, 25, and 35 °C, respectively, and the qmax values in these temperatures have been calculated at 19.157, 31.34, and 38.75 mg/g, respectively. The pseudo-second-order kinetic model had the best agreement with the experimental results. In this kinetic model, the values of q have been measured at 36.496, 45.248, and 49.019 mg/g at the acid concentration of 418, 600, and 718 ppm, respectively. The values of ΔHo and ΔSo were measured 31.972 kJ/mol and 120.253 kJ/mol K, respectively, which proves the endothermic and irregularity nature of the adsorption of AA. Besides, the optimum conditions of the design-expert software have been obtained 486.929 ppm of acid concentration, 0.875 g of the adsorbent dosage, and 105.397 min of the contact time, and the adsorption efficiency in these conditions was determined at 92.94%. The surface area of the RH and modified RH was determined of 98.17 and 120.23 m2/g, respectively, which confirms the high surface area of these two adsorbents.

Amine functionalized benzene based hypercrosslinked polymer as an adsorbent for CO2/N2 adsorption.

In this work, benzene based hypercrosslinked polymer (HCP) as an adsorbent was modified using amine group to enhance CO2 uptake capability and selectivity. Based on BET analysis result, the HCP and the modified HCP provide surface area of 806 (m2 g-1) and micropore volume of 453 (m2 g-1) and 0.19 (cm3 g-1) and 0.14 (cm3 g-1), respectively. The CO2 and N2 gases adsorption were performed in a laboratory scale reactor at a temperature between 298 and 328 K and pressure up to 9 bar. The experimental data were evaluated using isotherm, kinetic and thermodynamic models to identify the absorbent behavior. The maximum CO2 adsorption capacity at 298 K and 9 bar was obtained 301.67 (mg g-1) for HCP and 414.41 (mg g-1) for amine modified HCP. The CO2 adsorption thermodynamic parameters assessment including enthalpy changes, entropy changes, and Gibbs free energy changes at 298 K were resulted - 14.852 (kJ mol-1), - 0.024 (kJ mol-1 K-1), - 7.597 (kJ mol-1) for HCP and - 17.498 (kJ mol-1), - 0.029(kJ mol-1 K-1), - 8.9 (kJ mol-1) for amine functionalized HCP, respectively. Finally, the selectivity of the samples were calculated at a CO2/N2 composition of 15:85 (v/v) and 43% enhancement in adsorption selectivity at 298 K was obtained for amine modified HCP.

Lithium hydroxide as a high capacity adsorbent for CO2 capture: experimental, modeling and DFT simulation.

In this work, the potential of monohydrate Lithium hydroxide (LiOH) as a high capacity adsorbent for CO2 capture was investigated experimentally and theoretically. The effects of operating parameters, including temperature, pressure, LiOH particle size and LiOH loading, on the CO2 capture in a fixed-bed reactor have been experimentally explored using response surface methodology (RSM) based on central composite design. The optimum conditions obtained by the RSM for temperature, pressure, mesh and maximum adsorption capacity were calculated as 333 K, 4.72 bar, 200 micron and 559.39 mg/g, respectively. The experiments were evaluated using isotherm, kinetic and thermodynamic modeling. Isotherm modeling showed that Hill model could deliver a perfect fit to the experimental data, based on the closeness of the R2-value to unity. The kinetics models showed that the process was chemical adsorption and obeyed the second order model. In addition, thermodynamic analysis results showed that the CO2 adsorption was spontaneous and exothermic in nature. In addition, based on the density functional theory, we investigated the chemical stability of LiOH atomic clusters and examined the effects of LiOH nanonization on the physical attraction of carbon dioxide.

Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN.

In this research, artificial neural networks (ANN) and response surface methodology (RSM) were applied for modeling and optimization of carbon dioxide (CO2) absorption using KOH-Pz-CO2 system. In the RSM approach, the central composite design (CCD) describes the performance condition in accordance with the model using the least-squares technique. The experimental data was placed in second-order equations applying multivariate regressions and appraised applying analysis of variance (ANOVA). The p-value for all dependent variables was obtained to be less than 0.0001, indicating that all models were significant. Furthermore, the experimental values obtained for the mass transfer flux satisfactorily matched the model values. The R2 and Adj-R2 models are 0.9822 and 0.9795, respectively, which, it means that 98.22% of the variations for the NCO2 is explained by the independent variables. Since the RSM does not create any details about the quality of the solution acquired, the ANN method was applied as the global substitute model in optimization problems. The ANNs are versatile utensils that can be utilized to model and anticipate different non-linear and involved processes. This article addresses the validation and improvement of an ANN model and describes the most frequently applied experimental plans, about their restrictions and generic usages. Under different process conditions, the developed ANN weight matrix could successfully forecast the behavior of the CO2 absorption process. In addition, this study provides methods to specify the accuracy and importance of model fitting for both methodologies explained herein. The MSE values for the best integrated MLP and RBF models for the mass transfer flux were 0.00019 and 0.00048 in 100 epochs, respectively.

Machine learning analysis and prediction of N2, N2O, and O2 adsorption on activated carbon and carbon molecular sieve.

This research focuses on predicting the adsorbed amount of N2, O2, and N2O on carbon molecular sieve and activated carbon using the artificial neural network (ANN) approach. Experimental isotherm data (data set 1242) on adsorbent type, gas type, temperature, and pressure of the process adsorption were used as input datasets for network investigation utilizing the Sips and dual-site Langmuir isotherm models. The network's output has been used to assess the quantity of gas adsorbed. The Gaussian algorithm was applied as a single 98-neuron hidden layer from a radial based functions (RBF) approach, and the Bayesian regularization (BR) algorithm was used as a two-layer network deep learning from a multi-layer perceptron (MLP) approach utilizing 20 neurons. The MLP and RBF networks would have the best mean square error (MSE) after 98 and 100 epochs, respectively, validating efficiencies of 0.00008 and 0.00033, while the square of the coefficient of correlations (R2) was 0.9996 and 0.9993, respectively. The ANN weight matrix generated can accurately predict the adsorption process behavior of different carbon-based adsorbents under various process conditions for air separation and N2O adsorption. The results of this study have the potential to assist a wide range of process industries.

Intelligent prediction models based on machine learning for CO2 capture performance by graphene oxide-based adsorbents.

Designing a model to connect CO2 adsorption data with various adsorbents based on graphene oxide (GO) which is produced from various forms of solid biomass, can be a promising method to develop novel and efficient adsorbents for CO2 adsorption application. In this work, the information of several GO-based solid sorbents were extracted from 17 articles aimed to develop a machine learning based model for CO2 adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO2 uptake capacity was defined as model response, alsoseven different models, including support vector machine, gradient boosting, random forest, artificial neural network (ANN) based on multilayer perceptron (MLP) and radial basis function (RBF), Extra trees regressor and extreme gradient boosting, were employed to estimate the CO2 adsorption capacity. The best performance was obtained for ANN based on MLP method (R2 > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, ß1 = 0.9, ß2 = 0.999, epochs = 1971, and batch size = 32. To investigate CO2 uptake dependency on mentioned effective parameters, three dimensional diagrams were reported based on MLP network, also the MLP network characteristics including weight and bias matrices were reported for further application of CO2 adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO2 removal efficiency as the target based on adsorbent properties to pick more efficient adsorbents without increasing processing time. Current work employed statistical analysis and machine learning to support the logical design of porous GO for CO2 separation, aiding in screening adsorbents for cleaner manufacturing.

Surveying the elimination of hazardous heavy metal from the multi-component systems using various sorbents: a review.

In this review, several adsorbents were studied for the elimination of heavy metal ions from multi-component wastewaters. These utilized sorbents are mineral materials, microbes, waste materials, and polymers. It was attempted to probe the structure and chemistry characteristics such as surface morphology, main functional groups, participated elements, surface area, and the adsorbent charges by SEM, FTIR, EDX, and BET tests. The uptake efficiency for metal ions, reusability studies, isotherm models, and kinetic relations for recognizing the adsorbent potentials. Besides, the influential factors such as acidity, initial concentration, time, and heat degree were investigated for selecting the optimum operating conditions in each of the adsorbents. According to the results, polymers especially chitosan, have displayed a higher adsorption capacity relative to the other common adsorbents owing to the excellent surface area and more functional groups such as amine, hydroxyl, and carboxyl species. The high surface area generates the possible active sites for trapping the particles, and the more effective functional groups can complex more metal ions from the polluted water. Also, it was observed that the uptake capacity of each metal ion in the multi-component solutions was different because the ionic radii of each metal ion were different, which influence the competition of metal ions for filling the active sites. Finally, the reusability of the polymers was suitable, because they can use several cycles which proves the economic aspect of the polymers as the adsorbent.

Adsorption Equilibrium, Thermodynamic, and Kinetic Study of O2/N2/CO2 on Functionalized Granular Activated Carbon.

A volumetric system was used to assess carbon-based adsorbents for evaluation of the gas separation, equilibrium, and kinetics of oxygen (O2), nitrogen (N2), and carbon dioxide (CO2) adsorption on granular activated carbon (GAC) and functionalized GAC at 298, 308, and 318 K under pressures up to 10 bar. The effects of ZnCl2, pH, arrangement of the pores, and heat-treatment temperature on the adsorptive capabilities of O2, N2, and CO2 were evaluated. High-performance O2 adsorption resulted with a fine sample (GAC-10-500) generated with a 0.1 wt % loading of ZnCl2. The optimal sample structure and morphology were characterized by field-emission scanning electron microscopy, Fourier transform infrared spectroscopy, and powder X-ray diffraction. On the basis of the adsorption-desorption results, the fine GAC provides a surface area of 719 m2/g. Moreover, it possessed an average pore diameter of 1.69 nm and a micropore volume of 0.27 m3/g. At 298 K, the adsorption capacity of the GAC-10-500 adsorbent improved by 19.75% for O2 but was not significantly increased for N2 and CO2. Isotherm and kinetic adsorption models were applied to select the model best matching the studied O2, N2, and CO2 gas uptake on GAC-10-500 adsorbent. At 298 K and 10 bar, the sip isotherm model with the highest potential adsorption difference sequence and gas adsorption difference compared with pure GAC adsorbent as O2 > N2 > CO2 follows well for GAC-10-500. Eventually, the optimal sample is more effective for O2 adsorption than other gases.

The role of surface chemistry on CO2 adsorption in biomass-derived porous carbons by experimental results and molecular dynamics simulations.

Biomass-derived porous carbons have been considered one of the most effective adsorbents for CO2 capture, due to their porous structure and high specific surface area. In this study, we successfully synthesized porous carbon from celery biomass and examined the effect of external adsorption parameters including time, temperature, and pressure on CO2 uptake in experimental and molecular dynamics (MD) simulations. Furthermore, the influence of carbon's surface chemistry (carboxyl and hydroxyl functionalities) and nitrogen type on CO2 capture were investigated utilizing MD simulations. The results showed that pyridinic nitrogen has a greater tendency to adsorb CO2 than graphitic. It was found that the simultaneous presence of these two types of nitrogen has a greater effect on the CO2 sorption than the individual presence of each in the structure. It was also revealed that the addition of carboxyl groups (O=C-OH) to the carbon matrix enhances CO2 capture by about 10%. Additionally, by increasing the simulation time and the size of the simulation box, the average absolute relative error for simulation results of optimal structure declined to 16%, which is an acceptable value and makes the simulation process reliable to predict adsorption capacity under various conditions.