CO2 Capture in Chemically and Thermally Modified Activated Carbons Using Breakthrough Measurements: Experimental and Modeling Study

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.

Modeling the CO2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks

Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO2), which is an environmental pollutant with a greenhouse effect. The CO2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO2 permeability in PMP/ZnO, PMP/Al2O3, PMP/TiO2, and PMP/TiO2-NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO2-NT (functionalized nanotube with titanium dioxide) is the best medium for CO2 separation.

Effect of binder on CO2, CH4, and N2 adsorption behavior, structural properties, and diffusion coefficients on extruded zeolite 13X

The effect of inorganic binder-based extrusion (Silica sol, Bentonite, Attapulgite, and SB1) in the selective adsorption of CO₂, CH₄, and N₂ on zeolite 13X in the context of flue gas carbon capture and natural gas purification has been studied to reduce CO₂ emissions. The effect of extrusion with binders was examined by adding 20 wt% of the mentioned binders to pristine zeolite and the results were analyzed by four approaches; (i) the effect on structural properties was analyzed by XRD patterns followed by Williamson-Hall (W-H) plot, FESEM images, and BET surface area. In addition, the mechanical strength of the shaped zeolites was measured by crush resistance testing; (ii) the effect on the adsorption capacity for CO₂, CH₄, and N₂ were measured by volumetric apparatus up to 100 kPa; (iii) the impact on binary separation (CO₂/CH₄ and CO₂/N₂) were investigated; (iv) the influence on diffusion coefficients were estimated by micropore and macropore kinetic model. The results showed that the presence of a binder can cause reductions in BET surface area and pore volume, indicating partial pore blockage. It was found that the Sips model had the best adaptability to the experimental isotherms data. The trend of CO₂ adsorption was 13X > pseudo-boehmite > bentonite > attapulgite > silica, in which the adsorption capacity reached 6.02, 5.60, 5.24, 5.00, and 4.71 mmol/g, respectively. Among all samples, silica was found the most suitable binder for CO₂ capture in terms of selectivity, mechanical stability, and diffusion coefficients.

Carbon dioxide separation and capture by adsorption: a review

Rising adverse impact of climate change caused by anthropogenic activities is calling for advanced methods to reduce carbon dioxide emissions. Here, we review adsorption technologies for carbon dioxide capture with focus on materials, techniques, and processes, additive manufacturing, direct air capture, machine learning, life cycle assessment, commercialization and scale-up.

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 N_CO2 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.

Adsorption capacity of activated carbon derived from date seeds: Characterization, optimization, kinetic and equilibrium studies

Agricultural wastes have the potential to be reused in applications such as water/wastewater treatment. Several studies have focused on activating organic waste, such as date seeds, to produce activated carbon. However, these studies have always assumed that all date seeds behave similarly to each other. In this study, we evaluated different types of date seeds and characterized their physical-chemical properties. The results showed variation in the seed-to-fruit weight percentage, ash content, and moisture content among different seed types. Different activation procedures were performed to find the optimum combination of physical and chemical interventions. KOH impregnation yielded better results than H₃PO₄ impregnation. The maximum adsorption capacity was measured for nine different types of date seeds, and the Khalas seed type yielded the highest methylene blue (MB) adsorption capacity of 165 mg of MB/g of activated date seeds (ADS), which is 71% of the capacity of commercial activated carbon (CAC). Kinetics model was fitted to the experimental data, and the pseudo-second-order model provided the best fit, indicating that the adsorption process occurred following a chemical process rather than being controlled by intraparticle diffusion only. The results showed no significant difference among the three isotherm models used to fit the experimental data. The results indicated that there is a significant difference among various types of seeds regarding adsorption performance. The application of ADS in treating synthetic produced water showed that its performance is one third that of CAC. ADS showed promising potential in comparison with CAC, mostly considering the costs involved with CAC.

Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials

This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between -0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation.

Porous Adsorption Materials for Carbon Dioxide Capture in Industrial Flue Gas

Due to the intensification of the greenhouse effect and the emphasis on the utilization of CO₂ resources, the enrichment and separation of CO₂ have become a current research focus in the environment and energy. Compared with other technologies, pressure swing adsorption has the advantages of low cost and high efficiency and has been widely used. The design and preparation of high-efficiency adsorbents is the core of the pressure swing adsorption technology. Therefore, high-performance porous CO₂ adsorption materials have attracted increasing attention. Porous adsorption materials with high specific surface area, high CO₂ adsorption capacity, low regeneration energy, good cycle performance, and moisture resistance have been focused on. This article summarizes the optimization of CO₂ adsorption by porous adsorption materials and then applies them to the field of CO₂ adsorption. The internal laws between the pore structure, surface chemistry, and CO₂ adsorption performance of porous adsorbent materials are discussed. Further development requirements and research focus on porous adsorbent materials for CO₂ treatment in industrial waste gas are prospected. The structural design of porous carbon adsorption materials is still the current research focus. With the requirements of applications and environmental conditions, the integrity, mechanical strength and water resistance of high-performance materials need to be met.

Integration of Response Surface Methodology (RSM) and Principal Component Analysis (PCA) as an Optimization Tool for Polymer Inclusion Membrane Based-Optodes Designed for Hg(II), Cd(II), and Pb(II)

An optimization of the composition of polymer inclusion membrane (PIM)-based optodes, and their exposure times to metal ion solutions (Hg(II), Cd(II), and Pb(II)) was performed using two different chromophores, diphenylthiocarbazone (dithizone) and 1-(2-pyridylazo)-2-naphthol (PAN). Four factors were evaluated (chromophore (0.06-1 mg), cellulose triacetate (25-100 mg) and plasticizer amounts (25-100 mg), and exposure time (20-80 min)). Derringer's desirability functions values were employed as response variables to perform the optimization obtained from the results of three different processes of spectral data treatment: two full-spectrum methods (M1 and M3) and one band-based method (M2). The three different methods were compared using a heatmap of the coefficients and dendrograms of the Principal Component Analysis (PCA)reductions of their desirability functions. The final recommended M3 processing method, i.e., using the scores values of the first two principal components in PCA after subtraction of the normalized spectra of the membranes before and after complexation, gave more discernable differences between the PIMs in the Design of Experiments (DoE), as the nodes among samples appeared at longer distances and varyingly distributed in the dendrogram analysis. The optimal values were time of 35-65 min, 0.53 mg-1.0 mg of chromophores, plasticizers 34.4-71.9 of chromophores, and 62.5-100 mg of CTA, depending on the metal ion. In addition, the method yielded the best outcomes in terms of interpretability and an easily discernable color change so that it is recommended as a novel optimization method for this kind of PIM optode.

Adsorption of Sudan-IV contained in oily wastewater on lipophilic activated carbons: kinetic and isotherm modelling

Up to nine kinetic and fourteen isotherm adsorption models are employed to model the adsorption of Sudan IV, a lipophilic model pollutant present in a biphasic mixture of cyclohexane-water system to simulate oily wastewater. Six different modified activated carbons were used as adsorbents. The highest amount adsorbed of Sudan IV was found in the material prepared by successive treatments of the parent commercial activated carbon Norit ROX 0.8 with nitric acid and urea, followed by thermal treatment at 800 °C under continuous flow of nitrogen. Kinetic and isotherm adsorption models can be employed to simulate the process, since the effect of the presence of water in the adsorption of Sudan IV from the cyclohexane phase was found to be negligible, owing to the high lipophilic character of both adsorbent and adsorbate. All kinetic and isotherm coefficients, coupling with statistical parameters (r², adjusted r² and sum of squared errors), are determined by non-linear regression fitting and compared to literature data. The model of Avrami is found to be the most appropriate model to represent the adsorption of the pollutant in any of the six modified carbons tested, the highest value of the kinetic constant being 0.055 min^-1. The isotherm adsorption is well-modelled by using the general isotherm equation of Tóth and the multilayer Jovanović expression for the adsorption of Sudan-IV on that material, resulting in a high monolayer uptake capacity (q_m = 193.6 mg g^-1).

Simulation and Optimization of the CWPO Process by Combination of Aspen Plus and 6-Factor Doehlert Matrix: Towards Autothermal Operation

This work aims to present an industrial perspective on Catalytic Wet Peroxide Oxidation (CWPO) technology. Herein, process simulation and experimental design have been coupled to study the optimal process conditions to ensure high-performance oxidation, minimum H2O2 consumption and maximum energetic efficiency in an industrial scale CWPO unit. The CWPO of phenol in the presence of carbon black catalysts was studied as a model process in the Aspen Plus® v11 simulator. The kinetic model implemented, based on 30 kinetic equations with 11 organic compounds and H2O2 involvement, was valid to describe the complex reaction network and to reproduce the experimental results. The computer experiments were designed on a six-factor Doehlert Matrix in order to describe the influence of the operating conditions (i.e., the different process temperatures, inlet chemical oxygen demands, doses of H2O2 and space time) on each selected output response (conversion, efficiency of H2O2 consumption and energetic efficiency) by a quadratic model. The optimization of the WPO performance by a multi-criteria function highlighted the inlet chemical oxygen demand as the most influential operating condition. It needed to have values between 9.5 and 24 g L−1 for autothermal operation to be sustained under mild operating conditions (reaction temperature: 93–130 °C and pressure: 1–4 atm) and with a stoichiometric dose of H2O2.

Experimental Modeling and Optimization of CO2 Absorption into Piperazine Solutions Using RSM-CCD Methodology

The present work evaluates and optimizes CO₂ absorption in a bubble column for the Pz-H₂O-CO₂ system. We analyzed the impact of the different operating conditions on the hydrodynamic and mass-transfer performance. For the optimization of the process, variable conditions were used in the multivariate statistical method of response surface methodology. The central composite design is used to characterize the operating condition to fit the models by the least-squares method. The experimental data were fitted to quadratic equations using multiple regressions and analyzed using analysis of variance (ANOVA). An approved experiment was carried out to analyze the correctness of the optimization method, and a maximum CO₂ removal efficiency of 97.9%, an absorption rate of 3.12 g/min, an N _CO2 of 0.0164 mol/m²·s, and a CO₂ loading of 0.258 mol/mol were obtained under the optimized conditions. Our results suggest that Pz concentration, solution flow rate, CO₂ flow rate, and speed of stirrer were obtained to be 0.162 M, 0.502 l/h, 2.199 l/min, and 68.89 rpm, respectively, based on the optimal conditions. The p-value for all dependent variables was less than 0.05, and that points that all three models were remarkable. In addition, the experiment values acquired for the CO₂ capture were found to agree satisfactorily with the model values (R ² = 0.944-0.999).