Natural Products Derived Porous Carbons for CO2 Capture

As it is now established that global warming and climate change are a reality, international investments are pouring in and rightfully so for climate change mitigation. Carbon capture and separation (CCS) is therefore gaining paramount importance as it is considered one of the powerful solutions for global warming. Sorption on porous materials is a promising alternative to traditional carbon dioxide (CO2 ) capture technologies. Owing to their sustainable availability, economic viability, and important recyclability, natural products-derived porous carbons have emerged as favorable and competitive materials for CO2 sorption. Furthermore, the fabrication of high-quality value-added functional porous carbon-based materials using renewable precursors and waste materials is an environmentally friendly approach. This review provides crucial insights and analyses to enhance the understanding of the application of porous carbons in CO2 capture. Various methods for the synthesis of porous carbon, their structural characterization, and parameters that influence their sorption properties are discussed. The review also delves into the utilization of molecular dynamics (MD), Monte Carlo (MC), density functional theory (DFT), and machine learning techniques for simulating adsorption and validating experimental results. Lastly, the review provides future outlook and research directions for progressing the use of natural products-derived porous carbons for CO2 capture.

Durable superhydrophobic/superoleophilic melamine foam based on biomass-derived porous carbon and multi-walled carbon nanotube for oil/water separation

In the present study, fabrications of two eco-friendly superhydrophobic/superoleophilic recyclable foamy-based adsorbents for oil/water mixture separation were developed. Hierarchically biomass (celery)-derived porous carbon (PC) and multi-walled carbon nanotube (MWCNT) were firstly synthesized and loaded on pristine melamine foam (MF) by the simple dip-coating approach by combining silicone adhesive to create superhydrophobic/superoleophilic, recyclable, and reusable three-dimensional porous structure. The prepared samples have a large specific surface area of 240 m²/g (MWCNT), 1126 m²/g (PC), and good micro-mesoporous frameworks. The water contact angle (WCA) values of the as-prepared foams, PC/MF and MWCNT/MF, not only were 159.34° ± 1.9° and 156.42° ± 1.6°, respectively but also had oil contact angle (OCA) of equal to 0° for a wide range of oils and organic solvents. Therefore, PC/MF and MWCNT/MF exhibited superhydrophobicity and superoleophilicity properties, which can be considered effective adsorbents in oil/water mixture separations. In this context, superhydrophobic/superoleophilic prepared foams for kind of different oils and organic solvents were shown to have superior separation performance ranges of 54-143 g/g and 46-137 g/g for PC/MF and MWCNT/MF, respectively, suggesting a new effective porous material for separating oil spills. Also, outstanding recyclability and reusability of these structures in the ten adsorption-squeezing cycles indicated that the WCA and sorption capacity has not appreciably changed after soaking into acidic (pH = 2) and alkaline (pH = 12) as well as saline (3.5% NaCl) solutions. More importantly, the reusability and chemical durability of the superhydrophobic samples made them good opportunities for use in different harsh conditions for oil-spill cleanup.

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.

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.

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.

Oxygen and nitrogen enriched pectin-derived micro-meso porous carbon for CO2 uptake

Oxygen and nitrogen enriched micro–meso porous carbon powders have been prepared from pectin and melamine as oxygen and nitrogen containing organic precursors, respectively.

Biomass derived hierarchical porous carbon for high-performance O2/N2 adsorption; a new green self-activation approach

Biomass-derived porous carbons are the most common adsorbent materials for O₂/N₂ adsorption because of their excellent textural properties, high surface area, and low expense. A new synthesis method based on a self-activation technique was developed for a new green porous carbon adsorbent. This ecofriendly system was used for the synthesis of hierarchical porous carbons from walnut-shell precursors. The sorbent was successfully synthesized by facile one-step carbonization, with the activating reagents being gases released during the activation. The sample morphology and structure were characterized by field emission scanning electron microscopy, high-resolution transmission electron microscopy, Raman, Fourier transform infrared spectra, X-ray photoelectron spectroscopy, X-ray powder diffraction, thermogravimetric, and differential thermal analysis. The optimal porous carbons were synthesized at 1000 °C, providing a surface area as high as 2042.4 (m² g⁻¹) and micropore volume of about 0.499 (m³ g⁻¹). At 298 °K under 9.5 bar pressure, the potential for O₂/N₂ separation using porous carbon samples was studied, and the sips isotherms with the highest adsorption potential were determined to be 2.94 (mmol g⁻¹) and 2.67 (mmol g⁻¹), respectively. The sample exhibited stable O₂/N₂ separation over ten cycles, showing high reusability for air separation. Finally, the technology described presents a promising strategy for producing eco-friendly porous carbon from a variety of biomass on an industrial scale.

Green porous carbon was synthesized by self-activation methodology with facile one-step carbonization from a walnut-shell precursor for air separation. The adsorption process behavior was surveyed using isotherm, kinetic and thermodynamic models.

A comparative study of radiation models on propane jet fires based on experimental and computational studies

Radiation as a consequence of jet fires is one of the significant parameters in process industry events. In the present work, the open field vertical propane jet fire was studied via experimental and computational fluid dynamics (CFD). The predicted values of radiation were verified at three locations in the horizontal direction from the jet fire. In the simulation section, four radiation models of Monte Carlo (MC), P-1, Discrete Transfer (DT), and Rosseland were applied to find the fine model for simulating the jet fire. Shear Stress Transport (SST) and Eddy Dissipation Concept (EDC) models are employed for combustion and turbulence, respectively. The estimated data by the simulation demonstrated that the MC radiation is better than the other models with an average error of 5% for predicted incident radiation from the jet flame axis. Also, the P-1 radiation model had an above 65% error at around the jet fire, but due to the error of less than 15% estimated by MC and DT models, these radiation models could simulate the jet flame radiation. The simulation outcomes proved that the Rosseland radiation model is not applicable owing to a lack of accurate temperature prediction.

Corrigendum to "Analysis of deep learning neural network combined with experiments to develop predictive models for a propane vertical jet fire" [Heliyon 6 (11) (November 2020) Article e05511]

[This corrects the article DOI: 10.1016/j.heliyon.2020.e05511.].

Analysis of deep learning neural network combined with experiments to develop predictive models for a propane vertical jet fire

Fires are important responsible factors to cause catastrophic events in the process industries, whose consequences usually initiate domino effects. The artificial neural network has been shown to be one of the rapid methods to simulate processes in the risk analysis field. In the present work, experimental data points on jet fire shape ratios, defined by the 800 K isotherm, have been applied for ANN development. The mass flow rates and the nozzle diameters of these jet flames have been considered as input dataset; while, the jet flame lengths and widths have been collected as output dataset by the ANN models. A Bayesian Regularization algorithm has been chosen as the three-layer backpropagation training from Multi-layer perceptron algorithm. Then it has been compared with a Radial based functions algorithm, based on single hidden layer. The optimized number of neurons in the first and second hidden layers of the MLP algorithm, and in the single hidden layer of the RBF algorithm has been found to be twenty and fifteen, respectively. The best MSE validation performance of MLP and RBF networks has been found to be 0.00286 and 0.00426 at 100 and 20 epochs, respectively.