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

The potential of Ti3C2Tx-KH2PO4 and Ti3C2Tx-chitosan in the efficient removal of cesium from nuclear wastewater

Ti3C2Tx is synthesized from Ti3AlC2 by two common methods, HF and HF in situ. The synthesis approach is very practical regarding the structure, morphology, space between layers, type and number of surface-active sites and its specific surface. XRD, SEM, EDS, FTIR and BET analyzes were used to investigate the structure, morphology, type and number of surface-active sites. Under the operating conditions of cesium initial concentration ~ 150 ppm, ambient temperature, pH ~ 7.00 and time of 60 min, the cesium adsorption intensity with Ti3C2Tx-HF and Ti3C2Tx-HF in situ was obtained as 194 and 219.5 mg.g- 1, respectively. The structural results of modification with KH2PO4 show that in this modification, in addition to the increase of hydroxyl functional groups, the distance between the layers has also increased and the cesium adsorption intensity has increased to 338.75 mg.g- 1 under the above operating conditions. Meanwhile, in modification with Chitosan by increasing the frequency of functional groups and specific surface up to 3 times the effective specific surface of Ti3C2Tx-HF in situ, no significant change in the cesium adsorption intensity has been observed (247.5 mg.g- 1). Experiments were conducted to evaluate the effect of parameters of initial concentration of Cs+ (250 - 50 ppm), time (30-60 min), ambient temperature (298.15-318.15 K), solution pH (3.0-11.0) with the help of RSM design. RSM results show that the pH parameter is one of the most important parameters affecting the cesium adsorption intensity with Ti3C2(OH)x and Ti3C2(OH)x-KH2PO4. Also, with the increase in temperature, the adsorption intensity should increase. It is shown in the isotherm modeling that R2 matches well with the Freundlich isotherm model, which is based on the layered structure of Ti3C2(OH)x and Ti3C2(OH)x-KH2PO4 and the presence of active sites with different energy levels, which leads to heterogeneous adsorption. The consistency will be of the adsorbent selectivity investigation shows that the Cs+ hydration radius plays a decisive role in its high adsorption potential. Reduction of MXene by 0.1 M HCl was carried out in 3 steps. The experimental results show that 15% absorption has been achieved in the third stage. The results of structural analysis show that its structure has not changed and the reduction of active sites as a result of washing with acid has led to a decrease in adsorption.

Modeling of carbon dioxide absorption into aqueous alkanolamines using machine learning and response surface methodology

This research focuses on modeling CO2 absorption into alkanolamine solvents using multilayer perceptron (MLP), radial basis function network (RBF), Support Vector Machine (SVM), networks, and response surface methodology (RSM). The parameters, including solvent density, mass fraction, temperature, liquid phase equilibrium constant, CO2 loading, and partial pressure of CO2, were used as input factors in the models. In addition, the value of CO2 mass flux was considered as output in the models. Trainlm, trainbr, and trainscg algorithms trained the networks. The results showed that the best number of neurons for MLP with one layer is 16; with two layers, 5 neurons in the first layer and 12 neurons in the second layer; and with three layers, 9 neurons in the first layer, 5 neurons in the second layer, and 1 neuron in the third layer. The best spread in RBF was found to be 2.202 for optimal network performance. Furthermore, statistical data analysis revealed that the trainlm function performs best. The coefficients of determination for RSM, MLP, RBF, and SVM for optimized structures are obtained at 0.9802, 0.9996, 0.9940, and 0.8946, respectively. The results demonstrate that MLP and RBF networks can model CO2 absorption using the trainlm, trainbr, and trainscg algorithms.

Synthesis of nanogeopolymer adsorbent and its application and reusability in the removal of methylene blue from wastewater using response surface methodology (RSM)

Organic dyestuff are mostly toxic compounds that pose serious dangers to the environment. Adsorption using low-cost adsorbents is the most favorable method for its economic aspects. Recently, geopolymers have been introduced as an effective adsorbent for dyes and heavy metals. In this investigation, the synthesis of geopolymers from fired brick waste (Homra) was studied with full characterization using X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Brunauer-Emmett-Teller, Energy dispersive X-ray, Scanning electron microscope tests and Transmission electron microscopy. The synthesized nano-Homra geopolymer (NHGP) was then subjected to the removal of one of the most used basic dyes, Methylene Blue (MB). Adsorption optimization was applied using Response surface methodology to study dye adsorption by the synthesized nano-geopolymer. The independent variables studied were: temperature, contact time, and concentration of dye in the elimination process, which were varied in the range of (25-60 ℃), (10-180 min), and (20-300 mg/L) respectively. The results obtained from ANOVA indicated that the maximum removal efficiency of 95% and adsorption capacity of 80.65 mg/g at a temperature of 59 ℃, contact time of 163 min, and an initial concentration of 254 mg/L. The results showed that the data obtained from the adsorption of MB onto NHGP was compatible with the Pseudo second order (R2 = 0.9838) and Langmuir isotherm model (R2 = 0.9882).

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.