采用化学气相沉积法生产多壁碳纳米管(MWCNT),并通过Fe-Ni/AC催化剂对其进行改性以增强CO 2吸附。在这项研究中,通过尖端建模技术和最近合成的 Fe-Ni/AC 催化剂吸附剂的独特结合,揭示了 CO 2捕集技术的可能性和潜在进步的新领域。使用SEM、BET和FTIR分析其结构和形貌。发现MWCNT的表面积为240 m 2 /g,但经过改性后,其表面积降低至11 m 2 /g。由于引入了新的吸附位点和在较低温度下有利的相互作用,改性多壁碳纳米管在较高压力和较低温度下表现出更高的吸附能力。在25℃和10bar下,其最大吸附容量为424.08mg/g。压力、时间和温度参数的最佳值在 7 bar、2646 S 和 313 K 下获得。Freundlich 和 Hill 模型与实验数据的相关性最高。二阶和分数阶动力学模型很好地拟合了吸附结果。发现吸附过程是放热且自发的。改性多壁碳纳米管在气体储存或分离等领域具有高效气体吸附的潜力。研究证明,再生的 M-MWCNT 吸附剂能够在 CO 2吸附过程中多次重复使用。在本研究中,使用反向传播训练方法创建了前馈 MLP 人工神经网络模型来预测 CO 2吸附。 选择用于优化的最合适且最有效的 MLP 网络结构由分别具有 25 个和 10 个神经元的两个隐藏层组成。该网络使用 Levenberg-Marquardt 反向传播算法进行训练。创建了MLP人工神经网络模型,其最小MSE性能为0.0004247,R 2值为0.99904,表明其准确性。该实验还利用响应面法框架内的空白电子表格设计来预测CO 2吸附。预测 R 2值 0.8899 与调整 R 2值 0.9016 之间的接近度(差异小于 0.2)表明相似度很高。这表明该模型预测未来观测的能力异常可靠,凸显了其稳健性。
"点击查看英文标题和摘要"
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.