Kumari Sheetal, Agarwal Smriti, Kumar Manish, Sharma Pinki, Kumar Ajay, Hashem Abeer, Alotaibi Nouf H, Abd-Allah Elsayed Fathi, Garg Manoj Chandra
Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Budh Nagar, Noida, 201313, India.
Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 211004, India.
Sci Rep. 2025 Jan 23;15(1):2979. doi: 10.1038/s41598-025-87274-3.
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of RSM, ANN, and ANFIS included the quantification of R, mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The regression coefficients from the process modelling demonstrated that RSM (R = 0.9216), ANN (R = 0.8864), and ANFIS (R = 0.9589) all accurately predicted MB adsorptive removal. However, comparative statistical analysis revealed that the ANFIS model exhibited superior accuracy in data-based predictions compared to ANN and RSM models. The ideal pH for MB adsorption utilizing OSSB was established as 7. Additionally, favourable outcomes were obtained with 60-minute contact durations, 20 mg adsorbent quantities, and temperatures of 30 °C. The pseudo 2nd -order kinetic model for MB adsorption by OSSB was confirmed. The equilibrium data exhibited a superior fit with the Langmuir isotherm model in comparison to the Freundlich model. The thermodynamic adsorption parameters, including (∆G = -9.1489 kJ/mol), enthalpy change (∆H = -1457.2 kJ/mol), and entropy change (∆S = -19.03 J mol K) indicated that the adsorption of MB onto the OSSB surface is exothermic and spontaneous under the experimental conditions. This research effectively showcased the potential of RSM, ANN, and ANFIS in simulating dye removal using OSSB. The generated parameter data proved valuable for the design and control of the adsorption process.
本研究聚焦于利用水稻秸秆生物质（OSSB）模拟基于吸附的亚甲基蓝（MB）染料分离过程。采用了三种不同的建模方法：人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）和响应面方法（RSM）。为评估吸附剂的潜力，使用傅里叶变换红外光谱（FTIR）和扫描电子显微镜（SEM）进行了评估。对RSM、ANN和ANFIS的评估包括对R、均方误差（MSE）、均方根误差（RMSE）和平均绝对误差（MAE）指标的量化。过程建模的回归系数表明，RSM（R = 0.9216）、ANN（R = 0.8864）和ANFIS（R = 0.9589）均能准确预测MB的吸附去除情况。然而，比较统计分析表明，与ANN和RSM模型相比，ANFIS模型在基于数据的预测中表现出更高的准确性。利用OSSB吸附MB的理想pH值确定为7。此外，在接触时间为60分钟、吸附剂用量为20 mg、温度为30°C时获得了良好的结果。证实了OSSB吸附MB的准二级动力学模型。与Freundlich模型相比，平衡数据与Langmuir等温线模型的拟合效果更佳。热力学吸附参数，包括吉布斯自由能变化（∆G = -9.1489 kJ/mol）、焓变（∆H = -1457.2 kJ/mol）和熵变（∆S = -19.03 J mol K）表明，在实验条件下，MB在OSSB表面的吸附是放热且自发的。本研究有效地展示了RSM、ANN和ANFIS在模拟利用OSSB去除染料方面的潜力。生成的参数数据对吸附过程的设计和控制具有重要价值。
