An effective Application of response surface methodology combined-artificial neural network for bisphenol-A(BPA) treatment using synthesized CNT-MnO2 composite

IF 1 Q4 ENGINEERING, ENVIRONMENTAL Journal of Environmental Engineering and Science Pub Date : 2023-10-10 DOI:10.1680/jenes.23.00033
Md Habeeb Ahmed, Sangeetha Subramanian
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Abstract

Bisphenol-A is one of the emerging pollutants,which easily escapes conventional treatment techniques. It requires application of novel composite materials along with mathematical modelling for optimization and evaluation of treatment process. In present study, manganese oxide nanoparticles (MnO2) were doped on the surface of multi-walled carbon nanotubes (MWCNT) to develop an adsorptive-oxidative composite .Composite was characterized using transmission electronmicroscope,X-raydiffraction,RAMANspectroscopy,X-rayphotoelectron sectroscopy,fourier-transform infrared spectroscopyand Surface area analysistoconfirm composite formation and study its properties. Conventional optimization of PH(4-10)BPA initialconcentration(10-50 mg/L), contact time(0-60mins)was carried out and found to be fitting well with Freundlich isotherm model (R2 > 0.99) and followed pseudo-second-order kinetics reaction. Central composite design (CCD) model was applied using Response surface methodology (RSM) to study individual parameters and their interaction effects in order to enhance the process efficiency. Further, the experimental data sets and their responses from RSM were analyzed using Artificial Neural Network (ANN). (80%) of Random experimental sets of which (10%)each to Train,Validate and Test were selected to analyze the variance of models for higher efficiency using Levenberg-Marquardt Backpropagation (LM-BP) algorithm. Additionally, BPA spiked simulated pharmaceutical wastewater was treated with composite to explore its treatment potential. This systematic experimental and computational approach aids in optimizing the treatment efficiency for real-time application.
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响应面法结合人工神经网络在合成CNT-MnO2复合材料处理双酚a (BPA)中的有效应用
双酚A (BPA)是一种新兴的污染物,很容易逃脱传统的处理技术。它需要应用新型复合材料以及用于优化和评估处理过程的数学模型。在本研究中,将氧化锰(mno2)纳米颗粒掺杂到多壁碳纳米管表面,制备了一种吸附氧化复合材料。采用透射电镜、x射线衍射、拉曼光谱、x射线光电子能谱、傅里叶变换红外光谱和表面积分析等方法对复合材料进行表征,确认复合材料的形成并研究其性质。对pH(4-10)、BPA初始浓度(10-50 mg/l)和接触时间(0-60 min)进行了常规优化,发现与Freundlich等温线模型(r2 >0.99),并遵循伪二级动力学反应。采用响应面法(RSM)建立中心复合设计模型,研究各参数之间的交互作用,提高工艺效率。此外,利用人工神经网络对实验数据集及其对RSM的响应进行了分析。从随机实验集(80%)中选择(10%)进行训练,验证和测试,使用Levenberg-Marquardt反向传播(LM-BP)算法分析模型的方差,以提高效率。此外,还对含bpa的模拟制药废水进行了处理,以探索其处理潜力。这种系统的实验和计算方法有助于优化实时应用的处理效率。
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来源期刊
CiteScore
1.60
自引率
0.00%
发文量
20
审稿时长
12 months
期刊介绍: Journal of Environmental Engineering and Science is an international, peer-reviewed publication providing a forum for the dissemination of environmental research, encouraging interdisciplinary research collaboration to address environmental problems. It addresses all aspects of environmental engineering and applied environmental science, with the exception of noise, radiation and light.
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