活性碳纤维ACF吸附重金属Cr(Ⅵ)及神经网络智能优化研究
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摘要
本文以活性碳纤维(ACF)为吸附剂,以其对重金属污染物Cr(Ⅵ)的吸附率为评价指标进行了正交实验及BP神经网络仿真预测研究。采用比表面积、扫描电子显微镜(SEM)及傅里叶变换红外光谱(FTIR)对ACF的结构进行了表征。结果表明,当ACF添加量为0.15 g、Cr(Ⅵ)初始浓度为10 mg/L、吸附体系pH值为2、吸附温度为25℃时,ACF对Cr(Ⅵ)的吸附率最高可达95.12%。构建的BP神经网络具有较好的训练精度及泛化能力,网络预测值与实验目标值相关系数达到0.9746,可用于ACF吸附含Cr(Ⅵ)废水的智能处理研究。
In this paper,the activated carbon fiber(ACF)was used as adsorbent to adsorb heavy metal ion pollutant of Cr(Ⅵ),and the Cr(Ⅵ)adsorption rate of ACF was studied according to the orthogonal experiments and BP neural network. Specific surface area,scanning electron microscope(SEM) and Fourier transform infrared spectroscopy(FTIR)were employed to characterize the structure of ACF. Theresults showed that the adsorption rate of ACF to Cr(Ⅵ)could reach 95.12% under the condition of theACF utilization amount of 0.15 g,the Cr(Ⅵ)initial concentration of 10 mg/L,the adsorption system pH value of 2 and the adsorption temperature of 25 ℃. In addition,the BP neural network has good training accuracy and generalization ability. The correlation coefficient between network prediction value andexperimental target value reaches 0.9746,which is conducive to the intelligent treatment of wastewater containing Cr(Ⅵ)by using ACF.
In this paper,the activated carbon fiber(ACF)was used as adsorbent to adsorb heavy metal ion pollutant of Cr(Ⅵ),and the Cr(Ⅵ)adsorption rate of ACF was studied according to the orthogonal experiments and BP neural network. Specific surface area,scanning electron microscope(SEM) and Fourier transform infrared spectroscopy(FTIR)were employed to characterize the structure of ACF. Theresults showed that the adsorption rate of ACF to Cr(Ⅵ)could reach 95.12% under the condition of theACF utilization amount of 0.15 g,the Cr(Ⅵ)initial concentration of 10 mg/L,the adsorption system pH value of 2 and the adsorption temperature of 25 ℃. In addition,the BP neural network has good training accuracy and generalization ability. The correlation coefficient between network prediction value andexperimental target value reaches 0.9746,which is conducive to the intelligent treatment of wastewater containing Cr(Ⅵ)by using ACF.
引文
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[2]智研咨询集团.2017-2022年中国污水处理市场深度分析与发展前景研究报告[Z]. 2017.
[3]颜景顺.电镀废水处理技术与工艺在工程中的应用与研究[D].杭州:浙江大学,2014.
[4]国务院.水污染防治行动计划[Z]. 2015.
[5]刘新,冷言冰,谷仕艳,等.油菜秸杆外壳对水溶液中六价铬的吸附作用[J].中国环境科学,2015,35(06):1740-1748.
[6]李航彬,钱波,黄聪聪,等.钡盐沉淀法处理六价铬电镀废水[J].电镀与涂饰,2014,33(9):391-395.
[7]王淑娟,吕明威,王子侃,等.各类化学沉淀剂在电镀废水处理中的应用[J].广州化工,2015,43(6):42-44.
[8]张乾,迟占秋,段丽丽,等.活性炭吸附处理电镀废水的研究[J].电镀与环保,2018,38(2):55-58.
[9]龚正君,周文波,陈钰.活性炭纤维对水中酸性染料的吸附研究[J].工业水处理,2012,9:24-28.
[10]Yang Y,Yu C L,Guan C L. Study of the photocatalyticdegradation of toluene over CdS-TiO2nanoparticles sup-ported on multiwalled carbon nanotubes by back propaga-tion neural network[J]. Fullerenes,Nanotubes and Car-bon Nanostructures,2018,26(5):246-254.
[11]关成立,杨岳,陈兴汉.基于BP神经网络的线路板废水处理研究[J].计算机技术与发展,2015,25(8):194-198.
[12]杨岳,关成立,陈兴汉.基于BP神经网络的多壁碳纳米管复合CdS-TiO2光催化剂优化合成研究[J].环境污染与防治,2014,36(7):64-68.
[13]李煌,龚建勋,肖逸锋,等.含TiB2相Fe-Cr-Ti-B堆焊合金的显微组织及耐磨性[J].热加工工艺,2009,38(9):1-3,7.
[14]Zawadzki J,Wisniewski M. In situ characterization of in-teraction of ammonia with carbon surface in oxygen atmo-sphere[J]. Carbon,2003,41(12):2257–2267.
[15]Nakorn W,Shin H. Effect of oxidation pre-treatment at220 to 270℃on the carbonization and activation behav-ior of phenolic resin fiber[J]. Carbon,2003,41(5):933-944.
[16]Zhang S J,Yu H Q. PVA-based activated carbon fiberswith lotus root-like axially porous structure[J]. Carbon,2006,44(10):2059-2068.