Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

高等学校化学研究 ›› 2011, Vol. 27 ›› Issue (6): 944-948.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

GAO Qian, WANG Zhi-zeng, WANG Qian, LI Shan-shan and LI Yu*

Research Academy of Energy and Environmental Studies, North China Electric Power University, Beijing 102206, P. R. China

收稿日期:2011-01-24 修回日期:2011-03-08 出版日期:2011-11-25 发布日期:2011-11-07
通讯作者: LI Yu E-mail:liyuxx@jlu.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China(No.50879025).

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

GAO Qian, WANG Zhi-zeng, WANG Qian, LI Shan-shan and LI Yu*

Research Academy of Energy and Environmental Studies, North China Electric Power University, Beijing 102206, P. R. China

Received:2011-01-24 Revised:2011-03-08 Online:2011-11-25 Published:2011-11-07
Contact: LI Yu E-mail:liyuxx@jlu.edu.cn
Supported by:
Supported by the National Natural Science Foundation of China(No.50879025).

摘要/Abstract

摘要： Artificial neural network(ANN) and full factorial design assisted atrazine(AT) multiple regression adsorption model(AT-MRAM) were developed to analyze the adsorption capability of the main components in the surficial sediments(SSs). Artificial neural network was used to build a model(the determination coefficient square r2 is 0.9977) to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance(ANOVA), F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model(MRAM). The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn(–1.993 μmol/mol). Organic materials(OMs) and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components(Fe, Mn oxides and OMs) in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

关键词: Back propagation(BP) artificial neural network, Full factorial design, Fe/Mn oxide, Organic material, Atrazine, Interaction

Abstract: Artificial neural network(ANN) and full factorial design assisted atrazine(AT) multiple regression adsorption model(AT-MRAM) were developed to analyze the adsorption capability of the main components in the surficial sediments(SSs). Artificial neural network was used to build a model(the determination coefficient square r2 is 0.9977) to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance(ANOVA), F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model(MRAM). The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn(–1.993 μmol/mol). Organic materials(OMs) and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components(Fe, Mn oxides and OMs) in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

Key words: Back propagation(BP) artificial neural network, Full factorial design, Fe/Mn oxide, Organic material, Atrazine, Interaction

GAO Qian, WANG Zhi-zeng, WANG Qian, LI Shan-shan and LI Yu*. Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments[J]. 高等学校化学研究, 2011, 27(6): 944-948.

[1]	CHAI Jingshan, LI Qiushi, ZHAO Yu, LIU Yang. Nanocomposites Facilitate the Removal of Aβ Fibrils for Neuroprotection[J]. 高等学校化学研究, 2022, 38(2): 522-528.
[2]	YANG Miao, WANG Wenjing, SU Kongzhao, YUAN Daqiang. Dimeric Calix[4]resorcinarene-based Porous Organic Cages for CO₂/CH₄ Separation[J]. 高等学校化学研究, 2022, 38(2): 428-432.
[3]	WANG Yuwen, CHENG Tiexin, ZHOU Guangdong. Study on the Mechanism of Asphaltenes Reducing Oil-Water Interfacial Tension[J]. 高等学校化学研究, 2022, 38(2): 616-621.
[4]	XU Hao, WANG Yanhong, PENG Rusi, JIANG Jingang, ZHANG Kun, WU Peng. Synthesis of Micro-Mesoporous Ti-MOR/Silica Composite Spheres in Oil-in-Water Microemulsion System[J]. 高等学校化学研究, 2022, 38(1): 192-199.
[5]	QI Qi, XU Lekai, DU Jiang, YANG Nailiang, WANG Dan. Fabrication and Application of Graphdiyne-based Heterogeneous Compositions: from the View of Interaction[J]. 高等学校化学研究, 2021, 37(6): 1158-1175.
[6]	XU Haijiao, WANG Hongda. Conventional Molecular and Novel Structural Mechanistic Insights into Orderly Organelle Interactions[J]. 高等学校化学研究, 2021, 37(4): 829-839.
[7]	LI Yuan, LI Ruizhe, LI Zhenhua, WEI Weiqin, OUYANG Shuxin, YUAN Hong, ZHANG Tierui. Effect of Support on Catalytic Performance of Photothermal Fischer-Tropsch Synthesis to Produce Lower Olefins over Fe₅C₂-based Catalysts[J]. 高等学校化学研究, 2020, 36(6): 1006-1012.
[8]	ZHANG Yuanyuan, HUANG Xiaohua, LI Jiashu, LIN Gang, LIU Wengang, CHEN Zupeng, LIU Jian. Iron-doping Accelerating NADH Oxidation over Carbon Nitride[J]. 高等学校化学研究, 2020, 36(6): 1076-1082.
[9]	QIAN Binbin, CHANG Ze, BU Xian-He. Zinc-coordination Polymers Based on a Donor-acceptor Mix-ligand System: Syntheses, Crystal Structures and Photophysical Properties[J]. 高等学校化学研究, 2020, 36(1): 74-80.
[10]	ZHANG Xing, HUANG Jie, ZHANG Yu, QI Fan, WANG Sifan, SONG Jirong. Synthesis, Crystal Structure and Non-covalent Interactions Analysis of Novel N-Substituted Thiosemicarbazone[J]. 高等学校化学研究, 2019, 35(3): 471-477.
[11]	LIU Qianqian, GUO Yuan, WU Tun, CHEN Mingzhao, XIE Tingzheng, WANG Pingshan. Novel Terpyridine-bridged Parallel Dication Metallo-bisviologens and Their Supramolecular Complexes[J]. 高等学校化学研究, 2019, 35(2): 229-234.
[12]	SONG Yunxi, SONG Zhen, YANG Binsheng. Spectral Study on the Interactions Among Cu(II), Doxorubicin and CopC[J]. 高等学校化学研究, 2019, 35(1): 53-59.
[13]	LI Kaijiao, ZHANG Zhenyu, CUI Haining, WANG Haiyu. Transient Optical Characteristics of Broad Absorption Band Excitons Modulated by Micro-cavity[J]. 高等学校化学研究, 2018, 34(6): 978-982.
[14]	LIU Li, PAN Ge, WANG Licheng, REN Xiuyan, ZHANG Xinyue, WU Guangfeng. Hybrid Hydrogels Toughened by Chemical Covalent Bonding and Physical Electrostatic Interactions[J]. 高等学校化学研究, 2018, 34(3): 500-505.
[15]	YIN Shi, ZHU Lingjun, LIU Yincong, WANG Xiaoliu, LIU Yingying, WANG Shurong. Effect of Ni Precipitation Method on CO Methanation over Ni/TiO₂ Catalysts[J]. 高等学校化学研究, 2018, 34(2): 296-301.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价