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

作者：Gao Qian; Wang Zhi zeng; Wang Qian; Li Shan shan; Li Yu^*

来源：Chemical Research in Chinese Universities, 2011, 27(6): 944-948.

• Back propagation(BP) artificial neural network
• Full factorial design
• Fe/Mn oxide
• Organic material
• Atrazine
• Interaction

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 r(2) 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 mu mol/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 mu mol/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.

• 出版日期2011-11
• 单位华北电力大学