摘要
以碳源添加量、氮源添加量、恶臭假单胞菌接种量、超声时间和降解时间作为输入变量,以雌激素乙炔基雌二醇(EE2)和双酚A(BPA)的降解率为输出变量,构建土壤中超声辅助的EE2和BPA降解BP神经网络模型.利用BP神经网络模型预测雌激素超声辅助降解析因设计的响应值,并通过析因设计分析影响雌激素微生物降解的主效应及交互作用,进而求解土壤中EE2和BPA的最优降解条件为10%的碳源添加量、10%的氮源添加量、接种量为20mL、超声时间为1min和降解时间为168h.结果表明:BP神经网络模型的相关系数为0.952 5和0.983 1,模拟效率系数(NSC)为0.956 5和0.957 2,模型具有较准确的预测功能;碳源添加量×氮源添加量和碳源添加量×恶臭假单胞菌接种量在雌激素降解过程中发挥协同作用,EE2和BPA最大降解率分别为87.13%和69.27%;分析雌激素的有机碳标化系数(lg Koc)及其半衰期的结果表明,EE2和BPA的降解效果与其移动性成正比,与持久性成反比.
A BP neural network of uhrasonic-assisted biodegradation of EE2 and BPA was established,the amount of carbon source,amount of nitrogen source,inoculum dose,ultrasonic time,and degradation time were set as input,and the degradation rates of ethinyl estradiol （EE2） and bisphenol A （BPA） were set as output.The BP neural network model was employed for predicting the response for the factorial design of ultrasonic-assisted biodegradation of EE2 and BPA.The primary effects and interaction effect on the ultrasonic-assisted biodegradation were analyzed by factorial design.The optimum conditions for EE2 and BPA degradation were obtained.The correlation coefficient between experimental and predictive values were 0.952 5 and 0.983 1 for EE2 and BPA,Nash Suttclife coefficient values were 0.956 5 and 0.957 2,which indicated the accuracy prediction of the models.The results of factorial design analysis suggest that the amount of carbon source × the amount of nitrogen source,and the amount of carbon source × inoculum dose both had a synergistic effect on the degradation of EE2 and BPA.The maximum degradation rates of EE2 and BPA arrived at 87.13％and 69.27％,respectively.The optimum conditions for the degradation were as follow：10％ of carbon source,10％ of nitrogen source,20 mL of inoculum dose,1 min of ultrasonic time,and 168 h of degradation time.The biodegradation difference analysis shows the degradation rate of EE2 and BPA were proportional to mobility,while inversely proportional to the persistence.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2013年第6期1193-1199,共7页
Journal of Jilin University:Science Edition
基金
国家重点基础研究发展计划973项目基金(批准号:2004CB3418501)
关键词
乙炔基雌二醇
双酚A
BP神经网络
析因设计
超声辅助降解
恶臭假单胞菌
ethinyl estradiol （EE2）
bisphenol A （BPA）
BP neural network
factorial design
ultrasonic-assisted degradation
Pseudomonas putida
