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文章摘要
基于神经机器的电动-植物联合修复污染土壤方案评估
Evaluation of Combined Electrokinetic Phytoremediation Program for Contaminated Soil Based on Neural Machine
  
DOI:
中文关键词: 重金属  BP神经网络  电动-植物联合修复  污染土壤
英文关键词: Heavy metal  BPNN  Electrokinetic and phytoremediation  Contaminated soil
基金项目:陕西省自然科学“基于光矿农互补技术的尾矿库污染土壤修复与资源综合利用研究”基金资助项目(2019SF-246);国家自然科学“不同根系深度植物对薄层紫色土坡地的水分适应机理”基金资助项目(41471188)
作者单位
郭琳 商洛学院电子信息与电气工程学院商洛市生态环境技术研究中心 
张孝存 商洛市生态环境技术研究中心商洛学院城乡规划与建筑工程学院 
赵培 商洛市生态环境技术研究中心商洛学院城乡规划与建筑工程学院 
陈垚 商洛学院电子信息与电气工程学院 
刘俊 商洛学院电子信息与电气工程学院 
张商州 商洛学院电子信息与电气工程学院 
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中文摘要:
      以地处秦岭山区闭库14 a的金矿尾矿库为研究对象,实施电动-植物联合修复污染土壤方案,设计一种先甄别提取,再计算优化、交互集成,最后自动评估的方法,经模型训练得到该方案的评估值。结果表明:尾矿库区适宜生长的东南景天、白茅、黑麦草和蒲公英可作为富集植物,选取Cd、Cu、Zn作为去除对象,训练样本评估效率和实验效率的相对误差分别为47%、-35%、124%,测试样本的评估效率分别为756%、472%、565%,〖JP2〗可用于指导实验室或场地土壤修复。
英文摘要:
      Taking the gold tailing ponds which had been closed for 14 years in Qinling Mountains as the research object, a combined electrokinetic and phytoremediation program for contaminated soil was developed. The program was designed as first screening and extracting, then calculating for optimization and interactive integrating, finally automatic evaluating. The estimated value of the program was derived from model training. The results showed that as suitable plants for growing in the tailing ponds area, Sedum alfredii, Imperata cylindrica, Lolium perenne and Dandelion were selected as hyperaccumulators. Taking Cd, Cu and Zn as eliminating objects, the relative error of evaluation efficiency and experimental efficiency of training samples were 4.7%,-3.5% and 12.4%, respectively. The evaluation efficiency of test samples were 75.6%, 47.2% and 56.5%, respectively. It could be used to guide laboratory or site soil remediation.
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