Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process
作者: Z. SekulićD. AntanasijevićS. StevanovićK. Trivunac
作者单位: 1Institute of Public Health of Belgrade
2Innovation Center of the Faculty of Technology and Metallurgy
3University of Belgrade
刊名: International Journal of Environmental Science and Technology, 2017, Vol.14 (7), pp.1383-1396
来源数据库: Springer Nature Journal
DOI: 10.1007/s13762-017-1248-8
关键词: Back propagationHeavy metalsMicrofiltrationModeling of rejection coefficient
英文摘要: Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also...
原始语种摘要: Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also...
全文获取路径: Springer Nature  (合作)
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关键词翻译
关键词翻译
  • Zn 元素锌的符号
  • removal 消去
  • wastewater 废水
  • efficiency 效率
  • complexation 络合作用
  • neural 神经系统的
  • artificial 人为的
  • metal 金属
  • selected 被选
  • range 射程