Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

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• 作者：Arpita Ghosh ; Papita Das ; Keka Sinha
• 关键词：Biosorption ; Cu(II) ; Ca(OH)2 ; modified spent tea leaves ; Response surface methodology ; Central composite design ; Artificial neural network
• 刊名：Applied Water Science
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：5
• 期：2
• 页码：191-199
• 全文大小：1,292 KB
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• 作者单位：Arpita Ghosh (1)
  Papita Das (2)
  Keka Sinha (1)
  
  1. National Institute of Technology, Durgapur, India
  2. Department of Chemical Engineering, Jadavpur University, Kolkata, India
  
• 刊物主题：Hydrogeology; Water Industry/Water Technologies; Industrial and Production Engineering; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution; Nanotechnology; Private International Law, International & Foreign Law, Comparative Law;
• 出版者：Springer Berlin Heidelberg
• ISSN：2190-5495

文摘

In the present work, spent tea leaves were modified with Ca(OH)₂ and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a two-level three-factor (23) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12?%.