Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)-Reference-Cited by-同舟云学术

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

Published:2018-05-03 Issue:7 Volume:20 Page:721-729
ISSN:1522-6514
Container-title:International Journal of Phytoremediation
language:en
Short-container-title:International Journal of Phytoremediation

Author:

Titah Harmin Sulistiyaning¹²³,Halmi Mohd Izuan Effendi Bin⁴,Abdullah Siti Rozaimah Sheikh¹,Hasan Hassimi Abu¹,Idris Mushrifah⁵,Anuar Nurina¹^ORCID

Affiliation:

1. Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

2. Department of Environmental Engineering, Faculty of Civil Engineering and Planning, Institut Teknologi Sepuluh Nopember (ITS), Keputih, Sukolilo, Surabaya, Indonesia

3. Department of Civil and Structural, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

4. Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

5. Tasik Chini Research Centre, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

Publisher

Informa UK Limited

Subject

Plant Science,Pollution,Environmental Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/15226514.2017.1413337

Reference45 articles.

1. Optimized conditions for phytoremediation of diesel by Scirpus grossus in horizontal subsurface flow constructed wetlands (HSFCWs) using response surface methodology

2. Application of response surface methodology for optimization of lead biosorption in an aqueous solution by Aspergillus niger