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)

• Main Authors: Titah, Harmin Sulistiyaning, Halmi, Mohd Izuan Effendi, Sheikh Abdullah, Siti Rozaimah, Abu Hasan, Hassimi, Idris, Mushrifah, Anuar, Nurina
• Format: Article
• Language: English
• Published: Taylor & Francis 2018
• Online Access: http://psasir.upm.edu.my/id/eprint/73966/1/Statistical%20optimization%20of%20the%20phytoremediation%20of%20arsenic%20by%20ludwigia%20octovalvis%20in%20a%20pilot%20reed%20bed%20using%20response%20surface%20methodology%20%28RSM%29%20versus%20an%20artificial%20neural%20network%20%28ANN%29.pdf; http://psasir.upm.edu.my/id/eprint/73966/; https://www.ncbi.nlm.nih.gov/pubmed/29723047
• Institution: Universiti Putra Malaysia

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic remo...