|
Experimental studies and artificial neural network modelingof hydrogen sulfide removal from wastewater by calcium-modifiedcoconut shell based activated carbon |
|---|---|
| รหัสดีโอไอ | |
| Creator | 1. Omar Abed Habeeb 2. Bamidele Victor Ayodele 3. May Ali Alsaffar 4. Tuan Ab Rashid BinTuan Abdullah 5. Ramesh Kanthasamy 6. Rosli Bin Mohd. Yunus |
| Title | Experimental studies and artificial neural network modelingof hydrogen sulfide removal from wastewater by calcium-modifiedcoconut shell based activated carbon |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2564 |
| Journal Title | Songklanakarin Journal of Science and Technology (SJST) |
| Journal Vol. | 43 |
| Journal No. | 1 |
| Page no. | 96-104 |
| Keyword | artificial neural network, adsorbent, hydrogen sulfide, coconut shell, modelling |
| URL Website | https://rdo.psu.ac.th/sjstweb/index.php |
| ISSN | 0125-3395 |
| Abstract | In this study, activated carbon -based adsorbent was prepared from eggshells and coconut shells. The effects of contacttime, initial H2S concentration, and the calcium impregnated coconut shell activated carbon (Ca-CSAC) adsorption dosage on thehydrogen sulphide (H2S) removal efficiency and adsorption capacity were investigated. The batch adsorption data obtained fromthe experimental runs were employed to fit an artificial neural network (ANN) model. An initial optimization was performed toobtain the most suitable number of hidden neurons for training and validation of the ANN. The optimization results show that 16hidden neurons was the most appropriate choice. The trained ANN was adequately validated and tested with coefficients ofdetermination (R2) of 0.99 and 0.95, respectively. The ANN was found to be a robust tool for modeling of H2S removalefficiency by and adsorption capacity on Ca-CSAC under different process conditions. |
