TY  - CONF
Y1  - 2016///
SN  - 9781138029781
PB  - CRC Press/Balkema
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009793363&doi=10.1201%2fb21942-46&partnerID=40&md5=165dc58b0e0260274c1ebc3694a43cec
A1  - Khan, T.
A1  - Isa, M.H.
A1  - Mustafa, M.R.
EP  - 234
AV  - none
N1  - cited By 4; Conference of 3rd International Conference on Civil, offshore and Environmental Engineering, ICCOEE 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:180169
N2  - This study examined implementation of Artificial Neural Network (ANN) for the prediction of Cd (II) adsorption from aqueous solution by Incinerated Rice Husk Carbon (IRHC). Batch adsorption tests showed that extent of Cd (II) adsorption depended on initial concentration, contact time and pH. Equilibrium adsorption was achieved in 60 min, while maximum Cd (II) adsorption occurred at pH 5. The Levenberg- Marquardt algorithm (LM) training algorithm was found to be the best among 8 backpropagation (BP) algorithms tested; lowest Mean Square Error (MSE) of 20.99 and highest R2was 0.96. Langmuir constants Q� and b were 40 and 0.04, and Freundlich constants Kfand 1/n were 2.15 and 0.69, respectively. Adsorption capacity of IRHC was compared with other adsorbents and activated carbons reported in the literature. Being a low-cost carbon, IRHC has potential to be used for the adsorption of Cd (II) from aqueous solution and wastewater in developing countries. © 2016 Taylor & Francis Group, London.
KW  - Activated carbon; Adsorption; Backpropagation algorithms; Developing countries; Environmental engineering; Mean square error; Neural networks; Offshore oil well production
KW  -  Adsorption capacities; Artificial neural network approach; Batch adsorption tests; Equilibrium adsorption; Freundlich constants; Initial concentration; Levenberg-Marquardt algorithm; Training algorithms
KW  -  Cadmium compounds
SP  - 229
TI  - Artificial neural network approach for modeling of Cd (II) adsorption from aqueous solution by incinerated rice husk carbon
ID  - scholars7558
ER  -