%0 Conference Paper
%A Khan, T.
%A Isa, M.H.
%A Mustafa, M.R.
%D 2016
%F scholars:7558
%I CRC Press/Balkema
%K Activated carbon; Adsorption; Backpropagation algorithms; Developing countries; Environmental engineering; Mean square error; Neural networks; Offshore oil well production, Adsorption capacities; Artificial neural network approach; Batch adsorption tests; Equilibrium adsorption; Freundlich constants; Initial concentration; Levenberg-Marquardt algorithm; Training algorithms, Cadmium compounds
%P 229-234
%R 10.1201/b21942-46
%T Artificial neural network approach for modeling of Cd (II) adsorption from aqueous solution by incinerated rice husk carbon
%U https://khub.utp.edu.my/scholars/7558/
%X 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.
%Z 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