The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Prediction of Biosorption Capacity Using Artificial Neural Network Modeling and Genetic Algorithm: Prediction of Biosorption Capacity
Abstract
Artificial neural network model is applied for the prediction of the biosorption capacity of living cells of Bacillus cereus for the removal of chromium (VI) ions from aqueous solution. The maximum biosorption capacity of living cells of Bacillus cereus for chromium (VI) was found to be 89.24% at pH 7.5, equilibrium time of 60 min, biomass dosage of 6 g/L, and temperature of 30 ± 2 °C. The biosorption data of chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The data is divided into training (75%) and testing (25%) sets. Comparison between the model results and experimental data gives a high degree of correlation R2 = 0.984 indicating that the model is able to predict the sorption efficiency with reasonable accuracy. Bacillus cereus biomass is characterized using AFM and FTIR.
Related Content
Tanima Sahoo, Arijit Mondal, Piyal Roy, Amitava Podder.
© 2024.
20 pages.
|
Hüseyin Fatih Çetinkaya, Ali Fazıl Yenidünya, Serap Çetinkaya, Burak Tüzün.
© 2024.
15 pages.
|
Digvijay Pandey, Vinay Kumar Nassa, Binay Kumar Pandey, Blessy Thankachan, Pankaj Dadheech, Darshan A Mahajan, A. Shaji George.
© 2024.
22 pages.
|
Loutfy H. Madkour.
© 2024.
38 pages.
|
Loutfy H. Madkour.
© 2024.
50 pages.
|
Rita Komalasari.
© 2024.
25 pages.
|
Aakifa Shahul, Balakumar Muniandi, Mukundan Appadurai Paramashivan, Digvijay Pandey, Binay Kumar Pandey, Pankaj Dadheech, Hovan George.
© 2024.
14 pages.
|
|
|