Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

clinoptilolite Related Abstracts

3 Parameters Affecting the Removal of Copper and Cobalt from Aqueous Solution onto Clinoptilolite by Ion-Exchange Process

Authors: John Kabuba, Hilary Rutto

Abstract:

Ion exchange is one of the methods used to remove heavy metal such as copper and cobalt from wastewaters. Parameters affecting the ion-exchange of copper and cobalt aqueous solutions using clinoptilolite are the objectives of this study. Synthetic solutions were prepared with the concentration of 0.02M, 0.06M and 0.1M. The cobalt solution was maintained to 0.02M while varying the copper solution to the above stated concentrations. The clinoptilolite was activated with HCl and H2SO4 for removal efficiency. The pHs of the solutions were found to be acidic hence enhancing the copper and cobalt removal. The natural clinoptilolite performance was also found to be lower compared to the HCl and H2SO4 activated one for the copper removal ranging from 68% to 78% of Cu2+ uptake with the natural clinoptilolite to 66% to 51% with HCl and H2SO4 respectively. It was found that the activated clinoptilolite removed more copper and cobalt than the natural one and found that the electronegativity of the metal plays a role in the metal removal and the clinoptilolite selectivity.

Keywords: ion-exchange, clinoptilolite, cobalt and copper, mass dosage

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2 Application of Neural Network on the Loading of Copper onto Clinoptilolite

Authors: John Kabuba

Abstract:

The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: Modeling, Neural Network, clinoptilolite, loading

Procedia PDF Downloads 265
1 Isotherm Study of Modified Zeolite in Sorption of Naphthalene from Water Sample

Authors: Homayon Ahmad Panahi, Amir Hesam Hassani, Akram Torki, Elham Moniri

Abstract:

A new sorbent was synthesized through chemical modification of clinoptilolite zeolite using 2-naphtol, and characterized with fourier transform infrared spectroscopy and elemental analysis methods and applied for the removal and elimination of trace naphthalene from water samples. The optimum pH value for sorption of the naphthalene by modified zeolite was in acidic pH. The sorption capacity of modified zeolite was 142 mg. g−1. Isotherm models, Langmuir, Frendlich and Temkin were employed to analyze the adsorption capacity of modified zeolite, which revealed that naphthalene adsorption by this zeolite follows Langmuir model.

Keywords: Modification, Zeolite, naphthalene, clinoptilolite

Procedia PDF Downloads 266