Sulphur dioxide is a harmful gaseous product that needs to be minimized in the atmosphere. This research work investigates the use of zeolite as a possible additive that can improve the sulphur dioxide capture in wet flue gas desulphurisation dissolution process. This work determines the effect of temperature, solid to liquid ratio, acid concentration and stirring speed on the leaching of zeolite using a pH stat apparatus. The atomic absorption spectrometer was used to measure the calcium ions from the solution. It was found that the dissolution rate of zeolite decreased with increase in solid to liquid ratio and increases with increase in temperature, stirring speed and acid concentration. The activation energy for the dissolution rate of zeolite in hydrochloric acid was found to be 9.29kJ/mol. and therefore the product layer diffusion was the rate limiting step.
The elution process for the removal of Co and Cu from clinoptilolite as an ion-exchanger was investigated using three parameters: bed volume, pH and contact time. The present paper study has shown quantitatively that acid concentration has a significant effect on the elution process. The favorable eluant concentration was found to be 2 M HCl and 2 M H2SO4, respectively. The multi-component equilibrium relationship in the process can be very complex, and perhaps ill-defined. In such circumstances, it is preferable to use a non-parametric technique such as Neural Network to represent such an equilibrium relationship.
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.
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 ionexchange.