Abstract
A single-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Ni(II) ions from aqueous solution using shelled Moringa Oleifera seed (SMOS) powder. Batch experiments resulted into standardization of optimum conditions: biomass dosage (4.0 g), Ni(II) concentration (25 mg/L) volume (200 mL) at pH 6.5. A time of forty minutes was found sufficient to achieve the equilibrium. The ANN model was designed to predict sorption efficiency of SMOS for target metal ion by combining back propagation (BP) with principle component analysis. A sigmoid axon was used as transfer function for input and output layers. The Levenberg–Marquardt Algorithm (LMA) was applied, giving a minimum mean squared error (MSE) for training and cross validation at the ninth place of decimal.
Highlights
The pattern of industrial activity alters the natural flow of materials and introduces chemicals in their effluents [1]
In continuation of our work on biosorption of toxic metals using agricultural waste from waste water [17,18,19,20], the present paper describes the abatement of Ni(II) ions from aqueous system using shelled Moringa Oleifera seed (SMOS) powder
Moringa Oleifera Lam. tree was notified in the nearby area of Dayalbagh Educational Institute and the seeds were collected from the target plant
Summary
The pattern of industrial activity alters the natural flow of materials and introduces chemicals in their effluents [1] Most of these effluents contain toxic substances especially heavy metals. The removal of heavy metals from wastewater has recently become the subject of considerable interest due to more strict legislations introduced to control water pollution. Current methodologies such as chemical precipitation, electro floatation, ion-exchange and reverse osmosis have been used for the removal of heavy metals [2]. Activated carbon is regarded as an effective adsorbent for removal of metal ions from water [3] These processes are economically non-feasible especially for the developing countries [4]
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