Abstract
A two-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Cd(II) ions from aqueous solution using shelled Moringa Oleifera seed (SMOS) powder. Batch experiments re-sulted into standardization of optimum conditions: biomass dosage (4.0 g), Cd(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 layer. 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
An increasing awareness about the environment motivated research has developed search for new efficient technologies that would be capable of treating heavy metal contaminated waste water in a cost effective manner
In continuation of our work on biosorption of toxic metals using shelled Moringa Oleifera seed (SMOS) powder from waste water [8,9,10], this paper describes a two-layer Artificial Neural Network (ANN) model using a back propagation (BP) algorithm to predict the removal efficiency of SMOS for Cd(II) ions
Sorption studies led to the standardization of the optimum conditions as: metal concentration (25 mg/L), biomass dosage (4.0 gm), contact time (40 min) and volume (200 mL) at pH 6.5 for maximum Cd removal (85.10%)
Summary
An increasing awareness about the environment motivated research has developed search for new efficient technologies that would be capable of treating heavy metal contaminated waste water in a cost effective manner. Conventional techniques used for the removal of heavy metals from waste water include filtration, precipitation, flocculation, ion exchange resins and reverse osmosis [1]. These methods are not economically viable if sophisticated instrumentation is utilized [2]. In continuation of our work on biosorption of toxic metals using shelled Moringa Oleifera seed (SMOS) powder from waste water [8,9,10], this paper describes a two-layer ANN model using a back propagation (BP) algorithm to predict the removal efficiency of SMOS for Cd(II) ions. The present piece of work highlights the possibility of the prediction of sorption efficiency for the metal ions from waste water using SMOS in the range of metal concentration with which lab experiments have not been conducted
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