Application of Artificial Neural Network as a nonhazardous alternative on kinetic analysis and modeling for green synthesis of cobalt nanocatalyst from Ocimum tenuiflorum

The present paper is dedicated to analyze non-hazardous kinetic behaviour and modelling of green synthesized cobalt nanocatalyst (CoNCs), using an Artificial Neural Network (ANN). In order to supplement the trace metal in other applications, CoNCs were rapidly synthesized with a Cobalt sulphate solution at room temperature between 30 and 35 oC at pH 7.2 under less reaction time. The Levenberg - Marquardt algorithm (LM) is used to investigate the experimental values by applying ANN. The results of variance using logistic ANN model depicts that the maximum nanoparticles were synthesized at its optimized stipulation of 0.5 h stirring time, 25 mL volume of extract and 20 mL volume of cobalt sulphate. The developed ANN model proved to be an efficient size determining tool in the biosynthesis of cobalt nanocatalyst. Experimental behavior using potentiometric analysis confirms that the linearity in CoNCs formation and size coincides (5-38 nm)with the predicted values of the ANN model. Techno economic analysis proved that, green synthesis reduced 30-40% in raw material cost and 60% in energy consumption.

Automated summary

This study analyzed the non-hazardous kinetic behavior and modeling of green-synthesized cobalt nanocatalyst using an Artificial Neural Network (ANN). The results showed that the ANN model effectively predicted the synthesis conditions and size of nanoparticles. Experimental results indicated that green synthesis reduced raw material costs by 30-40% and energy consumption by 60%.