RESUMO
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.
Assuntos
Redes Neurais de Computação , Ocimum sanctum , Algoritmos , Cobalto , CinéticaRESUMO
A strain of Bacillus amyloliquefaciens KUB29 was identified by 16S ribosomal RNA sequencing (Genbank: MF772779.1). Production of thermostable protease, amylase and lipase were done by the isolated strain. The produced enzymes were partially purified by ammonium precipitation followed by dialysis process. Protease and lipase enzymes are effectively used in bio-oil extraction from proteinaceous sample followed by transesterification to produce methyl ester. Amylase enzyme is widely used in food and laundry industry. The produced enzymes are active at thermophilic condition of 55 °C. Use of these enzymes in biofuel production process will make the process cleaner and greener.