This article presents a novel methodology using a combination of nonparametric frontier analysis models, data envelopment analysis (DEA), and decision maker (DM) model to optimize real-time reaction variables, that is, concentration of butyl acrylate, time duration of the reaction, and temperature for precision synthesis of poly(butyl acrylate) (PBA) and xyloglucan (tamarind seed polysaccharide) graft copolymers.The copolymer samples (units) obtained by a different set of reaction variables and conditions are ranked using DEA to identify the efficient units. An appropriate minimum weight restriction is imposed by the DM on the chosen inputs and output by linear programming models that are designed in such a way that each DEA efficient unit can get "maximin" weight. The model predictions for reaction parameters and experimental data obtained are found to be very close to each other.
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