NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants

Abstract

Abstract:© Versita Sp. z o.o. Received 22 March 2011; Accepted 28 July 2011 Keywords: Optimization • Neural network modelling • Real jumping genes • Polymeric nanoparticlesPolydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. Two neural networks and a genetic algorithm were used to optimize this process, by minimizing the particle diameter and the polydispersity, finding in this way the optimum values for surfactant and polymer concentrations, and storage temperature. In order to improve the performance of the non-dominated sorting genetic algorithm, NSGA-II, a genetic operator was introduced in this study -the transposition operator -"real jumping genes", resulting NSGA-II-RJG. It was implemented in original software and was applied to the multi-objective optimization of the polymeric nanoparticles synthesis with silicone surfactants. The multi-objective function of the algorithm included two fitness functions. One fitness function was calculated with a neural network modelling the variation of the particle diameter on the surfactant concentration, polymer concentration, and storage temperature, and the other was computed by a neural network modelling the dependence of polydispersity index on surfactant and polymer concentrations. The performance of the software program that implemented NSGA-II-RJG was highlighted by comparing it with the software implementation of NSGA-II. The results obtained from simulations showed that NSGA-II-RJG is able to find non-dominated solutions with a greater diversity and a faster convergence time than NSGA-II.

Neural Networks Applied in Chemistry. II. Neuro-Evolutionary Techniques in Process Modeling and Optimization

“…Furtuna et al modeled a complex polymerization process using two neural networks and a vectorial GA. Polydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. By minimizing particle diameter and polydispersity, the optimum values for surfactant and polymer concentrations and storage temperature were found.…”

“…Figure illustrates the optimal decision variables (surfactant concentration, polymer concentration, and storage temperature), corresponding to each point from the optimal Pareto front. The storage temperature was found to affect only the average diameter, while the surfactant concentration and the polymer concentration affected both optimization objectives.…”

“…Further chemical engineering applications, using ANNs and GAs with a scalar objective function include estimation of the optimal conditions for dyestuff wastewater treatment through heterogeneous photocatalytic oxidation, 76 determination of optimum conditions for the absorption of copper ions from aqueous solutions by silica functionalized with dihydroxyazomethine groups, 77 determinination of molecular structures of thermally stable liquid crystalline ferrocene derivatives and phenyl compounds, 61 optimization of the color removal efficiency for TiO 2 -assisted photodegradation of the cationic dye Alcian Blue 8 GX. 78 Furtuna et al 79 modeled a complex polymerization process using two neural networks and a vectorial GA. Polydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. By minimizing particle diameter and polydispersity, the optimum values for surfactant and polymer concentrations and storage temperature were found.…”