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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/2846
Title: Formulation development, modeling and optimization of emulsification process using evolving RSM coupled hybrid ANN-GA framework
Authors: Kundu P.
Paul V.
Kumar, Vimal
Mishra I.M.
Published in: Chemical Engineering Research and Design
Abstract: In the present work, a mathematical and statistical approach was adopted to study the formation and stability of oil-in-water emulsion with an integrated hybrid genetic algorithm (GA) coupled with feed-forward back-propagation artificial neural network (BPANN) and response surface methodology (RSM) based on Box-Behnken design (BBD). The input parameters were oil concentration (10-50%, v/v), surfactant concentration (0.1-2%, w/v), stirring speed (2000-6000rpm) and stirring time (5-20min). The output parameter was relative emulsion volume expressed as emulsion stability index (ESI24). In the proposed hybrid GA model, outputs of BP-ANN model were used as initial population settings and RSM-BBD generated model equation was used as a fitness function. Error analysis was performed on the model fit to the experimental data using sum of square error (SSE), mean square error (MSE) and relative percent error (RPD). The optimum condition predicted by the hybrid GA was 0.913 of ESI24, with 4.70% error under 50% (v/v) oil concentrations, 2% (w/v) surfactant concentrations, 5691rpm and 5min stirring time. The proposed hybrid GA model was found to be useful for the optimization of process parameters for emulsion formation and stability analysis. © 2015 The Institution of Chemical Engineers.
Citation: Chemical Engineering Research and Design (2015), 104(): 773-790
URI: https://doi.org/10.1016/j.cherd.2015.10.025
http://repository.iitr.ac.in/handle/123456789/2846
Issue Date: 2015
Publisher: Institution of Chemical Engineers
Keywords: Back-propagation neural networks (BPANN)
Emulsification
Emulsion stability
Genetic algorithm (GA)
Process optimization
Response surface methodology (RSM)
ISSN: 2638762
Author Scopus IDs: 55835249800
57075879000
7404634425
23668474600
Author Affiliations: Kundu, P., Department of Chemical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, 247667, India
Paul, V., High Energy Materials Research Laboratory, Defence Research and Development Organization (DRDO), Sutarwadi, Pune, Maharashtra, 411021, India
Kumar, V., Department of Chemical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, 247667, India
Mishra, I.M., Department of Chemical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, 247667, India, Department of Chemical Engineering, Indian School of Mines, Dhanbad, Dhanbad, Jharkhand, 826004, India
Funding Details: The financial assistance in the form of research assistantship provided by the Ministry of Human Resource Development (MHRD), Government of India to one of the authors (P. Kundu) is gratefully acknowledged. Appendix A
Corresponding Author: Mishra, I.M.; Department of Chemical Engineering, Indian School of Mines, DhanbadIndia; email: immishra49@gmail.com
Appears in Collections:Journal Publications [CH]

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