Estimation of biosurfactant yield produced by Klebseilla sp. FKOD36 bacteria using artificial neural network approach. (March 2016)

• Record Type: Journal Article
• Authors: Ahmad, Zulfiqar; Crowley, David; Marina, Ninoslav; Jha, Sunil Kr.
• Is Part Of: Measurement. Volume 81(2016:Mar.)
• Page Start: 163
• Page End: 173
• Publication Date: 2016-03
• Subjects: Artificial neural network -- Biosurfactant production -- Emulsification index -- Surface tension reduction -- Estimation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.12.019
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Artificial neural network (ANN) model for estimation of biosurfactants yield. Bacterial strain Klebseilla sp. FKOD36 as ANN input. Best accuracy of ANN in prediction of biosurfactant yield and two related quantities. High correlation amongst predicted biosurfactant yield with related quantities. Abstract: Environmental and medium parameters estimation is an essential step in Bioprocess engineering. In the present study, artificial neural network (ANN) was employed in estimation of biosurfactants yield from bacterial strain Klebseilla sp. FKOD36, surface tension reduction as well emulsification index. The data obtained from experimental design were used in modelling and optimization of ANN method. Temperature, pH value, incubation period, carbon, nitrogen and hydrocarbon sources were used as input of ANN model independently in the prediction of biosurfactants yield, surface tension reduction and emulsification index. Using the optimized values of critical input elements of ANN, the experimental values of biosurfactant yield, emulsification index and surface tension showed close agreement with the model estimate. The most efficient ANN model assessment was 0.030 g/l for actual value 0.038 g/l of biosurfactant yield, 31.67% for actual value 31.68% of emulsification index, and 21.6 dyne/cm for actual value 21.5 dyne/cm of surface tension respectively.