Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant

Sivapathasekaran, C.; Mukherjee, Soumen; Ray, Arja; Gupta, Ashish; Sen, Ramkrishna

doi:10.1016/j.biortech.2009.09.093

Cited by 119 publications

(75 citation statements)

References 9 publications

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“…The most efficient ANN model showed a yield of 0.038 gL −1 , with and emulsion index and ST reduction of 31.67% and 21.6 mNm −1 , respectively. Sivapathasekaran et al [45] also used the ANN strategy with Bacillus circulans MTCC 8281. After determining the most important factors for Bs production (glucose, urea, SrCl 2 , and MgSO 4 ), ANN was used to enhance Bs production by 70%.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review

Bertrand

Martínez‐Morales

Rosas-Galván

et al. 2018

Colloids and Interfaces

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Biosurfactants (Bs) have been studied for decades and applied in different industrial sectors because of their competitive biochemical characteristics, and the fact that they are environmentally friendly. Current scientific investigations mainly involve the search for novel Bs producing organisms with attractive characteristics. Bs are expected to replace synthetic surfactants in the near future, but low production yields and inefficient downstream processes have prevented their widespread use. Although there are numerous reports on Bs optimization, to date there has been no critical compilation or revision of the statistical designs and strategies employed for improved production. The purpose of this mini review is to briefly discuss the factors that affect Bs production and the importance of statistical design as an essential tool for increasing production.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review

Bertrand

Martínez‐Morales

Rosas-Galván

et al. 2018

Colloids and Interfaces

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“…This is a new approach not reported earlier. However, optimization studies based on the ANN-GA for improved performance of biological systems have been reported earlier by Haider et al (2008) and Sivapathasekaran et al (2010).…”

Section: Software Usedmentioning

confidence: 99%

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

Meena

Gupta

Majumdar

et al. 2011

Braz. arch. biol. technol.

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“…However, in some cases, complex non-linear biological interactions cannot be completely described by using second-order polynomial model based on RSM [11,12]. Hence, a more advanced modelling and optimization technique such as artificial neural network modelling coupled with genetic algorithm has been successfully implemented to optimize multivariate non-linear bio-processes [13,14]. The merits of ANN based models were discussed in earlier reports [13,15].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, a more advanced modelling and optimization technique such as artificial neural network modelling coupled with genetic algorithm has been successfully implemented to optimize multivariate non-linear bio-processes [13,14]. The merits of ANN based models were discussed in earlier reports [13,15]. Since genetic algorithm suffers from one major shortcoming as it destroys previous information between successive generations, a more robust algorithm that can deal with relatively small population size and can help converge at the optimal solutions very quickly while memorizing the previously known good solutions between generations is in great demand.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Degradation of Low Density Polyethylene Films by Curvularia lunata SG1 Using Particle Swarm Optimization Strategy

Raut

Sharma

et al. 2015

Indian J Microbiol

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In the present study, artificial neural network (ANN) modelling coupled with particle swarm optimization (PSO) algorithm was used to optimize the process variables for enhanced low density polyethylene (LDPE) degradation by Curvularia lunata SG1. In the non-linear ANN model, temperature, pH, contact time and agitation were used as input variables and polyethylene bio-degradation as the output variable. Further, on application of PSO to the ANN model, the optimum values of the process parameters were as follows: pH = 7.6, temperature = 37.97°C, agitation rate = 190.48 rpm and incubation time = 261.95 days. A comparison between the model results and experimental data gave a high correlation coefficient (R 2 ANN ¼ 0:999). Significant enhancement of LDPE bio-degradation using C. lunata SG1by about 48 % was achieved under optimum conditions. Thus, the novelty of the work lies in the application of combination of ANN-PSO as optimization strategy to enhance the bio-degradation of LDPE.Electronic supplementary material The online version of this article

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Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant

Cited by 119 publications

References 9 publications

Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review

Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

Enhancing Degradation of Low Density Polyethylene Films by Curvularia lunata SG1 Using Particle Swarm Optimization Strategy

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