Modeling biosurfactant production from agroindustrial residues by neural networks and polynomial models adjusted by particle swarm optimization
Author:
Funder
CNPq/MCT
CAPES
FAPERJ
FINEP
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-022-22481-3.pdf
Reference39 articles.
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2. Ahmad Z, Crowley D, Marina N, Jha SK (2016) Estimation of biosurfactant yield produced by Klebseilla sp. FKOD36 bacteria using artificial neural network approach. Meas: J Int Meas Confed 81:163–173. https://doi.org/10.1016/j.measurement.2015.12.019
3. Akbari S, Abdurahman NH, Yunus RM et al (2018) Biosurfactants—a new frontier for social and environmental safety: a mini review. Biotechnol Res Innov 2:81–90
4. Bezerra MA, Santelli RE, Oliveira EP et al (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76:965–977. https://doi.org/10.1016/j.talanta.2008.05.019
5. Bharathi Raja S, Baskar N (2011) Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation. Int J Adv Manuf Technol 54:445–463. https://doi.org/10.1007/s00170-010-2958-y
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1. Unlocking the potential of biosurfactants: Production, applications, market challenges, and opportunities for agro-industrial waste valorization;Environmental Research;2024-03
2. Advances in the co-production of biosurfactant and other biomolecules: statistical approaches for process optimization;Journal of Applied Microbiology;2024-02-01
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