Modeling and Optimization of Gas Sparging-Assisted Bacterial Cultivation Broth Microfiltration by Response Surface Methodology and Genetic Algorithm Article Swipe
Aleksandar Jokić
,
Ivana Pajčin
,
Nataša Lukić
,
Vanja Vlajkov
,
Arpad Kiralj
,
Selena Dmitrović
,
Jovana Grahovac
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/membranes11090681
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/membranes11090681
Production of highly efficient biomass-based microbial biopesticides significantly depends on downstream processing in terms of obtaining as high concentration of viable cells as possible. Microfiltration is one of the recommended operations for microbial biomass separation, but its main limitation is permeate flux decrease due to the membrane fouling. The effect of air sparging as a hydrodynamic technique for improvement of permeate flux during microfiltration of Bacillus velezensis cultivation broth was investigated. Modeling of the microfiltration was performed using the response surface methodology, while desirability function approach and genetic algorithm were applied for optimization, i.e., maximization of permeate flux and minimization of specific energy consumption. The results have revealed antagonistic relationship between the investigated dependent variables. The optimized values of superficial feed velocity and transmembrane pressure were close to the mean values of the investigated value ranges (0.68 bar and 0.96 m/s, respectively), while the optimized value of superficial air velocity had a more narrow distribution around 0.25 m/s. The results of this study have revealed a significant improvement of microfiltration performance by applying air sparging, thus this flux improvement method should be further investigated in downstream processing of different bacterial cultivation broths.
Related Topics
Concepts
Microfiltration
Sparging
Air sparging
Fouling
Response surface methodology
Membrane
Permeation
Chromatography
Biomass (ecology)
Flux (metallurgy)
Environmental science
Materials science
Pulp and paper industry
Process engineering
Chemistry
Engineering
Biology
Agronomy
Ecology
Metallurgy
Biochemistry
Physical chemistry
Contamination
Environmental remediation
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/membranes11090681
- https://www.mdpi.com/2077-0375/11/9/681/pdf?version=1630499473
- OA Status
- gold
- Cited By
- 4
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198082696
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3198082696Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/membranes11090681Digital Object Identifier
- Title
-
Modeling and Optimization of Gas Sparging-Assisted Bacterial Cultivation Broth Microfiltration by Response Surface Methodology and Genetic AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
-
Aleksandar Jokić, Ivana Pajčin, Nataša Lukić, Vanja Vlajkov, Arpad Kiralj, Selena Dmitrović, Jovana GrahovacList of authors in order
- Landing page
-
https://doi.org/10.3390/membranes11090681Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0375/11/9/681/pdf?version=1630499473Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2077-0375/11/9/681/pdf?version=1630499473Direct OA link when available
- Concepts
-
Microfiltration, Sparging, Air sparging, Fouling, Response surface methodology, Membrane, Permeation, Chromatography, Biomass (ecology), Flux (metallurgy), Environmental science, Materials science, Pulp and paper industry, Process engineering, Chemistry, Engineering, Biology, Agronomy, Ecology, Metallurgy, Biochemistry, Physical chemistry, Contamination, Environmental remediationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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