Maryam Jamshidzadeh , Antonia Ursula Griesz , Jesper Wang Jensen , Ulrich Krühne , John M. Woodley , Krist V. Gernaey , Pablo Ivan Nikel , Helena Junicke
{"title":"以 CFD 为指导的假单胞菌发酵规模化","authors":"Maryam Jamshidzadeh , Antonia Ursula Griesz , Jesper Wang Jensen , Ulrich Krühne , John M. Woodley , Krist V. Gernaey , Pablo Ivan Nikel , Helena Junicke","doi":"10.1016/j.bej.2024.109549","DOIUrl":null,"url":null,"abstract":"<div><div>This study evaluated the scale-up of <em>Pseudomonas putida</em> fed-batch fermentation from a 2 L benchtop-scale stirred bioreactor to a 200 L pilot-scale tank by using a validated computational fluid dynamic (CFD) model. One of the major concerns in this fermentation process is the potential reduction in mixing quality with increasing scale, leading to lower yield or product quality. For a low-risk scale-up, a multiphase Euler-Euler CFD model was developed that simulated the hydrodynamics of the fed-batch system at various filling volumes, representing different stages of the fermentation process. The model was validated using experimental data of mixing time and mass transfer coefficient. The hydrodynamic model was then coupled with a Monod kinetic model of <em>P. putida</em> ‘s fermentation. Response surface methodology was used to generate a performance map of the pilot bioreactor at various aeration, agitation, and bioreactor filling volumes. The study considered different established scale-up approaches, such as constant tip speed and aeration rate across scales, constant <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mi>L</mi></mrow></msub><mi>a</mi></mrow></math></span>, as well as constant power to unit of liquid volume (P/V). The performance of the bioreactor was assessed, and the optimum operating ranges of the input parameters were obtained at different stages of the fermentation. Using performance map the possibility of substrate and oxygen gradient formation, and the gradient severity inside the pilot bioreactor at different working volumes were evaluated.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"213 ","pages":"Article 109549"},"PeriodicalIF":3.7000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CFD-guided scaling of Pseudomonas putida fermentation\",\"authors\":\"Maryam Jamshidzadeh , Antonia Ursula Griesz , Jesper Wang Jensen , Ulrich Krühne , John M. Woodley , Krist V. Gernaey , Pablo Ivan Nikel , Helena Junicke\",\"doi\":\"10.1016/j.bej.2024.109549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study evaluated the scale-up of <em>Pseudomonas putida</em> fed-batch fermentation from a 2 L benchtop-scale stirred bioreactor to a 200 L pilot-scale tank by using a validated computational fluid dynamic (CFD) model. One of the major concerns in this fermentation process is the potential reduction in mixing quality with increasing scale, leading to lower yield or product quality. For a low-risk scale-up, a multiphase Euler-Euler CFD model was developed that simulated the hydrodynamics of the fed-batch system at various filling volumes, representing different stages of the fermentation process. The model was validated using experimental data of mixing time and mass transfer coefficient. The hydrodynamic model was then coupled with a Monod kinetic model of <em>P. putida</em> ‘s fermentation. Response surface methodology was used to generate a performance map of the pilot bioreactor at various aeration, agitation, and bioreactor filling volumes. The study considered different established scale-up approaches, such as constant tip speed and aeration rate across scales, constant <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mi>L</mi></mrow></msub><mi>a</mi></mrow></math></span>, as well as constant power to unit of liquid volume (P/V). The performance of the bioreactor was assessed, and the optimum operating ranges of the input parameters were obtained at different stages of the fermentation. Using performance map the possibility of substrate and oxygen gradient formation, and the gradient severity inside the pilot bioreactor at different working volumes were evaluated.</div></div>\",\"PeriodicalId\":8766,\"journal\":{\"name\":\"Biochemical Engineering Journal\",\"volume\":\"213 \",\"pages\":\"Article 109549\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biochemical Engineering Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1369703X2400336X\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemical Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1369703X2400336X","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
CFD-guided scaling of Pseudomonas putida fermentation
This study evaluated the scale-up of Pseudomonas putida fed-batch fermentation from a 2 L benchtop-scale stirred bioreactor to a 200 L pilot-scale tank by using a validated computational fluid dynamic (CFD) model. One of the major concerns in this fermentation process is the potential reduction in mixing quality with increasing scale, leading to lower yield or product quality. For a low-risk scale-up, a multiphase Euler-Euler CFD model was developed that simulated the hydrodynamics of the fed-batch system at various filling volumes, representing different stages of the fermentation process. The model was validated using experimental data of mixing time and mass transfer coefficient. The hydrodynamic model was then coupled with a Monod kinetic model of P. putida ‘s fermentation. Response surface methodology was used to generate a performance map of the pilot bioreactor at various aeration, agitation, and bioreactor filling volumes. The study considered different established scale-up approaches, such as constant tip speed and aeration rate across scales, constant , as well as constant power to unit of liquid volume (P/V). The performance of the bioreactor was assessed, and the optimum operating ranges of the input parameters were obtained at different stages of the fermentation. Using performance map the possibility of substrate and oxygen gradient formation, and the gradient severity inside the pilot bioreactor at different working volumes were evaluated.
期刊介绍:
The Biochemical Engineering Journal aims to promote progress in the crucial chemical engineering aspects of the development of biological processes associated with everything from raw materials preparation to product recovery relevant to industries as diverse as medical/healthcare, industrial biotechnology, and environmental biotechnology.
The Journal welcomes full length original research papers, short communications, and review papers* in the following research fields:
Biocatalysis (enzyme or microbial) and biotransformations, including immobilized biocatalyst preparation and kinetics
Biosensors and Biodevices including biofabrication and novel fuel cell development
Bioseparations including scale-up and protein refolding/renaturation
Environmental Bioengineering including bioconversion, bioremediation, and microbial fuel cells
Bioreactor Systems including characterization, optimization and scale-up
Bioresources and Biorefinery Engineering including biomass conversion, biofuels, bioenergy, and optimization
Industrial Biotechnology including specialty chemicals, platform chemicals and neutraceuticals
Biomaterials and Tissue Engineering including bioartificial organs, cell encapsulation, and controlled release
Cell Culture Engineering (plant, animal or insect cells) including viral vectors, monoclonal antibodies, recombinant proteins, vaccines, and secondary metabolites
Cell Therapies and Stem Cells including pluripotent, mesenchymal and hematopoietic stem cells; immunotherapies; tissue-specific differentiation; and cryopreservation
Metabolic Engineering, Systems and Synthetic Biology including OMICS, bioinformatics, in silico biology, and metabolic flux analysis
Protein Engineering including enzyme engineering and directed evolution.