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Optimization for high-density cultivation of heterotrophic Chlorella based on a hybrid neural network model
Abstract
AIMS: The purpose of this study was to develop a reliable hybrid neural network (HNN) model for heterotrophic growth of Chlorella, based on which optimization for fed-batch (FB) cultivation of Chlorella may be successfully realized. METHODS AND RESULTS: Deterministic kinetic model was preliminarily developed for the optimization of FB cultivation of Chlorella. The highest biomass concentration and the maximum productivity were obtained as: 104.9 g l(-1) dry cell weight and 0.613 g l(-1) h(-1), respectively. After several cultivations had been performed, an HNN model was developed. The efficiency of biomass production was further increased by the optimization using this model. The highest biomass concentration and the maximum productivity attained was: 116.2 g l(-1) dry cell weight and 1.020 g l(-1) h(-1), respectively. CONCLUSION: The HNN model agreed well with experimental results in different cultivations. Comparison between the HNN model and the deterministic model showed that the former had better generalization ability, which made it a reliable tool in modelling and optimization. SIGNIFICANCE AND IMPACT OF THE STUDY: The high cell density and productivity of biomass obtained in this study is of significance for the commercial cultivation of Chlorella. The simple and efficient optimization strategy proposed in this paper may be employed in heterotrophic mass culture of Chlorella as well as other similar organisms.
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Cites
Recovery of microalgal biomass and metabolites: Process options and economics
Molina Grima, E., Belarbi, E.-H., Acién Fernández, F. G., Robles Medina, A., & Chisti, Y. (2003) · Biotechnology Advances · doi:10.1016/s0734-9750(02)00050-2 · Cited by 2,286
Miao, X., & Wu, Q. (2004) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2004.01.013 · Cited by 609
Commercial development of microalgal biotechnology: from the test tube to the marketplace
Olaizola, M. (2003) · Biomolecular Engineering · doi:10.1016/s1389-0344(03)00076-5 · Cited by 585
High cell density culture of microalgae in heterotrophic growth
Chen, F. (1996) · Trends in biotechnology · doi:10.1016/0167-7799(96)10060-3 · Cited by 354
Predictive non-linear modeling of complex data by artificial neural networks
Almeida, J. S. (2002) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(02)00288-4 · Cited by 332
Bailey, J. E. (1998) · Biotechnology Progress · doi:10.1021/bp9701269 · Cited by 331
Progress in monitoring, modeling and control of bioprocesses during the last 20 years
Schügerl, K. (2001) · Journal of Biotechnology · doi:10.1016/s0168-1656(00)00361-8 · Cited by 278
Shi, X., Zhang, X., & Chen, F. (2000) · Enzyme and Microbial Technology · doi:10.1016/s0141-0229(00)00208-8 · Cited by 251
Self-citationBiological Solar Energy Conversion
(1977) · Elsevier eBooks · doi:10.1016/b978-0-12-500650-7.x5001-6 · Cited by 230
Shi, X., & Chen, F. (2002) · Biotechnology Progress · doi:10.1021/bp0101987 · Cited by 193
Self-citationGrowth and phycocyanin formation of Spirulina platensis in photoheterotrophic culture
Chen, F., Zhang, Y., & Guo, S. (1996) · Biotechnology Letters · doi:10.1007/bf00140211 · Cited by 171
The use of a fuel containing Chlorella vulgaris in a diesel engine
Scragg, A. H., Morrison, J., & Shales, S. W. (2003) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2003.01.001 · Cited by 130
Vlassides, S., Ferrier, J. G., & Block, D. E. (2001) · Biotechnology and Bioengineering · doi:10.1002/1097-0290(20010405)73:1 · Cited by 75
Data‐Based Modeling and Analysis of Bioprocesses: Some Real Experiences
Karim, M. N., Hodge, D. B., & Simon, L. (2003) · Biotechnology Progress · doi:10.1021/bp015514w · Cited by 35
A hybrid representation approach for modelling complex dynamic bioprocesses
Thibault, J., Acuña, G., Pérez-Correa, R., Jorquera, H., Molin, P., & Agosin, E. (2000) · Bioprocess and Biosystems Engineering · doi:10.1007/s004499900110 · Cited by 33
MASS CULTURE OF CHLORELLA IN ASIAN COUNTRIES
Tsukada, O., KAWAHARA, T., & Miyachi, S. (1977) · Elsevier eBooks · doi:10.1016/b978-0-12-500650-7.50032-5 · Cited by 28
Zhang, X.-W., Shi, X.-M., & Chen, F. (1999) · Journal of Industrial Microbiology & Biotechnology · doi:10.1038/sj.jim.2900760 · Cited by 24
Self-citationIdentifiability and retrievability of unique parameters describing intrinsic Andrews kinetics
Seagren, E. A., Kim, H., & Smets, B. F. (2003) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-002-1220-4 · Cited by 14
Research works printed but not linked: 1
- Shinskey F.G., 1998, Chem Proc, 61, 87
Cited by
Sorted by citations
Brennan, L., & Owende, P. (2010) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2009.10.009 · Cited by 4,894
Heterotrophic cultures of microalgae: Metabolism and potential products
Perez‐Garcia, O., Escalante, F. M. E., de‐Bashan, L. E., & Yoav Bashan, A. (2010) · Water Research · doi:10.1016/j.watres.2010.08.037 · Cited by 1,820
Microalgae as versatile cellular factories for valued products
Koller, M., Muhr, A., & Braunegg, G. (2014) · Algal Research · doi:10.1016/j.algal.2014.09.002 · Cited by 639
Xiong, W., Li, X., Xiang, J., & Wu, Q. (2007) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-007-1285-1 · Cited by 548
Microalgae bioengineering: From CO2 fixation to biofuel production
Zeng, X., Danquah, M. K., Chen, X. D., & Lu, Y. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2011.04.014 · Cited by 299
Metadata
- Authors
- Wu, Z., & Shi, X.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Shi, Xianming
- Venue
- Letters in Applied Microbiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-01
- Volume
- 44
- Issue
- 1
- Pages
- 13-18
- ISSN
- 0266-8254 (ISSN-L)
- 1365-2673
- OpenAlex
- W1984288767
- Cited by
- 115
- References
- 18
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 6 · 2022: 4 · 2021: 10 · 2020: 4 · 2019: 7 · 2018: 3 · 2017: 6 · 2016: 9 · 2015: 8 · 2014: 9 · 2013: 8 · 2012: 6
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Letters in Applied Microbiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Agricultural and Biological Sciences › Food Science › Advanced Scientific Research Methods
- Keywords
- Chlorella, heterotrophic cultivation, fed-batch fermentation, hybrid neural network, cultivation condition optimization, biomass productivity, microalgae mass culture, high-density cultivation
- MeSH terms
- Chlorella, Industrial Microbiology, Models, Biological, Neural Networks, Computer, Biomass, Cell Culture Techniques, Heterotrophic Processes
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