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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Microalgae Grown under Heterotrophic and Mixotrophic Conditions
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Microalgal Biotechnology in the Production of Nutraceuticals
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Recovery of Biodiesel Precursors from Heterotrophic Microalga Chlorella protothecoides
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Effect of Photo Bioreactor with Optical Panel on the Growth Rate of Chlorella vulgaris
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Systematic review and perspective on the progress of algal biofuels
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- 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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