Improving lipid production by Rhodotorula glutinis for renewable fuel production based on machine learning

Abstract

Microbial lipid fermentation encompasses intricate complex cell growth processes and heavily relies on expert experience for optimal production. Digital modeling of the fermentation process assists researchers in making intelligent decisions, employing logical reasoning and strategic planning to optimize lipid fermentation. It this study, the effects of medium components and concentrations on lipid fermentation were investigated, first. And then, leveraging the collated data, a variety of machine learning algorithms were used to model and optimize the lipid fermentation process. The models, based on artificial neural networks and support vector machines, achieved R² values all higher than 0.93, ensuring accurate predictions of the fermentation process. Multiple linear regression was used to evaluate the respective target parameter, which were affected by the medium components of lipid fermentation. Lastly, single and multi-objective optimization were conducted for lipid fermentation using the genetic algorithm. Experimental results demonstrated the maximum biomass of 50.3 g·L⁻¹ and maximum lipid concentration of 14.1 g·L⁻¹ with the error between the experimental and predicted values less than 5%. The results of the multi-objective optimization reveal the synergistic and competitive relationship between biomass, lipid concentration, and conversion rate, which lay a basis for in-depth optimization and amplification.

Graphical abstract

Keywords

microbial lipid / machine learning / artificial neural network / support vector machine / genetic algorithm

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Competing interests

The authors declare that they have no competing interests.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2022YFB4201903) and the National “111 Project” of China (Grant No. B13005).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11705-024-2410-8 and is accessible for authorized users.

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