Abstract
Dunaliella salina is a green microalga with the great potential to generate natural β-carotene. However, the corresponding mathematical models to guide optimized production of β-carotene in Dunaliella salina (D. salina) are not yet available. In this study, dynamic models were proposed to simulate effects of environmental factors on cell growth and β-carotene production in D. salina using online monitoring system. Moreover, the identification model of the parameter variables was established, and an adaptive particle swarm optimization algorithm based on parameter sensitivity analysis was constructed to solve the premature problem of particle swarm algorithm. The proposed kinetic model is characterized by high accuracy and predictability through experimental verification, which indicates its competence for future process design, control, and optimization. Based on the model established in this study, the optimal environmental factors for both β-carotene production and microalgae growth were identified. The approaches created are potentially useful for microalga Dunaliella salina cultivation and high-value β-carotene production.Graphical
Highlights
In the last decades, massive investments were done on microalgae industry, mainly due to their capacity to synthesize lipids for biofuel production or synthesize the carotenoid for high-value product production (Chew 2017; Kong et al 2018; Salome and Merchant 2019). β-Carotene is a high-valued carotenoid pigment with wide applications in the cosmetic, pharmaceutical, and food industries (Coppens et al 2016; Gateau et al 2017; Paillie-Jimenez et al 2020)
To successfully conduct process control and make optimization, it is essential to construct a highly accurate kinetic model, which can be capable of well predicting the dynamic behavior of the underlying biosystem
To accurately simulate the dynamic process of the β-carotene induction stage, the current study aims to construct rigorous models including the effects of temperature, average light intensity, carbon and nitrogen source on microalgal growth, and β-carotene accumulation, which to the best of our knowledge has not been reported at present
Summary
Introduction In the last decades, massive investments were done on microalgae industry, mainly due to their capacity to synthesize lipids for biofuel production or synthesize the carotenoid for high-value product production (Chew 2017; Kong et al 2018; Salome and Merchant 2019). β-Carotene is a high-valued carotenoid pigment with wide applications in the cosmetic, pharmaceutical, and food industries (Coppens et al 2016; Gateau et al 2017; Paillie-Jimenez et al 2020). Mathematical models have been used to predict and optimize the microalgae biomass and astaxanthin production, and specific variables including light intensity, temperature, retention time, and nutrients’ concentration have been used to monitor the process performance and construct models (Zhang et al 2015, 2016). Integrated experimental–computational frameworks that have the ability to predict biomass growth and product accumulation under different growing conditions, which will help to optimize the process performance, operating conditions, and scale-up of cultivation systems for commercialization and industrial applicability (Zeriouh et al 2017). To accurately simulate the dynamic process of the β-carotene induction stage, the current study aims to construct rigorous models including the effects of temperature, average light intensity, carbon and nitrogen source on microalgal growth, and β-carotene accumulation, which to the best of our knowledge has not been reported at present.
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