Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Abstract

Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

“…–. To account for light attenuation, the modified Lambert–Beer law presented in Eq. was adopted to calculate local light intensity.…”

“…To facilitate the industrialization of microalgal bioprocesses, extensive research has been conducted on the identification of optimal operating conditions including light intensity, temperature, and nutrient supply, to achieve maximum biomass growth and bioproduct synthesis . A major challenge to commercialization is light attenuation (decrease of local light intensity along the light transmission direction) caused by algal cell absorption and bubble scattering, critically limiting the potential for high‐density biomass cultivation .…”

A Non-Vector Approach to Increase Lipid Levels in the Microalga Planktochlorella nurekis

“…Industrial applications of some microalgal strains may be limited due to a lack of strain robustness or low productivity under outdoor conditions [6,7]. Successful process scale-up in terms of dense biomass concentrations and high biomolecule productivities requires the real-time control and optimization that may be achieved by dynamic modeling [8]. In order to achieve full processing capabilities of microalgae as cell-factories of bio-based products, several approaches have been considered, e.g., biochemical and genetic engineering [7,[9][10][11][12][13].…”

“…In order to achieve full processing capabilities of microalgae as cell-factories of bio-based products, several approaches have been considered, e.g., biochemical and genetic engineering [7,[9][10][11][12][13]. Microalgal growth and biomass composition may be modulated by selected environmental factors such as light, temperature and availability of nutrients and minerals [8]. In general, optimal light and temperature and nutrient replete conditions are needed to achieve enhanced growth rate [8,14].…”

“…Microalgal growth and biomass composition may be modulated by selected environmental factors such as light, temperature and availability of nutrients and minerals [8]. In general, optimal light and temperature and nutrient replete conditions are needed to achieve enhanced growth rate [8,14]. Moreover, in nutrient replete conditions, carbon is usually built to nitrogen containing macromolecules such as proteins and amino acids [14].…”