(619ab) Modelling of Exponential and Stationary Phases in Microalgae Growth Using a Population Balance Equation

Being the fastest growing biomass on earth [1], makes microalgae an interesting bio-product [2] source and, depending on the environmental conditions, it is possible to further enhance the productivity. In the past few years, numerous works have focused on developing models able to predict the growth [3, 4], taking under consideration parameters such as temperature, light intensity, nutrients concentration, etc. However, all of these models do not consider the growth as a consequence of the cell life cycle. In other words, the reactor is considered as black box, that under certain conditions (input) gives a correspondent growth (output). Differently, aim of this work is to consider the whole life cycle of the cells, which means that the cell will first grow, then get mature and ready for division and, finally, give birth to daughter cells.

Looking at the downstream process, we know that the harvesting is one of the most energy consuming steps. Knowing that some microalgae may have a wide size range [5], we can use the model to predict which is the most suitable moment to collect the cells. On one side we have that larger cells will require less energy for their collection (assuming that the cell density doesnâ??t change). Also, the moment in which harvesting is actuated, may affect the next batch. For example, if the collected population is old, the initial growth (in the subsequent batch) will be particularly good because the cells will be ready for reproduction and, at the same time, the amount of useful components (e.g. lipids) inside the cell will be much higher than the one present in younger cells.

The population balance equation has been used in numerous and diverse applications in science and engineering (dispersed phases and microbial populations to mention some). Also, applications related to microalgae can be found in literature [6]. The equation can allow to monitor the cell population over the time in different conditions. In this work, the objective is to simulate the exponential and the stationary phases of microalgae growth and evaluate the size distribution.

The work has been experimentally validated. Inspired to open ponds, which are among the most common type of photobioreactors, the experiment was run using some open roof tanks (approximately 10 L) and the turbulence was maintained using some water pumps. Temperature was kept about constant using a temperature controller, connected to a heater placed inside the tank. Nutrients were introduced into the system at the beginning of the experiment, while lights were turned on during all the experiment The concentration of cells as well as the size distribution have been monitored daily. In few words, data related to mass concentration, number concentration and size distribution over the time were collected. The developed model can predict all of the mentioned parameters dynamically in the same experimental conditions.

References

[1] R. P. John, G. Anisha, K. M. Nampoothiri and A. Pandey, "Micro and macroalgal biomass: a renewable source for bioethanol," Bioresour. Technol., vol. 102, pp. 186-193, 2011.

[2] M. A. Borowitzka, "High-value products from microalgaeâ??their development and commercialisation," J. Appl. Phycol., vol. 25, pp. 743-756, 2013.

[3] Q. Béchet, A. Shilton and B. Guieysse, "Modeling the effects of light and temperature on algae growth: State of the art and critical assessment for productivity prediction during outdoor cultivation," Biotechnol. Adv., vol. 31, pp. 1648-1663, 2013.

[4] H. Chang, Y. Huang, Q. Fu, Q. Liao and X. Zhu, "Kinetic characteristics and modeling of microalgae Chlorella vulgaris growth and CO 2 biofixation considering the coupled effects of light intensity and dissolved inorganic carbon," Bioresour. Technol., vol. 206, pp. 231-238, 2016.

[5] K. Miklasz, Physical Constraints on the Size and Shape of Microalgae, 2012.

[6] E. Pahija, Y. Zhang, M. Wang, Y. Zhu and C. W. Hui, "Microalgae growth determination using modified breakage equation model," Computer Aided Chemical Engineering, vol. 37, pp. 389-394, 2015.