462833 Modelling of Exponential and Stationary Phases in Microalgae Growth Using a Population Balance Equation
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 , 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 doesn’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 . 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.
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