Bioenergy is considered to be one of the promising renewable energies as an alternative to non-renewable fossil fuels. Among different bio-sources for biofuel production, microalgae is advantageous due to its high lipid content photosynthetic rate. However, microalgae production is a cost and energy intensive process, in which the bottle neck is the harvesting since it is challenging to separate the cells from their cultivation medium.

Current research works on microalgae harvesting mainly focus on experiments to address higher cell recovery. In order to optimize the process, mathematical models that includes more comprehensive factors are required. In this research, Population Balance Equation is applied to model several common harvesting processes, including sedimentation (with and w...[ Read more ]

Bioenergy is considered to be one of the promising renewable energies as an alternative to non-renewable fossil fuels. Among different bio-sources for biofuel production, microalgae is advantageous due to its high lipid content photosynthetic rate. However, microalgae production is a cost and energy intensive process, in which the bottle neck is the harvesting since it is challenging to separate the cells from their cultivation medium.

Current research works on microalgae harvesting mainly focus on experiments to address higher cell recovery. In order to optimize the process, mathematical models that includes more comprehensive factors are required. In this research, Population Balance Equation is applied to model several common harvesting processes, including sedimentation (with and without flocculation), centrifugation and filtration. The models are used to derive size specific parameters, such as cell density and filtration coefficient, and their interrelationship with the process operating conditions. Impacts of the process selectivity on the cell size distribution and thus cell lipid content are investigated. After obtaining the parameters, the change in cell number densities along with time and their position during the processes can be predicted. Experiments of microalgae harvesting are carried out to provide cell size distribution data and to validate the models.

Process integration of the harvesting models into microalgae production is performed to simulate and optimize the whole production process. The objective is to minimize the production cost and obtain preliminary operating parameters for process design. The optimization results are presented in two applications to exhibit the result improvement of including cell size distribution in the model.