Development of a predictive aeration strategy for microalgae cultivation in photobioreactors - Magdaong, Jeremy Jay B. (Animo Repository, 2018)
Microalgae have gained popularity due to its potential applications ranging from biofuel production, food production, wastewater treatment, CO2 capture technologies more. However, the cost for cultivating microalgae, especially in photobioreactors are still too high to become economically feasible. Strategies to drive down the operational costs of microalgae cultivation in photobioreactors should therefore be a priority. In microalgae cultivation aeration is an important factor needed to improve the growth, but it is also one of the contributors to the high operational costs of photobioreactors. Majority of the operation of photobioreactors employ a constant aeration strategy throughout the whole cultivation process, however this can still be improved using dynamic aeration. Therefore, a predictive aeration strategy was proposed and developed to enhance the overall performance of the microalgae cultivation process in photobioreactors using artificial neural networks. The growth characteristics of C. sorokiniana AK 1 were obtained via a series of cultivation experiments which were then used to create and train a robust artificial neural network using MATLAB. Different validation methods were done to assess the robustness of the ANN trained. The growth characteristics of C. sorokiniana AK 1 were then predicted at multiple dynamic aeration profiles that were generated. From the simulations, performance enhancing aeration strategies/profiles were identified and were tested in another batch of cultivation of C. sorokiniana AK 1. Results showed that the performance enhancing aeration strategies were able to reduce the total aeration requirement by 17% - 21% without reducing the biomass production and other parameters. The predictive aeration strategy was therefore able to enhance the overall performance of microalgae cultivation in photobioreactors. In addition to this, the ANN framework used to develop the PAS can also be applied to enhance other processes.