Minimization of carbon losses in pilot-scale outdoor photobioreactors by model-based predictive control

Abstract

The optimization of carbon use in pilot-scale outdoor tubular photobioreactors is investigated in this study. The behavior of a 0.20-m(3) tubular photobioreactor was studied, with and without algae, by steady-state and pulse dynamic-response analysis experiments. A model of the system was obtained and implemented in a programmable control unit and was used to control the reactor under normal production conditions. Results showed that, using and on-off control, the mean daily CO(2) flow in the reactor was 0.86 g min(-1), 19.7% of this being lost. By using a predictive control algorithm the mean daily CO(2) flow was reduced to 0.74 g min(-1), with losses being reduced to 15.6%. In this case, pH tracking was not adequate, especially at the beginning and end of the daylight period, because the variation in solar irradiance was not considered. Taking solar irradiance into account resulted in better performance, with mean daily CO(2) flow reduced to 0.70 g min(-1), and carbon losses reduced to 5.5%. pH tracking was improved and valve actuation was reduced. Improvement of pH control reduced pH gradients in the culture, which increased the photosynthesis rate and biomass productivity of the system. Biomass productivity increased from 1.28 to 1.48 g L(-1) day-(1) when on-off control was replaced by model-based predictive control plus solar irradiance effect mode. Implementation of this methodology in outdoor photobioreactors can increase productivity by 15% and reduce the cost of producing biomass by >6%. Clearly, application of effective control techniques, such as model-based predictive control (MPC), must be considered when developing these processes.