Optimisation and performance prediction of photosynthetic biogas upgrading using a bubble column

Optimising biogas upgrading with microalgae in a combined bubble column-photobioreactor setup is at a low technology readiness. Most of the prior studies investigated the optimisation of the overall system and generated variable results. The approach employed here is to optimise the individual components of the system to achieve definitive conclusions and improve the integrated process performance. In this work, performance prediction and optimisation of the least optimised component of photosynthetic biogas upgrading, the bubble column, was conducted. The influence of liquid inlet pH and alkalinity, superficial gas velocity and liquid to gas flow rate (L/ G) ratio were studied using the response surface methodology (RSM). Bubble column operations were sufficiently predicted via quadratic models for liquid inlet pH between 9.4 and 10.2; liquid inlet alkalinity between 1.3 ginorganic carbon (IC)/L and 2.1 gIC/L; superficial gas velocities between 0.3 cm/s and 0.6 cm/s; and L/G ratios between 0.3 and 0.8. To ensure optimal operations while producing grid quality biomethane (CO2 < 2.5% and O2 < 1% by volume), the following tighter conditions were shown to be necessary: liquid inlet pH between 10 and 10.2; alkalinity between 1.7 gIC/L and 2.1 gIC/L; superficial gas velocities between 0.5 cm/s and 0.6 cm/s; and L/G ratio between 0.6 and 0.7. Based on these results, a perspective on the industrial-scale operation of the integrated bubble column photobioreactor system was developed by estimating the required number of bubble columns and the areal footprint of photobioreactors to upgrade 478 Nm3/hr of biogas (equivalent to a 1 MWe biogas plant).

Automated summary

This study aims to optimize the bubble column component in the photosynthetic biogas upgrading system, and provides optimization conditions for liquid inlet pH, alkalinity, superficial gas velocity, and liquid to gas flow rate. These results can improve the overall process performance.