Dynamics of CO2 consumption, and biomass and lipid carbon production during photobioreactor cultivation of the diatom Cyclotella

Abstract

Understanding of CO2 delivery and consumption dynamics in algal photobioreactors are critical to unravel microalgae's full potential for bioproduct generation and carbon capture from flue gas streams. This study aims to expand our current understanding by cultivating the diatom Cyclotella under controlled process conditions of a bubble column photobioreactor and analyzing CO2 consumption dynamics in real time using results from an online CO2 sensor connected to the reactor exhaust. Two sets of experiments were conducted: they served to contrast the influence of silicon and nitrate (Si & N colimitation) and Si limitation, and the light availability, respectively. CO2 consumption was calculated based on the mass balance around the reactor inlet and outlet gas streams. Biomass samples and lipid extracts were analyzed for carbon (C) content to determine biomass-C and lipid-C concentrations. The outlet CO2 concentrations varied significantly with cultivation time and process conditions. More than 15% to 65% of the CO2 introduced left the reactor in the exhaust at any instance based on the set CO2 transfer rates. The highest average daily capturing efficiency was 60%. Nutrient limitation regimes imposed generated unique CO2 consumption profiles undiscernible by the biomass-C analysis, i.e. unlike Si limitation, N limitation had more immediate detrimental effects on C consumption. Final biomass-C concentration increased with increasing N and light availability, 275 mg/L vs. 336 mg/L, and 270 mg/L vs. 501 mg/L, respectively. Biomass-C based capturing efficiency approximations resulted in 20% to 40% underestimation. Under Si-limited conditions, the higher light intensity increased the final lipid-C to biomass-C ratio by two times (from 20% to 40%) and the final lipid-C concentration and peak productivity by four times (from 56 mg/L to 216 mg/L, from 7 to 30 mg/L-day, respectively). This study demonstrates online exhaust CO2 concentration-based analysis's unique capabilities for assessing carbon availability and capture, organic-C production, and its diversion to biomass and lipid production.