A New Modeling Approach for Predicting the Growth of Filamentous Algae in Outdoor Algae-Based Wastewater Treatment Systems

Abstract

Filamentous algae (FA) can form readily harvestable floating mats or attached turfs that facilitate their application in wastewater treatment systems. However, large-scale implementation is hindered by our inability to predict performance as a function of key operational parameters. A predictive mathematical model would be a valuable tool for designing efficient FA-based systems. Developing accurate models is challenging due to dynamic environmental conditions and the spatial complexities of FA cultures. In this work, a model was developed to mathematically describe the biomass productivity of static FA cultures (mats and turfs) in relation to the incident light intensity and temperature. The model was validated against published data to investigate the influence of time-dependent inhibition (inhibition from sustained light exposure) on productivity. When time-dependent inhibition was included in the model, predictions were within ~10% of experimental values, however, without including time-dependent inhibition there was a sixfold overestimation of biomass productivity. The model could also generate predictions of the effects of time-dependent inhibition during diurnal light fluctuations using experimentally determined rate constants. The model represents a powerful tool for optimizing the design and operational parameters in FA cultures that could be further expanded to incorporate the influence of nutrient and CO₂ availability.