Simulating biotechnological processes affected by meteorology: Application to algae-bacteria systems

Most of the existing mathematical models for outdoor biotechnological processes require the measurement of medium temperature, and therefore, they cannot forecast the dynamics of the process in the future or perform scenario analysis under different climatology. Fully predictive models are thus required for advanced predictions, and optimization, of environmental bioprocesses affected by weather fluctuations. This is of major importance for supporting bioprocess design, decision making and process management industries. Here, we introduce the FLAME modelling framework to forecast the future of outdoor bioprocesses. It integrates, on top of a core biological model conserving carbon, nitrogen and phosphorus, a heat transfer model and a chemical sub-model for computing the speciation of all the dissociated chemical molecules. The versatile FLAME modelling platform includes different modules with balanced complexities. Alternative biological models can easily be interchanged, in order to promote a dialog for bioremediation model comparisons and improvements. This approach is illustrated with an algae-bacteria wastewater treatment pond, subjected to solar flux and meteorological events (wind, rain, ... ). The fully predictive model was validated during more than a year, therefore representing every season. Temperature prediction appeared to be crucial, especially for appropriately simulating nitrification. The model estimates the dynamics of the different biomasses in the system, providing a diagnosis tool to follow the hidden part of the process dynamics. The proposed framework is a powerful tool for advanced control and optimization of environmental processes, which can guide the scaling up and management of the most innovative bioprocesses.

Automated summary

Existing mathematical models for outdoor biotechnological processes cannot predict the future dynamics, thus fully predictive models are needed. The FLAME modelling framework integrates biological, heat transfer, and chemical sub-models to forecast outdoor bioprocesses. It provides a powerful tool for controlling and optimizing environmental processes.