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Issue 8.1

Neural Networks for Monitoring Microalgae Biomass in Building Façades

The substantial impact of buildings on CO2 emissions and climate change highlights the urgent need to adopt sustainable measures. Microalgae photobioreactors have shown potential as a renewable feedstock for biomass generation and CO2 absorption. Conventional biomass prediction methods are usually laborious and time-intensive, and sacrifice biomass for measurement. This research addresses these challenges by developing a novel approach using Feed-forward Neural Networks and Convolutional Neural Networks for accurate biomass prediction. Feed-forward Neural Networks demonstrated a positive linear relationship between predicted and valid biomass values of 0.99 on average. Convolutional Neural Networks improved performance over time with an accuracy of up to 97%. This research advanced biomass prediction methodologies and supported optimizing photobioreactors performance for enhanced energy efficiency and biomass generation. 

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