Clustering hybrid regression: a novel computational approach to study and model biohydrogen production through dark fermentation

Clustering hybrid regression (CHR) approach was developed and evaluated using data from H2-producing glucose-based, suspended-cell bioreactor operated for 5 months. The aim was to describe the relationship between metabolic end products and H2-production rate. Self-organizing maps (SOM) were used to better visualize the dataset and to detect main metabolic patterns in bioprocess data. SOM detected three distinct metabolic patterns with butyrate, acetate and ethanol as dominant metabolites, respectively. Butyrate dominated metabolism was related to high H2 production, while acetate and ethanol dominated metabolisms resulted in low H2 production. CHR models performed well [mean square error (MSE) 0.55 and 0.56] in modeling the H2-production rate. The results validate the suitability of the CHR approach in describing the bioprocess behavior and in the modeling of H2 production rate. The developed model can help in discovering key metabolic interactions and suitable process parameters from complex datasets, and increase the understanding of the bioprocesses occurring in engineered and natural environments.