Application of the Clustering Hybrid Regression Approach to Model Xylose-Based Fermentative Hydrogen Production

The applicability of the CHR (clustering hybrid regression) approach was evaluated in modeling the H2 production rate from the metabolic endproducts (ethanol, acetate, butyrate, propionate, and valerate), CO2 production rate, and monitoring variables (pH, oxidation–reduction potential, and alkalinity) of the bioreactor system. Self-organizing maps (SOMs) were used to visualize and understand the relationships between the variables in the multidimensional data set. K-means clustering was used to cluster the data set into statistically significant clusters. The local multiple-regression models for modeling the H2 production rate were formulated for each cluster. The data was obtained from the xylose (concentration 20 gCOD/L) based fermentative H2-producing continuously stirred tank reactor (CSTR). The bioreactor (working volume, 4 L) was operated for 376 days at 35 ± 1 °C and a hydraulic retention time of 12 h. The data was obtained when the bioreactor reached steady-state conditions. Different metabolic patterns (acetate and butyrate as the main metabolic products) in anaerobic xylose degradation were investigated. High H2 production rates were observed during two states: first, when butyrate metabolism at pH 7 was occurring; and second, when acetate coupled metabolism at pH 6.7 was taking place in the bioreactor.