Understanding Fouling in an Industrial Biorefinery Membrane Separation Process by Feature-Oriented Data-Driven Modeling

Membrane separation processes are precious assets for biorefineries to separate biomass from the solution containing the product after bioconversion in an effective and energy-efficient way. However, fouling can significantly reduce the benefits of membrane separations. Effects of fouling can be reversible, manifesting as short-term process disruption, or irreversible, causing long-term membrane degradation; the two actions typically affect one another. Understanding potential causes of membrane fouling is of paramount importance to mitigate this undesired phenomenon and improve process operation. In this study, we perform a comprehensive investigation of membrane fouling in the ultrafiltration operation of the world's first industrial-scale biorefinery manufacturing 1,4-biobutanediol via bioconversion of renewable raw materials. We use principal component analysis to extract information from sensor data spanning six months of plant operation. Furthermore, we resort to feature-oriented data-driven modeling to address the variability of batch duration, and we exploit process knowledge to enhance information on the effects of fouling. We show how this approach can provide valuable information on the effectiveness of the cleaning and control policies adopted by plant operators, and offer guidelines on how to improve the membrane maintenance schedule. We also resort to engineering judgment for model interpretation in order to identify potential causes of fouling, uncover a strong interaction between reversible and irreversible fouling, and plan experimental investigations to clarify some of the detected effects and assess new ones.