Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation

This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures-obtained in the production of xylitol-by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models' inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis.

Automated summary

This study presents a new perspective on the separation of xylose reductase enzyme from reaction mixtures in the large-scale production of xylitol using machine learning techniques. Two types of machine learning models, including an adaptive neuro-fuzzy inference system and a boosted regression tree, were developed and tested. The boosted regression tree model demonstrated superior predictive performance in forecasting membrane flux and xylitol production.