Optimization of operating parameters for xylose reductase separation through ultrafiltration membrane using response surface methodology
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Updated: Dec 14, 2025

Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing
Published on: February 13, 2016
Santhana Krishnan1, B Noor Suzana2, Zularisam Abdul Wahid3
1Center of Environmental Sustainability and Water Security (IPASA), Research Institute of Sustainable Environment (RISE), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia.
Abstract:
The application of the xylose reductase (XR) enzyme in the development of biotechnology demands an efficient and large scale enzyme separation technique. The aim of this present work was to optimize xylose reductase (XR) purification process through ultrafiltration membrane (UF) technology using Central composite design (CCD) of response surface methods (RSM). The three effective parameters analyzed were filtration time (0-100), transmembrane pressure (TMP) (1-1.6 bar), cross flow velocity (CFV) (0.52-1.2 cm/s-1) and its combined effect to obtain high flux with less possibility of membrane fouling. Experimental studies revealed that the best range for optimization process for filtration time, operational transmembrane pressure and cross flow velocity was 30 min, 1.4 bars and 1.06 cm/s, respectively as these conditions yielded the highest membrane permeability (56.03 Lm-2h-1 bar-1) and xylitol content (15.49 g/l). According to the analysis of variance (ANOVA), the p-value (<0.0001) indicated the designed model was highly significant. The error percentage between the actual and predicted value for membrane permeability and xylitol amount (2.21 % and 4.85 % respectively), which both were found to be close to the predicted values. The verification experiments gave membrane actual permeability of 57.3 Lm-2h-1 bar-1 and 16.29 g/l of xylitol production, thus indicating that the successfully developed model to predict the response.
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