Artificial Neural Network Based Modeling of Separation of Xylose from Glucose by Nanofiltration from Monosaccharide Solutions

Abstract

Abstract In industrial processes, chromatographic techniques are applied to separate monosaccharides from one another. Here, the investigation was focused on the use of nanofiltration method to separate the pentose sugar from hexose sugar. A xylose is an abundantly available raw material that can be converted into various vital products like xylitol, 2,3-butanediol ethanol and ethanol. The production of xylitol involves xylose as an intermediate product and glucose interferes in this process. The purpose of this research study is to use an Artificial Neural Network (ANN) to create a model for membrane processes based on nanofiltration using already published experimental results. It was interpreted that the xylose and glucose separation using nanofiltration was possible up to a limited extent. It was observed that the mass proportion of a xylose to the glucose was 1.5 to 3.0 times greater in the permeate than their proportion in the feed. Observed monosaccharide retention depends extremely on factors such as permeate flux, type of membrane used for comparable fractionations and the concentration of monosaccharides. The regression model obtained and the corresponding set of simulations strongly support the required results.