Xylitol has manifold beneficial health properties, e.g. possessing a low insulin index, while tasting similarly to sucrose and thus being perfectly suitable as sugar substitute for diabetics . Its currently predominant production process involves extensive purification and separation steps, which makes it highly cost-expensive . The feedstock for this process is lignocellulosic biomass, whose hemicellulosic fraction consists to major parts of xylose. Rather than pursuing a chemical reaction, the xylose can also be converted to xylitol by fermentation. Consequently, a sustainable, biotechnological production process for xylitol represents a possible alternative . One of the bottlenecks remains the pretreatment of the lignocellulosic biomass in order to partition the hemicellulosic fraction optimally from the biomass. Therefore, the scope of this work comprises a comprehensive analysis of a pretreatment experiment design: Two pretreatment methods are analyzed in two respective design of experiments. Furthermore, the design is optimized comparatively by means of a) Response Surface Methodology (RSM) and b) an Artificial Neural Network (ANN) coupled with a Genetic Algorithm (GA), in order to provide a statement on optimal process conditions for a maximal xylitol production. This is complemented by a sensitivity analysis of both approaches . On basis of the optimized design, one of the pretreatments is chosen to be implemented as a model in order to serve in an overall process design routine.
|Number of pages||2|
|Publication status||Published - 2019|
|Event||ECCE12, the 12th European Congress of Chemical Engineering - |
Duration: 15 Sep 2019 → 19 Sep 2019
|Conference||ECCE12, the 12th European Congress of Chemical Engineering|
|Period||15/09/2019 → 19/09/2019|