On-Line Measurements and Modelling Study in Second Generation Ethanol Production from Sugarcane

Abstract

A model based on Artificial Neural Networks (ANN) to predict the concentration of ethanol, substrate and cells from secondary measurements (pH, turbidity, CO2 and temperature) was developed in this work. A second generation ethanol production from hydrolyzed sugarcane bagasse was considered as a study case. Experimental data were obtained from fermentation in the range of 30 to 38 °C with cell recycle. The fermentation feedstock is a mixture of molasses and hydrolyzated bagasse from the alkaline hydrogen peroxide pretreatment at 25 % of volume and 75 %, respectively. The accuracy of prediction of the ANN model is evaluated by its precision in describing experimental observations, and by the challenges involved in the use of online measurements. The model used to describe the fermentation provided a good prediction of concentration of cell, substrate and ethanol.