MODELLING, SIMULATION AND OPTIMIZATION OF BIOETHANOL PRODUCTION FROM LIGNOCELLULOSE MATERIALS USING OPEN-SOURCE SOFTWARE- SCILAB, R AND AP MONITOR

Abstract

Bioethanol production from lignocellulose biomass has gained a lot of traction over the last two decades because it is a viable alternative energy source to fossil fuel and does not compete with the food supply like first-generation bioethanol does. In this study, previously reported research is used to generate mathematical models for enzymatic hydrolysis, and co-fermentation operations, which are then improved to include the decomposition of hemicellulose to xylose sugars. Two case studies were investigated and modelled in the Scilab and R software which are free and open-source software for numerical and statistical computing, respectively. The first case study examined the impact of alkaline loading and temperature (represented by A & B respectively) on delignification. Results show that A and A² are significant model terms and the optimum process parameters for delignification of 61% are 0.8% alkaline loading and 121℃. The second case investigated how the simultaneous saccharification and co-fermentation (SSCF) operation responds to the key control variables namely cellulose loading(A), hemicellulose loading(B), enzyme loading (C), yeast loading (D), temperature (E) and time (F). From the analysis of variance, the variables found to be significant model terms are: cellulose loading (A), enzyme loading (C), time (F), and then AF, A², C², F². Major parameters of the SSCF unit are then optimized using the APMonitor modeling package in Python to give 0.1263kg/kg minimum enzyme adsorbed per substrate loading, maximum cellulose conversion as 26.26% and a 47.9% maximum ethanol yield. Keywords: bioethanol; optimize; simultaneous saccharification and co-fermentation; dynamic modelling