Mathematical Modelling of the Hydrolysis of Lignocellulosic Materials (Sawdust) Using Genetic Algorithms for the Production of Bioethanol

Abstract

This paper investigates the use of lignocellulosic biomass for the production of bioethanol at laboratory and bench scale, combining appropriate technology with suitable raw materials: residual lignocellulosic materials (sawdust) from the growing forestry industry and commercially available cellulase enzymes. Bioethanol produced from sugar and starch is known as first generation bioethanol, whereas bioethanol from lignocellulosic biomass is called second generation bioethanol. Lignocellulosic material pretreatment and the hydrolysis of the pretreated material was investigated to determine the optimal conditions for laboratory and bench scale. The collected data was used to implement an experimental setup for the pretreatment of lignocellulosic material (based on laboratory screening tests and with a capacity to generate 5-L substrates), and a 20-L autoclave reactor to perform the hydrolysis of cellulosic materials and study its transformation into fermentable sugars. Genetic algorithms were employed to model and simulate the hydrolysis of sawdust. To start, a suitable mathematical model was generated by defining the most significant chemical reactions and obtaining the best kinetic parameters for the model (steady-state simulation). Finally, a systematic study was performed to analyze the controllability of the key process variables (dynamic-state simulation).