Use of Genetic Algorithm and Particle Swarm Optimization in the Estimation of Kinetic Parameters of Green Coconut Biomass

Abstract

The development of more economically and energy-efficient processes for the sustainable production of fuels and chemicals is becoming increasingly necessary. In this context, it is relevant to understand the behaviour of thermal degradation of different biomasses in oxygen free atmosphere to investigate the breakdown of polymer chains, which can be converted into new products. Good effects on the acceleration of degradation of organic molecules can be achieved with the use of catalysts in these breaking processes, thus increasing the yield of bio-oil production. In this work, fiber of crushed and sifted green coconut shell, submitted to thermogravimetry analysis (TG), was used as biomass. Some types of a catalyst were incorporated into the biomass, based on cobalt ferrite, Fe2CoO4. The design of a biomass pyrolysis reactor requires knowledge of kinetic parameters, activation energy (Ea), pre-exponential factor (k0) and order of reaction (n). In this context, the present study aimed to perform the thermogravimetric (TG) analysis in an inert atmosphere of pure nitrogen, and to determine the kinetic parameters involved in the lignin pyrolysis process, to assist in the design of biomass conversion reactors. This work presents a solution to obtain the kinetic parameters for thermogravimetric reaction of lignin breaking present in the biomass of the green coconut. Two computational methods were used: genetic algorithm and particle swarm optimization. The results obtained for activation energy, pre-exponential factor and order reaction are in the range of values found in the literature.