Model Free and Hybrid Genetic Algorithm Coupled Direct Search Methods for Pyrolysis Kinetics: Thermal Decomposition of Waste PET

Model-free approach that are implemented till date for evaluating the optimum overall pyrolysis kinetics parameters usually used traditional gradient base optimization techniques. Since polymer decomposition is a complicated process, serious uncertainties arise about an accurate description of decomposition kinetics by using simplified equations expressing a rate of the process only via mass loss [1-5]. To overcome such drawbacks and uncertainties, we have, used the modern evolutionary optimization method (i.e. hybrid genetic algorithms (HGA) technique). The experimental thermogravimetric analysis (TGA) data was used in this technique to attain the globally optimum kinetics parameters. Also, we compare the experimental and simulated data to obtain the possible mechanism to take place during pyrolysis. As case study, we used thermal decomposition of polyethylene terephthalate (PET). The suitability of the models is also tested using the AICc score. Also, the nth order model demonstrations good AICc score and well predicted the experimental TGA data.