Hybrid genetic algorithm to find the best model and the globally optimized overall kinetics parameters for thermal decomposition of plastics

Abstract

Abstract Model-fitting methods that are exercised till date for evaluating the optimum overall pyrolysis kinetics parameters usually applied traditional gradient base optimization techniques but associated with major drawback of attaining global optimum due to uncertainties in selection of initial guess. To overcome such uncertainties and drawbacks, we have, applied the modern evolutionary optimization method (hybrid genetic algorithms (HGA) technique) for 15 models to attain the globally optimum kinetics parameters using the experimental thermogravimetric analysis (TGA) data and we did compare the experimental and simulated data to expect the possible mechanism to occur during pyrolysis. As case studies, we used thermal decomposition of waste polyethylene terephthalate (PET), waste low-density polyethylene (LDPE) and polypropylene (PP). The suitability of the models is also tested using the AIC c score. Nucleation and growth model with reaction order, n = 2/3 is the best suited one and it also predicted the experimental TGA data successfully. The n th order model also shows good AIC c score and well predicted the experimental TGA data.