Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

Ruming Pan; João Vitor Ferreira Duque; Márcio Ferreira Martins; Gérald Debenest

doi:10.1016/j.heliyon.2020.e05598

Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

Heliyon. 2020 Nov 25;6(11):e05598. doi: 10.1016/j.heliyon.2020.e05598. eCollection 2020 Nov.

Authors

Ruming Pan¹, João Vitor Ferreira Duque^{1

2

3}, Márcio Ferreira Martins³, Gérald Debenest¹

Affiliations

¹ Institut de Mécanique des Fluides de Toulouse (IMFT) - Université de Toulouse, CNRS-INPT-UPS, Toulouse, 31400, France.
² Federal Institute of Education, Science and Technology of Espírito Santo, Serra, 29173-087, Brazil.
³ Laboratory of Combustion and Combustible Matter (LCC), PPGEM, Federal University of Espírito Santo, Vitória, 29075-910, Brazil.

Abstract

In the present study, the waste polyethylene (PE) pyrolysis under different non-isothermal conditions was investigated to estimate the optimal conversions and pyrolysis rates. The pyrolysis study was carried out using Thermogravimetry (TG) of the virgin and the waste PE under different heating rates of 5, 10, 15 and 20 °C/min. The TG experiments indicated that the virgin and the waste PE pyrolysis processes mainly underwent in the temperature range of 390-510 °C. Subsequently, the adaptive neural fuzzy model was adopted to predict the conversions and the pyrolysis rates of the virgin and the waste PE. The optimal operating conditions in different temperature ranges were optimized by the simulated annealing algorithm (SA). Moreover, the R-squared values of the virgin PE conversions (~ 1) and pyrolysis rates (> 0.999), and the waste PE conversions (~ 1) and pyrolysis rates (> 0.999) revealed the high accuracy of the adaptive neural fuzzy model predicted results.

Keywords: Adaptive neural fuzzy model; Chemical engineering; Chemical reaction engineering; Energy sustainability; Environmental science; Fuel technology; Non-isothermal pyrolysis; Simulated annealing algorithm; Waste; Waste polyethylene; Waste treatment.