Thermal degradation properties of LDPE insulation for new and aged fine wires

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

“…For the individual heating rate, the mass fraction and pyrolysis rate curves of the waste PE shifted laterally to the higher temperatures than the virgin one. The same phenomenon was observed in [ 39 ], indicating that the waste PE is more challenging to be thermally decomposed than the virgin PE. Moreover, as shown in Figure 3 b, the mass fraction and pyrolysis rate curves of the virgin and the waste PE had more significant gaps at 10 °C/min, consistent with the literature [ 39 ].…”

“…The same phenomenon was observed in [ 39 ], indicating that the waste PE is more challenging to be thermally decomposed than the virgin PE. Moreover, as shown in Figure 3 b, the mass fraction and pyrolysis rate curves of the virgin and the waste PE had more significant gaps at 10 °C/min, consistent with the literature [ 39 ]. However, to our best knowledge, the abnormality at 10 °C/min has been undetermined to date.…”

“…The single peak reveals the overall pyrolysis rate, which practically integrates all the reactions [38]. The onset temperature (To), the temperature at maximum pyrolysis rate (Tm), and end temperature (Te) were proposed to evaluate the thermal pyrolysis process by different researchers [38,39,41]. Figure 4 depicts To, Tm, Te and the maximum pyrolysis rates of the virgin and the waste PE at different heating rates.…”

“…The negative mass fraction difference values were in smaller ranges, around 5-7 and 13-20 C/min, and 390-420 C. The minimum value was -2.01 wt% at 408 C and 5 C/min, while the maximum value was up to 24.28 wt% at 465 C and 8.75 C/min. It indicated that the waste PE's thermal pyrolysis process was more difficult to start than the virgin PE [39]. Figure 7b demonstrates the pyrolysis rate differences between the waste PE and the virgin PE.…”

LLDPE kinetic properties estimation combining thermogravimetry and differential scanning calorimetry as optimization targets

“…Although thermal analysis techniques provides a useful framework to obtain the characterization of behaviour of materials in fire [11], most of the INPUT thermal parameters and kinetic properties for fire computer models cannot be obtained directly from those tests. To solve this gap, some authors have combined pyrolysis models and optimization methods, such as model-fitting [12][13][14][15], isoconversional methods [16][17][18][19][20] or genetic algorithms, etc. [21][22][23] to calculate these INPUT, which may be used in fire computer models.…”