Pyrolysis and ANN-based modeling of copper-impregnated nylon fishing net waste

Abstract

Although copper-impregnated nylon fishing nets have excellent anti-corrosion properties, they are also highly resistant to biodegradation, which hinders its recycling and requires finding effective solutions to overcome its complexities and reduce its accumulation. In this context, this work investigates the possibility of using pyrolysis to recycle copper-impregnated nylon fishing net waste (CFNW), which has not been addressed before in the literature. The investigation process was carried out using an analytical reactor, followed by studying its kinetic and thermodynamic characteristics. The research began with analysing the composition and basic properties of CFNW using scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), Fourier transform infrared spectroscopy (FTIR), and elemental and proximate measurements. Next, the thermal decomposition properties of CFNW were determined using thermogravimetric analysis (TGA) at different heating parameters. The released pyrolytic vapors were analysed using thermogravimetric coupled with FTIR and gas chromatography–mass spectrometry (GC-MS) system. Based on the measured pyrolysis characteristics of CFNW, the corresponding kinetic and thermodynamic parameters were determined, followed by the development of an artificial neural network (ANN) algorithm to predict decomposition behavior under uncertain heating conditions. The fundamental analyses showed that CFNW was loaded with copper (>3.5 wt%) and had a high content of volatile matter (87.37 %). TGA analysis showed that CFNW has a high melting peak (415–456 ◦C) and only 86 % of its composition can be decomposed up to 500 ◦C. Ester and O–H bonds were the main functional groups of the pyrolytic vapors and caprolactam was its key GC compound with a huge abundance in the range of 75.01 % (10 ◦C/ min) - 89.45 % (15 ◦C/min). The pyrolytic activation energy was evaluated to be in the range of 166.5–193.3 kJ/mol with enthalpy (− 1259 to − 3508 J/mol K), Gibbs free energy (552881–626681 J/mol K), and entropy (− 1426 to − 1259 J/mol K). Finally, the optimized ANN algorithm successfully recognized the thermal degradation of CFNW (ambiguous decomposition) with excellent prediction capabilities. Accordingly, pyrolysis process is an effective candidate for degradation of nylon portion of CFNW to its parent chemical compound caprolactam.