Caprolactam-rich vapor from copper leaching and pyrolysis of copper-impregnated nylon fishing net waste: Kinetics, thermodynamics and artificial neural network

Abstract

This study aimed to extract copper and caprolactam from copper-impregnated nylon fishing net waste (CFNW) using chemical leaching and pyrolysis, respectively. Leaching was carried out using nitric acid, and the extraction parameters were then optimized until copper-free nylon fishing net waste was produced. Subsequently, the thermal decomposition characteristics of the leached CFNW (LCFNW) were investigated using thermogravimetric analyser (TGA) as an analytical pyrolysis reactor. The composition of the emitted pyrolysis vapors was determined using TG-FTIR and GC-MS techniques. Kinetic and thermodynamic models of LCFNW were constructed using various methods to study the reaction pathway and the complexities of its decomposition. Finally, an artificial neural network (ANN) was used to simulate the decomposition of LCFNW under unknown decomposition conditions. The optimized leaching process successfully dissolved over 99 % of copper. The TGA results showed that copper removal from CFNW reduces the its decomposition temperature to 490 °C and increases weight loss by up to 92 wt%. Simultaneously, GC-MS analysis revealed a significant increase in caprolactam content (96 %) in the pyrolysis vapors. However, leaching resulted in a slight increase in activation energy in the range of 193–302 kJ/mol with R2 ≥ 0.925. Furthermore, a trained ANN can be adapted as an advanced machine learning tool to monitor the decomposition of LCFNW under undefined conditions with R² = 1. Accordingly, leaching and pyrolysis process offer significant potential for extracting copper and caprolactam from CFNW and simplifying their decomposition process, contributing to the reduction of marine plastic pollution and the protection of aquatic ecosystems.