Synthesis of value-added aromatic chemicals from catalytic pyrolysis of waste wind turbine blades and their kinetic analysis using artificial neural network
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Date
2024-01
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Elsevier
Series Info
Journal of Analytical and Applied Pyrolysis;Volume 177January 2024 Article number 106330
Scientific Journal Rankings
Abstract
This research aims to convert the resin fraction of waste wind turbine blades (WTB) into value-added aromatic
chemicals using catalytic pyrolysis. The catalytic study on WTB made of glass fibre/unsaturated polyester resin
(UPR) was performed on two different types of zeolite catalysts (ZSM-5 and Y-type) using a thermogravimetric
(TG) analyser. The effect of catalyst and heating rate on the abundance and composition of the synthesised
aromatic chemicals was observed using TG-FTIR and GC/MS. The kinetics and thermodynamic behaviour of
catalytic pyrolysis of WTB was also studied using traditional modelling techniques (KAS, FWO, Friedman,
Vyazovkin, and Cai) and an artificial neural network (ANN). TG-FTIR results showed that the gases released from
the catalytic process were very rich in aromatic groups, while GC/MS analysis revealed that benzene, toluene,
and ethylbenzene (BTE) were the main constituents of the synthesised aromatic chemicals with abundance
estimated at 36% (ZSM-5 at 10◦C/min) and 64% (Y-type at 15◦C/min) accompanied by a significant reduction in
styrene formation up to 16.2% (main toxic element in the UPR). Besides, it contributed to reduction of the
activation energy of the reaction up to 126 KJ/mol (ZSM-5) and 100 KJ/mol (Y-type). The trained ANN model
also showed high performance in predicting the thermal decomposition zones of WTB at unknown heating rates
with R2 close to 1. Accordingly, the use of catalytic pyrolysis of WTB over a Y-type zeolite catalyst is highly
recommended for decomposition of UPR to aromatic chemicals BTE and reduction of styrene in the produced
fuel.
Description
Keywords
Waste wind turbine blades, Catalytic pyrolysis, Value-added aromatic chemicals, Artificial neural network, Catalytic pyrolysis kinetics