Textile waste and plastic blend creates viable fuel in new recycling process
Researchers have demonstrated that mixing textile factory sludge with plastic waste and heating it to 500°C produces usable fuel oil and biochar. The finding offers manufacturers a practical waste-to-energy solution that could reduce landfill pressure while creating revenue from byproducts.
Originaltitel: Assessing the synergies between textile sludge and low-density polyethylene (LDPE) Co-pyrolysis: Experimental insights and machine learning
<p>Co-pyrolysis leverages complementary properties of diverse feedstock to improve conversion efficiency, offering a sustainable route for integrated waste management and energy production. This study investigates reaction kinetics, synergistic interactions, product analysis, and machine learning prediction for co-pyrolyzing textile sludge (TS) with low-density polyethylene (LDPE) at mass ratios of 25%TS:75%LDPE, 50%TS:50%LDPE, and 75%TS:25%LDPE. Thermogravimetric analysis was performed from room temperature to 1000 degrees C at 2.5, 5, 7.5, and 10 degrees C/min. Model-free methods like linear differential Friedman, linear integral Kissinger-Akahira-Sunose (KAS), and Ozawa-Flynn-Wall (OFW) were used to evaluate kinetic parameters. Average Ea (Friedman) was 262.61 kJ/mol for 25%TS:75%LDPE, 178.34 kJ/mol for 50%TS:50%LDPE and 267.80 kJ/mol for 75%TS:25% LDPE. Furthermore, positive synergistic interactions were most significant between 450-600 degrees C with the dominant peaks at 500 degrees C for all blended samples. Fixed-bed co-pyrolysis of the optimum blend (50%TS:50% LDPE) at 500 degrees C produced 22% pyro-oil, 34% biochar, and 44% gaseous products. Moreover, obtained pyro-oil comprises of hydrocarbons and long-chain aliphatic derivatives, N-containing heterocycles and amines, carboxylic and phenolic acids, ethers and acetal, phthalates, and the carbohydrate derivatives. Furthermore, machine learning models like Artificial neural networks (ANNs), Classification & regression trees (C&RT), and support vector machine (SVM) were developed to predict Ea. ANN performed best for 50%TS:50%LDPE and 75% TS:25%LDPE (R2 = 0.999 and 0.997) while C&RT excelled for 25%TS:75%LDPE (R2 = 0.988). Findings demonstrate pronounced synergistic interactions and integrated kinetic and machine learning prediction strategy for converting diverse waste into energy.</p>