Spent Red Mud-Catalyzed Copyrolysis of Waste Pods of Prosopis juliflora and Low-Density Polyethylene Waste: Thermal and Kinetic Insights and Predictive Modeling Using Artificial Neural Network Approach

Abstract

In the present study, catalytic copyrolysis of Prosopis juliflora pods (PJ) with waste low-density polyethylene (LDPE) was investigated using a spent red mud-derived catalyst (RMC) through thermogravimetric analysis. The analysis was conducted at three heating rates of 10, 20, and 30 °C/min for the PJ and LDPE blend (1:1), and the blend of feedstock (PJ & LDPE) with RMC catalyst (1:1). Further, kinetic and thermodynamic analyses were conducted, and the reaction mechanism was elucidated through Criado’s Z-master plot method. Kinetic parameters were assessed using four different iso-conversional models, and subsequently thermodynamic parameters were estimated. The results from the present study showed that the average activation energy was reduced by 9–10% in all models with the incorporation of spent red mud-derived catalyst (RMC) in copyrolysis. Furthermore, an artificial neural network (ANN) model was applied to predict the catalytic copyrolysis behavior of PJ and LDPE waste. A high regression coefficient (R ≈ 0.99) and a low mean squared error (MSE ≈ 10–5) were observed, which suggests the accuracy of the prediction model for copyrolytic degradation of PJ and LDPE waste. Hence, the study confirms the potential for copyrolytic conversion of PJ and LDPE waste using a spent red mud-derived catalyst into fuels and chemicals via a cost-effective and sustainable process.