Study on waste tire pyrolysis product characteristics based on machine learning

Abstract

Tire pyrolysis is a highly complex thermochemical conversion process that transforms waste tires into high-value products such as pyrolysis oil, pyrolysis gas, and pyrolysis char. This process significantly mitigates the environmental issues caused by waste tires and reduces reliance on fossil resources. The physicochemical properties of tires and pyrolysis operation parameters have a significant impact on the yield of the three-phase products, thus affecting the industrial viability of tire pyrolysis to a large extent. Traditional prediction methods such as computational fluid dynamics and process simulation often fail to provide satisfactory results. However, data-driven machine learning (ML) models have demonstrated their ability to handle complex nonlinear problems and offer more reliable predictions of pyrolysis products yield. This study employed a collected database of tire pyrolysis to develop tire pyrolysis product prediction models based on five ML models. These models were further optimized using Particle Swarm Optimization (PSO), and their prediction performances were quantitatively evaluated to identify the optimal model. Shapley analysis and one-way partial dependence analysis were conducted to explore the impact of input features on the output responses. Furthermore, an industrial-grade software was developed for accurate prediction of tire pyrolysis three-phase products yield. The results revealed that Gaussian process regression (GPR) and random forest regression (RFR), both optimized with PSO, demonstrated impressive prediction performance. Among them, the GPR model achieved the highest prediction accuracy with coefficient of determination (R2) values of 0.964, 0.924, and 0.86 for oil, char, and gas yields respectively, during the testing stage.