From simulation to reality: CFD-ML-driven structural optimization and experimental analysis of thermal plasma reactors

Abstract

Thermal plasma reactors offer an environment of high temperature, enthalpy, and reactivity, making them highly efficient for solid waste treatment and promising for clean energy production from municipal and industrial waste. Optimal geometrical parameters of the reactor can enhance waste treatment and reactor performance. This study presents a comprehensive analysis of 11 key geometrical parameters of a thermal plasma reactor. Utilizing CFD Fluent software, numerical simulations were conducted to generate a dataset. Subsequently, a predictive model focusing on the average temperature in the core melt zone was trained using six Machine Learning (ML) algorithms. The Particle Swarm Optimisation (PSO) algorithm optimized the hyperparameters of the Gradient Booster Regression (GBR) model, which was combined with a Genetic Algorithm (GA) to identify the reactor's optimal geometrical parameters. A DC arc plasma torch-solid waste thermal plasma reactor treatment system was established on this basis. The study also explored the effects of gasification coefficient, reaction temperature, and thermal plasma jet mode on system performance. Findings indicate that the PSO-GBR model achieved the highest prediction accuracy, with the temperature in the core reaction zone reaching 3621 K. The deviation between numerical simulations and machine learning predictions was a mere 1.3%. Enhancing syngas yield and energy efficiency is achievable by controlling reaction temperature and increasing the gasification coefficient. A laminar plasma jet mode, at equal power, provides a more effective reaction environment. The accuracy and reliability of the machine learning-driven regression model and optimization results are significant in guiding the optimal design of plasma reactors and advancing waste-to-energy conversion processes.