首页|Findings from Shanghai Jiao Tong University Provide New Insights into Machine Le arning (From Simulation To Reality: Cfd-ml-driven Structural Optimization and Ex perimental Analysis of Thermal Plasma Reactors)
Findings from Shanghai Jiao Tong University Provide New Insights into Machine Le arning (From Simulation To Reality: Cfd-ml-driven Structural Optimization and Ex perimental Analysis of Thermal Plasma Reactors)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on Machine Learning is now available. According to news reporting out of Shanghai, People’s Republic of China, by New sRx editors, research stated, “Thermal plasma reactors offer an environment of h igh temperature, enthalpy, and reactivity, making them highly efficient for soli d waste treatment and promising for clean energy production from municipal and i ndustrial waste. Optimal geometrical parameters of the reactor can enhance waste treatment and reactor performance.” Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “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 mo del focusing on the average temperature in the core melt zone was trained using six Machine Learning (ML) algorithms. The Particle Swarm Optimisation (PSO) algo rithm optimized the hyperparameters of the Gradient Booster Regression (GBR) mod el, which was combined with a Genetic Algorithm (GA) to identify the reactor’s o ptimal 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 plasm a jet mode on system performance. Findings indicate that the PSO-GBR model achie ved the highest prediction accuracy, with the temperature in the core reaction z one reaching 3621 K. The deviation between numerical simulations and machine lea rning predictions was a mere 1.3%. Enhancing syngas yield and energ y efficiency is achievable by controlling reaction temperature and increasing th e gasification coefficient. A laminar plasma jet mode, at equal power, provides a more effective reaction environment.”
ShanghaiPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningMathematicsNumerical Mod elingShanghai Jiao Tong University
NETL
NSTL
