合肥工业大学主页平台管理系统 Yang Qingchun--Home--Scientific Research

Personal Information

Associate professor

Supervisor of Master's Candidates

Date of Birth:1990-01-11

School/Department:化工工艺系

Education Level:With Certificate of Graduation for Doctorate Study

Business Address:升华楼427

Gender:Male

Degree:Doctoral degree

Status:Employed

Alma Mater:华南理工大学

Discipline:Industrial Catalysis
Chemical Technology
Chemical Engineering

Date of Birth:1990-01-11

Gender:Male

Education Level:With Certificate of Graduation for Doctorate Study

Alma Mater:华南理工大学

Current position: Home / Scientific Research

[1] Yang QC(*), Zhao L, Bao R, Fan Y, Zhou J, Rong D, Zhou H, Zhang, D. Interpretable machine learning-assisted advanced exergy optimization for carbon-neutral olefins production. Renewable and Sustainable Energy Reviews 2025, 208, 115027. (IF=16.3, 一区TOP)
[2] Yang QC(*), Fan Y, Rong D, Bao R, Zhang D. An auto-configurable machine learning framework to optimize and predict catalysts for CO2 to light olefins process. AIChE Journal 2024, e18437. (IF=3.5, 化工三大刊)
[3] Bao R, Zhang F, Rong D, Wang Z, Guo Q, Yang QC(*). An interpretable machine learning-based optimization framework for the optimal design of carbon dioxide to methane process. Energy Conversion and Management 2024, 320, 119010. (IF=9.9, 一区TOP)
[4] Yang QC(*), Zhou J, Bao R, Rong D, Zhao L, Zhang D. Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm. Chemical Engineering Science 2024, 120295. (IF=4.1, 化工三大刊)
[5] Yang QC(*), Bao R, Rong D, Xiao J, Zhou J, Zhao L, Zhang D. (2024). Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures. Industrial & Engineering Chemistry Research, 2024, 63, 14727-14747. (IF=3.8, 化工三大刊)