Data-Driven Thermal Efficiency Modeling and Optimization for Co-firing Boiler
The changes of flow rate and heating value of blast furnace gas(BFG)make the boiler operation more like art than science.In this paper,statistics analysis methods are utilized to justify the significance of the derived variables for the thermal efficiency modeling.By employing nonnegative garrote(NNG)variable selection and auto-regression integrated moving average(ARIMA)correction,an adaptive scheme for thermal efficiency modeling and adjustment is proposed and virtually implemented for a BFG/coal co-firing boiler.The detail analysis shows that there is large room for energy conservation when the boiler operation shifts from the present practice to the model-based control.
Multi-fuel boiler thermal efficiency variable selection data-driven statistics analysis
Jian-Guo Wang Juan-Juan Wang Qian-Ping Xiao Shi-Wei Ma Wen-Tao Rao Yong-Jie Zhang
School of Mechatronical Engineering and Automation,Shanghai University,Shanghai Key Lab of Power Sta Institute of Environment & Resources,Baosteel Research Institute,Shanghai,201900
国际会议
长沙
英文
3608-3611
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）