The accurate prediction of coal carbon emissions is an important basis for achieving China's"dual carbon"goal.Completing the accurate and fast prediction of carbon emissions and coal quality indicators before coal entering the furnace will transform the current method of calculating carbon emis-sions data from"emission first,then detection,"and is essential for implementing the"dual carbon"strategy.Our study aims to accurately evaluate the coal carbon content and coal quality indicators.We build a laser-induced breakdown spectroscopy(LIBS)system and use principal component analysis-partial least squares(PCA-PLS)combined with small sample algorithms to regressively train the train-ing set.A prediction model of coal carbon emission is established to demonstrate the reliability of the LIBS system in the analysis of coal quality indicators through its practical application in the A power plant in Xinjiang and the B power plant in Shandong.The results show that the proposed prediction method has a maximum absolute error of 0.025 6 t compared to the traditional method,demonstrating good prediction capabilities for carbon emission.It can be adopted for the accurate prediction of carbon emissions in coal-fired enterprises and provides a relatively efficient technical solution for the pre-as-sessment of coal carbon emissions and coal quality indicators.
关键词
激光诱导击穿光谱技术/碳排放/高精度快速预测
Key words
Laser-Induced Breakdown Spectroscopy(LIBS)/carbon emission/high precision and fast prediction
维普
万方数据