基于高斯过程的煤元素分析全成分含量预测研究

Prediction of comprehensive elemental compositions of coal based on Gaussian process

利用煤的工业分析成分预测元素分析成分存在较多困难,现有文献给出的模型通常只对C、H和O等主要元素成分含量进行预测。而高斯过程能够解决复杂的机器学习问题,且可以对预测不确定性进行有效估计。该文建立基于高斯过程的煤元素含量预测模型,选用干燥无灰基挥发分和高位发热量作为随机过程的索引变量,分别对C、H、N、S元素含量进行高斯过程建模,O元素含量采用差减法得到,从而实现对元素分析全部成分含量的预测。采用10折交叉验证法对模型进行检验。结果表明,C、H、N和O元素含量预测的平均误差分别为1.46%、6.97%、16.80%和14.28%,但对于高硫煤,S元素含量的预测误差偏大,使用模型时应加以注意。

It is difficult to predict elemental compositions of coal using proximate analysis so far,and the models given in the existing literature usually predict the contents of the main elements such as C,H and O.Gaussian process models are routinely used to solve hard machine learning problems,and can effectively estimate the prediction uncertainty.In this paper,models based on Gaussian process are established to predict comprehensive elemental compositions of coal using proximate analysis.The dry ash free volatile matter and dry ash free basis high calorific value being selected as index variables,the Gaussian processes modeling are proposed to carbon,hydrogen,and oxygen compositions,respectively,and oxygen content is calculated by subtraction method,which realizes the prediction of all components in element analysis of coal.The model was tested and verified by 10 fold cross validation,results show that the averaged prediction errors of C,H,N and O are 1.46%,6.97%,16.80%and 14.28%,respectively.For coals with too high or low sulfur,the prediction error of S content is high,which means that one should be very careful to deploy the presented models to predict the elemental composition of coal with high or low sulfur contents.