中文摘要：

选取对烟气汞排放影响显著的特征参数，采用一类新的随机过程方法——高斯过程回归模型来预测烟气中单质汞、氧化汞和颗粒汞的排放浓度，分别讨论了协方差函数和样本比例对模拟预测精度的统计学影响．结果表明：平方指数协方差函数优于有理二次协方差函数和Matern协方差函数；预测精度随样本比例的增大而提高；高斯过程回归模型优于常规非线性模化方法并显示出更好的鲁棒性，对烟气中汞的形态预测有较好的适用性．

英文摘要：

By selecting the parameters which significantly influence the mercury emission from coal-fired flue gas, the concentration of elemental, oxidized and particulate mercury in flue gas was predicted using Gaussian process regression, a new random process method, while the effects of covariance function and train-test sample ratio on the simulation accuracy were respectively studied. Results show that the squared exponential covariance function is better than rational quadratic and Matern covariance function; the predicted accuracy increases with the rise of train-test sample ratio; Gaussian process regression is superior to traditional modeling methods of nonlinear regression, and displays good generalization ability, which therefore has strong applicability in prediction of mercury speciation in coal-fired flue gas.