流化床垃圾焚烧炉烟气停留时间计算及预测

保证焚烧烟气在大于850 ℃区域内停留2 s以上是保证垃圾稳定燃烧和避免二次污染的重要途径，但目前只采用炉膛出口热电偶测温对其定性评估，难以定量计算和预测烟气在高温区域停留时间. 本研究基于热力学计算方法、运行参数关联性分析和多种机器学习算法（反向传播神经网络、循环神经网络、随机森林算法），对我国某典型生活垃圾循环流化床焚烧锅炉开展了烟气高温段（>850 ℃）停留时间计算、关键运行参数关联计算和停留时间预测模型构建等研究. 结果表明，炉膛温度、一二次风温度和压力等10个关键运行参数与高温烟气停留时间具有强关联性和预测性. 循环神经网络预测模型相对最优，其拟合度及准确性较反向神经网络、随机森林算法更高，均方根误差(MSE)为0.11626，预测值与真实值的平均绝对误差为1.174%. 本研究可以用于预测炉内高温区域烟气温度变化，为炉内焚烧工况优化和污染物减排超前调控提供支撑.

Calculation and prediction of flue gas residence time from CFB municipal solid waste incinerator

Ensuring that the flue gas in the furnace stays within the temperature range of no less than 850 ℃ for at least 2 s contributes to the steady municipal solid waste (MSW) incineration, and the reduction of secondary pollution. However, at present, it is difficult to quantitatively calculate and predict the residence time of flue gas in the high temperature area by only using the thermocouple for qualitative evaluation. Based on the thermodynamic calculation, correlation analysis of practical operation parameters, and a variety of machine learning algorithms (backpropagation neural network, recurrent neural network, and random forest regression), the residence time of flue gas in high-temperature areas (>850 ℃) was calculated, correlation analysis of key operation parameters was conducted, and the prediction model of residence time was constructed, aiming at a typical MSW circulating fluidized bed boiler in China. Results revealed that 10 key operating parameters, e.g. section temperature of the furnace, temperature and pressure of primary air and secondary air, etc., had a strong correlation and predictability with the high-temperature flue gas residence time. Moreover, the model of the recurrent neural network was relatively optimal, with a higher fitting degree and accuracy. Specifically, the mean square error (MSE) was 0.11626, and the average absolute error between the predicted value and real value was 1.174%. Research enabled the prediction of flue gas temperature variation in high-temperature areas, helped optimize the MSW incineration, and contributed to the advanced control of pollutant emission reduction.