基于多元线性回归模型的生物质与烟煤混燃灰熔融特征温度预测

Prediction of the characteristic fusion temperature of co-combustion ash of biomass and bituminous coal using multiple linear regression model

生物质中钾、钠等碱金属含量高,极易导致生物质与煤混燃灰沾污结渣严重。灰熔融特征温度是用于表征积灰结渣倾向的重要指标。该研究在充分考虑灰中酸/碱性组分对熔融特征温度影响的基础上,以生物质与烟煤混燃灰中Al_(2)O_(3)、SiO_(2)、P_(2)O_(5)、SO_(3)、K_(2)O、CaO、Fe2O_(3)7种氧化物作为变量,利用Matlab软件建立了基于多元线性回归模型的生物质与烟煤混燃灰熔融特征温度预测模型,并以中国农村典型的玉米秸秆和神木烟煤为试样,测定其在不同掺混比例、不同温度和不同停留时间下的灰分组成及熔融特征温度。试验结果表明:随着混合燃料中玉米秸秆的质量分数由25%增至75%时,灰样中MgO、K_(2)O、CaO、Na2O等碱性氧化物的含量增加,特别是对于K_(2)O而言,其质量分数由5.89%升至14.41%,而Al_(2)O_(3)、P_(2)O_(5)和SO_(3)等酸性氧化物的含量逐渐减少,其中Al_(2)O_(3)的质量分数由12.05%降至7.78%,P_(2)O_(5)的质量分数由3.66%降至1.07%,SO_(3)的含量由7.70%降至1.48%。随着灰化温度升高和停留时间延长,Cl元素的含量明显减少。将该模型的预测结果与经验公式预测结果及试验结果对比,发现该预测模型中P_(2)O_(5)、SO_(3)对灰熔融特征温度的影响系数较大,这与试验结果基本相符。灰熔融特征温度与Al_(2)O_(3)、SiO_(2)、P_(2)O_(5)、CaO等氧化物含量呈正相关,表明这些氧化物成分有助于抑制熔融结渣。利用试验测量及经验公式等方法对该预测模型的结果进行检验,利用该模型预测的熔融特征温度与试验值的误差在5%以内,验证了该模型的准确性和可靠性。该模型研究结果可为准确预测生物质与烟煤混燃灰熔融特征温度以及防治锅炉积灰结渣提供参考。

A large amount of biomass wastes have been generated during agricultural production nowadays.The combustion and gasification can be expected to convert the biomass into clean energy,such as gas,electricity,and heat.Among them,the characteristic temperature of ash fusion is one of the important indexes for the tendency of slagging.Since the biomass ash is a mixture of a variety of minerals,there is no fixed melting point within a certain temperature range.In general,ash fusion temperatures are commonly characterized by four characteristic temperatures,namely the deformation,softening,hemisphere,and flow temperature.Few studies have been conducted to predict the ash fusion temperature of biomass and coal cocombustion ash.Most of them only considered the role of alkaline components in the ash,particularly without the influence of acidic components(SO_(3) and P_(2)O_(5)).This investigation aims to fully consider the effects of acidic/alkaline components in ash on the ash fusion temperature.Seven oxides were taken as the variables,including Al_(2)O_(3),SiO_(2),P_(2)O_(5),SO_(3),K_(2)O,CaO,and Fe2O_(3) in biomass and coal co-combustion ash.A prediction model was established for the ash fusion temperature of biomass and coal cocombustion ash using a multiple linear regression model in the Matlab software.Besides,taking the corn straw and Shenmu bituminous coal in rural areas of China as examples,the ash composition and ash fusion temperatures were measured under different mixing ratios,temperatures,and residence times.The results showed that the content of MgO,K_(2)O,CaO,Na2O,Fe2O_(3),and MnO oxides increased with the increase of the content of corn straw from 25%to 75%.Especially for K_(2)O,the content increased from 5.89%to 14.41%.The content of acidic oxides decreased gradually,such as Al_(2)O_(3),P_(2)O_(5),and SO_(3),among which the content of Al_(2)O_(3) decreased from 12.05%to 7.78%,the content of P_(2)O_(5) decreased from 3.66%to 1.07%,and the content of SO_(3) decreased from 7.70%to 1.48%.The Cl content decreased outstandingly with the increase of ashing temperature and the extension of residence time.A comparison was made between the prediction and the experimental values,according to the existing empirical formula.It was found that the influence coefficients of P_(2)O_(5) and SO_(3) on the characteristic ash fusion temperature were large in the prediction model,which was consistent with the test.It inferred that there was a significant effect of acidic components,such as P_(2)O_(5) and SO_(3),on the ash fusion temperatures.Actually,the ash fusion temperature was positively correlated with the content of oxides,such as Al_(2)O_(3),SiO_(2),P_(2)O_(5),and CaO,indicating a great contribution to the inhibition of ash melting and slagging.There was an error of less than 5%in the characteristic ash fusion temperatures between the prediction of the model and the experiment,indicating the better fitting of the multiple linear regression.The accuracy and reliability of the model were verified as well.