生物质混煤的灰熔融特性研究
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摘要
近年来,由于能源与环境问题日益突出,人们开始对诸如生物质之类的可再生能源进行研究,以期实现生物质的清洁、高效利用。由于生物质碱性物含量较大,熔融温度较低,且软化温度和流动温度相差较小,致使生物质锅炉及生物质混煤燃烧锅炉易发生积灰、结渣和腐蚀等问题,因此研究生物质及生物质混煤的灰熔融特性等关键基础科学问题会加快解决生物质能发电锅炉的积灰、腐蚀和结焦等问题,促使我国尽快具备生物质能发电系列锅炉的生产、研发和设计能力。
首先,本文在TGA/SDTA851e热分析系统上,对麦秆、稻秆、玉米秆、棉秆、花生壳、杨木屑、酒糟和造纸废液浓缩颗粒等8种生物质的灰熔融特性做了试验研究。由于各种生物质灰成分和矿物组成的差异性,所以通过热分析方法所表现出来的熔融特性有着明显的差异性。碳酸盐和硫酸盐的分解以及碱金属氧化物和碱金属盐的蒸发是造成生物质灰升温失重的主要原因。DSC曲线的吸热峰多是由于碳酸盐分解和碱金属的硅酸盐和硅铝酸盐熔融造成的。根据生物质灰中各种化学元素在灰熔融过程中的迁移规律,应用回归分析方法构造的失重率函数能较好的反映出生物质灰中各种元素化合物的失重规律,方程的线性相关性好,回归效果显著。
其次,本文在YX-HRD灰熔融性测定仪上研究了生物质以不同配比混煤时对灰熔融特征温度的影响规律。由于煤的灰分含量比生物质的灰分含量大,所以生物质混煤灰熔融特性在小配比范围内基本体现了煤灰的成分特点和熔融特性。随着生物质配比的增加,生物质混煤灰熔融特征温度与配比之间在总体上是呈现非线性的关系,但是在部分配比范围内可出现近似线性关系。通过单生物质混煤的灰熔融性试验研究发现,在小配比范围内生物质降低煤灰熔融特征温度能力的排序为:(稻秆、麦秆)＞(玉米杆、酒糟、棉秆)＞(杨木屑、花生壳)。在双生物质混煤的灰熔融性试验中研究发现,双生物质对煤灰熔融性特征温度的影响基本体现了两种生物质的共同作用。通过添加剂对生物质灰熔融性的影响试验,验证了灰熔融特征温度随着Al_2O_3含量的增加而增加,而SiO_2对了灰熔融特征温度的影响不明显,但是当含量过高时易发生烧结现象。CaO和MgO对麦秆灰在小配比范围内起到了“骨架”作用,而当配比过大时灰熔融性特征温度增加明显。
根据得到的在不同配比下生物质混煤灰熔融特征温度数据,通过回归分析方法得出灰组分与特征温度之间的多元线性回归模型及二次多项式回归模型,由回归分析结果得出,由11个灰组分变量合并得到的5个变量的二次多项式回归模型的显著性明显优于线性回归模型。
最后,本文分别采用普通径向基神经网络和广义回归神经网络对生物质混煤灰熔融特征温度进行了建模,用190组样本数据对网络进行学习,用8组测试数据对网络的有效性进行检验。网络仿真结果表明广义回归神经网络与普通径向基神经网络相比,具有更好的预测能力和泛化能力,预测结果满足误差要求,适合用来对生物质配煤灰熔融特征温度进行建模预测。
In recent years, due to the problem of energy crisis and environment pollution become more and more serious, people began to research the biomass energy to use cleanly and efficiently. Because biomass has high alkaline substance content, low ash fusion temperature and small difference between softening temperature and flowing temperature, the boiler have the problems of fouling, slagging and corrosion when burning biomass and blending with coal. So the studying on the ash fusion property of biomas and blending with coal can speed up resolving the problems of fouling, slagging and corrosion of biomass boiler, and prompt our country to have the ability of produce, reaserch and design of biomass boiler.
First of all, a thermal analyzer modeled TGA/SDTA851e was used to study the ash fusion property of eight kinds of biomass, such as wheat straw, rice straw, corn stalk, cotton stalk, peanut shell, poplar chip, vinasse and paper sullage. Because of the differences in ash chemical composition and mineral composition of the biomass, there were obvious differences in ash fusion property through the thermal analysis methods. Thermal analysis indicates that the principal causes of weight loss of the biomass ashes are the decomposition of carbonates and sulfates and the evaporation of alkali oxides and alkali metal salts. The endothermic peaks on the DSC curves are mainly caused by the decomposition of carbonates and the melting of alkali silicates and alkali aluminosilicates. According to the migration regularity of chemical element during ash fusing, the function of weight loss rate, which deduced by regression analysis method, can reflect the weightless regulation of most element contents of biomass ashes.
Secondly, the effects of biomass proportions on the ash fusion temperature of biomass and coal blending were researched through YX-HRD ash fusion analyzer. Because coal has higher ash content than biomass, ash fusion property of biomass and coal blending reflects the ash composition and fusion prpperty of the coal. With the increment of the biomass proportions, the relationship between ash fusion temperature and the biomass proportions is nonlinear in general, but the relationship is approximate to linear property in part proportions ranges. Through experimental research on ash fusion property of single biomass and coal blending, it is found that the sequencing of ability of biomass to reduce coal ash fusion temperature is ( rice straw, wheat straw ) >( corn stalk, vinasse, cotton stalk )>( poplar chip, peanut shell ) . Through experimental research on ash fusion property of double biomass and coal blending, it is found that the effect of double biomass on coal ash fusion property basically embodies the combined action of double biomass. Through the influence test of additives on biomass ash fusion temperature, it is verified that ash fusion temperature increases with increasing content of Al_2O_3, while the effect of SiO_2 on ash fusion temperature is not obvious, but the ash will be sintered with a higher content of SiO_2. CaO and MgO play a role as skeleton at low content, and ash fusion temperature is significantly increased with a higher content.
According to the ash fusion temperature of biomass and coal blending in different proportions, multiple linear regression model and quadratic polynomial regression model between ash chemical composition and ash fusion temperature were estimated. Regressive analysis shows that the significance of quadratic polynomial regression model, which has five independent variables merged from eleven independent variables, is obviously better than multiple linear regression model.
At last, the radial basis function neural network (RBFNN) and the generalized regressive neural network (GRNN) were applied to build models for forecasting the ash fusion temperature of biomass and coal blending. 190 group samples were used to train and 8 group samples were used to check up the validity of the network. The simulated result shows that GRNN has better predictive ability and generalization ability than RBFNN, and the prediction results of GRNN satisfy the error requirement. So GRNN is suitable to be used to build models for forecasting the ash fusion temperature of biomass and coal blending.
首先,本文在TGA/SDTA851e热分析系统上,对麦秆、稻秆、玉米秆、棉秆、花生壳、杨木屑、酒糟和造纸废液浓缩颗粒等8种生物质的灰熔融特性做了试验研究。由于各种生物质灰成分和矿物组成的差异性,所以通过热分析方法所表现出来的熔融特性有着明显的差异性。碳酸盐和硫酸盐的分解以及碱金属氧化物和碱金属盐的蒸发是造成生物质灰升温失重的主要原因。DSC曲线的吸热峰多是由于碳酸盐分解和碱金属的硅酸盐和硅铝酸盐熔融造成的。根据生物质灰中各种化学元素在灰熔融过程中的迁移规律,应用回归分析方法构造的失重率函数能较好的反映出生物质灰中各种元素化合物的失重规律,方程的线性相关性好,回归效果显著。
其次,本文在YX-HRD灰熔融性测定仪上研究了生物质以不同配比混煤时对灰熔融特征温度的影响规律。由于煤的灰分含量比生物质的灰分含量大,所以生物质混煤灰熔融特性在小配比范围内基本体现了煤灰的成分特点和熔融特性。随着生物质配比的增加,生物质混煤灰熔融特征温度与配比之间在总体上是呈现非线性的关系,但是在部分配比范围内可出现近似线性关系。通过单生物质混煤的灰熔融性试验研究发现,在小配比范围内生物质降低煤灰熔融特征温度能力的排序为:(稻秆、麦秆)＞(玉米杆、酒糟、棉秆)＞(杨木屑、花生壳)。在双生物质混煤的灰熔融性试验中研究发现,双生物质对煤灰熔融性特征温度的影响基本体现了两种生物质的共同作用。通过添加剂对生物质灰熔融性的影响试验,验证了灰熔融特征温度随着Al_2O_3含量的增加而增加,而SiO_2对了灰熔融特征温度的影响不明显,但是当含量过高时易发生烧结现象。CaO和MgO对麦秆灰在小配比范围内起到了“骨架”作用,而当配比过大时灰熔融性特征温度增加明显。
根据得到的在不同配比下生物质混煤灰熔融特征温度数据,通过回归分析方法得出灰组分与特征温度之间的多元线性回归模型及二次多项式回归模型,由回归分析结果得出,由11个灰组分变量合并得到的5个变量的二次多项式回归模型的显著性明显优于线性回归模型。
最后,本文分别采用普通径向基神经网络和广义回归神经网络对生物质混煤灰熔融特征温度进行了建模,用190组样本数据对网络进行学习,用8组测试数据对网络的有效性进行检验。网络仿真结果表明广义回归神经网络与普通径向基神经网络相比,具有更好的预测能力和泛化能力,预测结果满足误差要求,适合用来对生物质配煤灰熔融特征温度进行建模预测。
In recent years, due to the problem of energy crisis and environment pollution become more and more serious, people began to research the biomass energy to use cleanly and efficiently. Because biomass has high alkaline substance content, low ash fusion temperature and small difference between softening temperature and flowing temperature, the boiler have the problems of fouling, slagging and corrosion when burning biomass and blending with coal. So the studying on the ash fusion property of biomas and blending with coal can speed up resolving the problems of fouling, slagging and corrosion of biomass boiler, and prompt our country to have the ability of produce, reaserch and design of biomass boiler.
First of all, a thermal analyzer modeled TGA/SDTA851e was used to study the ash fusion property of eight kinds of biomass, such as wheat straw, rice straw, corn stalk, cotton stalk, peanut shell, poplar chip, vinasse and paper sullage. Because of the differences in ash chemical composition and mineral composition of the biomass, there were obvious differences in ash fusion property through the thermal analysis methods. Thermal analysis indicates that the principal causes of weight loss of the biomass ashes are the decomposition of carbonates and sulfates and the evaporation of alkali oxides and alkali metal salts. The endothermic peaks on the DSC curves are mainly caused by the decomposition of carbonates and the melting of alkali silicates and alkali aluminosilicates. According to the migration regularity of chemical element during ash fusing, the function of weight loss rate, which deduced by regression analysis method, can reflect the weightless regulation of most element contents of biomass ashes.
Secondly, the effects of biomass proportions on the ash fusion temperature of biomass and coal blending were researched through YX-HRD ash fusion analyzer. Because coal has higher ash content than biomass, ash fusion property of biomass and coal blending reflects the ash composition and fusion prpperty of the coal. With the increment of the biomass proportions, the relationship between ash fusion temperature and the biomass proportions is nonlinear in general, but the relationship is approximate to linear property in part proportions ranges. Through experimental research on ash fusion property of single biomass and coal blending, it is found that the sequencing of ability of biomass to reduce coal ash fusion temperature is ( rice straw, wheat straw ) >( corn stalk, vinasse, cotton stalk )>( poplar chip, peanut shell ) . Through experimental research on ash fusion property of double biomass and coal blending, it is found that the effect of double biomass on coal ash fusion property basically embodies the combined action of double biomass. Through the influence test of additives on biomass ash fusion temperature, it is verified that ash fusion temperature increases with increasing content of Al_2O_3, while the effect of SiO_2 on ash fusion temperature is not obvious, but the ash will be sintered with a higher content of SiO_2. CaO and MgO play a role as skeleton at low content, and ash fusion temperature is significantly increased with a higher content.
According to the ash fusion temperature of biomass and coal blending in different proportions, multiple linear regression model and quadratic polynomial regression model between ash chemical composition and ash fusion temperature were estimated. Regressive analysis shows that the significance of quadratic polynomial regression model, which has five independent variables merged from eleven independent variables, is obviously better than multiple linear regression model.
At last, the radial basis function neural network (RBFNN) and the generalized regressive neural network (GRNN) were applied to build models for forecasting the ash fusion temperature of biomass and coal blending. 190 group samples were used to train and 8 group samples were used to check up the validity of the network. The simulated result shows that GRNN has better predictive ability and generalization ability than RBFNN, and the prediction results of GRNN satisfy the error requirement. So GRNN is suitable to be used to build models for forecasting the ash fusion temperature of biomass and coal blending.
引文
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