模糊模式识别在水煤浆锅炉结渣特性判别上的应用

该文基于模糊数学原理，采用模式识别方法，尝试性地提出将综合指数R与4个常规结渣指标(软化温度T2、硅比G、硅铝比SiO2/Al2O3和碱酸比B/A)一起构成评判因素集，对某燃料水煤浆灰和取自该锅炉不同部位的另3个样品(炉渣、转向室灰和除尘灰)进行了结渣倾向性判别，以验证该模型的可靠程度。通过沾污特性分析和粘聚特性试验及与同一模型的模糊综合评判对照，结果表明该新模型较以前的四因素法具有更高的准确性，从而保证了判别结果的可靠性。同时也证明了实验室煤灰明显有别于现场锅炉煤灰。

APPLICATION OF FUZZY PATTERN IDENTIFICATION FOR SLAGGING CHARACTERISTICS JUDGEMENT IN COAL-WATER-SLURRY FIRED BOILER

Based on the principle of fuzzy mathematics, five coal slagging indices, such as integrated index R, softening temperature T2, silicon ratio G , ratio of SiO2 to Al2O3 and ratio of alkali to acid are collected to build a new model in a pattern identification method. The new model has been applied to predict the slagging tendency of a kind of Coal-Water-Slurry (CWS) ash, as well as the bottom ash, the reversing chamber ash and the dust precipitator ash all of which are sampled from different locations of a CWS-fired boiler. The analysis of fouling characteristics and test of cohesion performance shows that this new model has an accuracy much higher than the traditional four-factor model. In addition, it is proved by experiments that the coal ash prepared in the laboratory is much different from that obtained practically from a utility boilers.