摘要
为了解决低熔点含氟氯类危险废物(简称"危废")在回转窑焚烧炉内燃烧时,灰渣熔化导致窑内结圈的问题,实验采用氧化物分析纯(SiO2、Al2O3、CaO、Fe2O3、Al2O3)和NaCl、KCl、NaF分析纯来模拟危废炉渣组分,研究了危废焚烧炉灰渣的熔融特性,并采用神经网络预测危废灰渣的熔融温度.结果表明,SiO2、Al2O3和CaO能够提高危废炉渣的熔融温度,Na2O会大幅度降低危废炉渣的熔融温度,NaF对危废炉渣熔融温度的影响与NaF的含量有关.神经网络计算结果显示,危废灰渣熔融温度的预测值和实验值吻合较好.
While the hazardous wastes containing fluorine and chlorine were incinerated in the rotary kiln incinerator,the slag was melted because of the low fusion temperature.The molten slag stuck on the inner side of rotary kiln,leading to the instable operation of the incinerator.Analytical reagents （SiO2,Al2O3,CaO,Fe2O3,Al2O3,NaCl,KCl and NaF） were substituted for slag components of this kind of hazardous waste to investigate the melting characteristics.Neural network analysis was used to predict the slag fusion temperatures of hazardous wastes containing fluorine and chlorine.The experimental results showed that SiO2,Al2O3 and CaO increased the slag fusion temperature,and Na2O decreased fusion temperature sharply.Effect of NaF on slag fusion temperature of this kind of hazardous waste was dependent on the content of NaF.Neural network-predicted slag fusion temperatures agreed well with the experimental values.
出处
《环境科学学报》
CAS
CSCD
北大核心
2011年第11期2499-2505,共7页
Acta Scientiae Circumstantiae
基金
国家重点基础研究发展计划(973)项目(No.2011CB201500)
国家高技术研究发展计划(863)项目(No.2009AA064704)
国家科技支撑计划项目(No.2007BAC27B04-3)~~
关键词
危险废物
化学组分
灰渣熔融温度
神经网络
hazardous waste
chemical components
slag fusion temperature
neural networks
