上海市生活垃圾低位热值特性预测研究  

Research on Prediction of Lower Heat Value(LHV) Characteristics of Municipal Solid Waste in Shanghai

作　　者：杜星宇 牛冬杰[1,2] Du Xingyu;Niu Dongjie(State Key Laboratory of Pollution Control and Resource Reuse,Tongji University,Shanghai 200000;Shanghai Institute of Pollution Control and Ecological Security,Tongji University,Shanghai 200000,China)

机构地区：[1]同济大学污染控制与资源化研究国家重点实验室同济大学,上海200000 [2]上海污染控制与生态安全研究院同济大学,上海200000

出　　处：《广东化工》2022年第3期120-122,142,共4页Guangdong Chemical Industry

基　　金：国家重点研发计划固废资源化重点专项“生活垃圾分类回收模式与智慧环卫关键设备”(2018YFC1900701)。

摘　　要：生活垃圾热值的大小影响着垃圾焚烧发电厂的锅炉运行和发电效率,关乎焚烧工艺及设备的选型。本文收集并整理了国内外三大类垃圾热值计算的经验公式,以上海市生活垃圾热值数据为例,提出了建立广义回归神经网络模型(GRNN)对垃圾热值进行预测的方法,并与传统经验公式进行了对比。结果表明:垃圾热值与各组分具有非线性回归关系,GRNN可以有效弥补传统经验公式的短板,实现对垃圾热值的高精度预测。The heat value of domestic waste affects the boiler operation and power generation efficiency of waste incineration power plants, and it is related to the selection of incineration technology and equipment. This paper collects and sorts out the empirical formulas for calculating the heat value of three major types of garbage at home and abroad. Taking the heat value data of Shanghai domestic garbage as an example, a method for establishing a generalized regression neural network model(GRNN) to predict the heat value of garbage is proposed. Contrast with traditional empirical formula. The results show that: the heat value of waste has a non-linear regression relationship with each component, GRNN can effectively make up for the shortcomings of traditional empirical formulas, and achieve high-precision prediction of the heat value of waste.

关 键 词：焚烧处理 低热值 Lin公式 广义回归神经网络 预测模型 

分 类 号：X799.3[环境科学与工程—环境工程]