摘要
随着我国经济的快速发展,城镇化进度越来越快,污泥(sewage sludge,SS)产量急剧增加。污泥中含有多种有害物质,若不妥善处置会严重污染生态环境,威胁人类身体健康。焚烧是最直接有效的污泥处置方式之一,掺混生物质能有效提高污泥燃烧性能和排放,与城市生活垃圾共燃可同时实现城市生活垃圾和污泥的资源化和减量化。人工神经网络(artificial neural network,ANN)具有拟合高度复杂非线性关系的能力,目前在燃烧实验结果预测方面有较好应用。本文将污泥和湿垃圾(wet waste,WW)进行掺混,分别从热重分析(thermogravimetric analysis,TGA)、傅立叶变换红外光谱(Fourier transform infrared spectroscopy,FTIR)、动力学等方面对污泥、湿垃圾及其混合物的共燃烧特性进行了实验研究,通过人工神经网络模型预测样品热重数据,得到了ANN模型和样品剩余质量百分比之间的明确关系。结果表明:污泥和湿垃圾的燃烧行为存在明显差异,湿垃圾的掺入可以显著提升混合物的燃烧性能,降低混合物反应活化能,改善污泥燃烧状况,促进污泥的燃烧和气体产物的形成。ANN模型预测数据可以用来进行实验评估和共燃烧反应器设计,对降低污泥和湿垃圾工业化应用的成本非常有利。
With the rapid development of China′s economy and accelerating urbanization,the production of Sewage Sludge(SS)has increased dramatically.Sewage sludge contains a variety of harmful substances,and improper disposal can severely pollute the environment and pose significant risks to human health.Incineration is one of the most direct and effective methods for sludge disposal.Blending sewage sludge with biomass can effectively improveits combustion performance and reduce pollutant emissions.Co-combustion with municipal solid waste can not only improves the combustion efficiency of sewage sludge but also promotes resource utilization and volume reduction of both municipal solid waste and sludge..Artificial neural networks(ANN),known for their ability to model non-linear relationships,have been widely and effectively applied to predict combustion experiment results.In this paper,sewage sludge and wet waste(WW)were blended,and their co-combustion characteristics were investigated experimentally using thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and kinetics analysis.An artificial neural network model was employed to predict the thermogravimetric data of the samples,and a clear relationship between ANN model and residual mass percentage was obtained.The results show obvious differences in the combustion behavior of sewage sludge and wet waste.The addition of wet waste can significantly improve the combustion performance,reduce activation energy,enhance sludge combustion efficiency,and influences the formation of gaseous products.The ANN model’s predictive capabilities offer valuable insights for experimental evaluation and co-combustion reactor design,thereby reducing the industrial application costs associated with sewage sludge and wet waste disposal.
作者
周宇
林其钊
ZHOU Yu;LIN Qizhao(School of Physics and Electrical Engineering,Liupanshui Normal University,Liupanshui 553004,China;School of Engineering Science,University of Science and Technology of China,Hefei 230026,China)
出处
《热科学与技术》
CSCD
北大核心
2024年第6期541-550,共10页
Journal of Thermal Science and Technology
基金
国家重点研发计划资助项目(2021YFF0601004)
贵州省科技厅科学技术基金资助项目(黔科合基础-ZK[2021]一般280)
六盘水师范学院校级基金资助项目(LPSSYKYJJ201813、LPSSYKYJJ201815)
六盘水红果开发区龙鼎工贸有限公司资助项目(LD-LPSSY-ZY-2021-01)
关键词
污泥
湿垃圾
共燃
热重分析
人工神经网络
sewage sludge
wet waste
co-combustion
thermogravimetric analysis
artificial neural network
