垃圾填埋场会产生大量的填埋气,其中40%~60%为CO2,CO2会导致温室效应并降低填埋气的利用效率,因此掌握及预测填埋气中CO2的释放规律对于控制其扩散及填埋场温室气体排放评估具有重要意义.本文分析了杭州某填埋场2018年全年CO2浓度,发现CO2与H2S的浓度在春秋季表现出强线性相关性,秋季的皮尔逊相关性系数绝对值为0.77.同时,建立多层感知器(multi layer perceptron,MLP)的人工神经网络模型预测CO2浓度,并选取箱型图法对数据进行前处理以剔除监测设备故障等非自然因素导致的异常值,选取PM2.5、风速、风向、气温、空气湿度作为输入指标,结果表明模型预测结果与现场实测结果约为R=0.7,说明模型效果较好.基于该模型对缺失的9月份数据进行填补,其结果与CO2全年释放规律吻合良好.本文基于现场监测数据建立的神经网络模型可用于填埋场CO2释放的预测评估,对CO2等填埋气释放的控制和填埋场现场管理具有指导意义.
Landfills produce a large amount of landfill gas,40%-60%of which is CO2.CO2 causes greenhouse effect and reduces the efficiency of landfill gas utilization.Understanding the emission mechanisms of CO2 from the landfill is key to control its dispersion and release.This study inverstigated the CO2 concentration in 2018 of a landfill in Hang-zhou.The concentration of CO2 and H2S showed a strong linear correlation in spring and autumn,with the absolute val-ue of the Pearson correlation coefficient of 0.77 in autumn.Meanwhile,an artificial neural network model based on the multi layer perceptron(MLP)was developed to predict and analyze the concentration of CO2.In order to eliminate the abnormal values,box chart method was used to screen the concentration data for the whole year of 2018.Five in-put indicators including PM2.5,wind speed,wind direction,air temperature and air humidity were finally chosen as the input variables through combination and comparison.The results indicate that the predicted results of the model are in agreement with the field measurement results with about R=0.7.In addition,the missing data in September was ob-tained and the results were consistent with the annual CO2 release law.The MLPNN model established based on the on-site monitoring data can be used to predict and evaluate the carbon dioxide release,which is of great significance for the control of CO2 and other landfill gas emission.