北京市城市生活垃圾组分预测  

On prediction of municipal solid waste classification in Beijing

作　　者：周翠红[1] 路迈西[2] 吴文伟[3] 

机构地区：[1]北京石油化工学院环境工程系,北京102617 [2]中国矿业大学北京校区化学与环境工程学院,北京100083 [3]北京市环境卫生设计科学研究所,北京100028

出　　处：《安全与环境学报》2004年第5期37-40,共4页Journal of Safety and Environment

摘　　要：研究了北京市 1991— 1999年城市生活垃圾组分及变化特点。发现食品的含量最大 ,其次是塑料和纸类。以 2 0世纪 90年代生活垃圾产量和组分数据为基础 ,建立了生活垃圾组分人工神经网络预测模型。以预测的生活垃圾产量为输入值 ,分别对 6个不同区域 2 0 0 1年、2 0 0 5年、2 0 0 8年与 2 0 10年的生活垃圾组分进行了预测 ,并用类比法进行了修正。考虑到由于城市发展规划不同区域生活垃圾比例变化情况 ,最终计算出全市生活垃圾组分变化。结果表明 ,在未来几年内北京市城市生活垃圾组分中 ,食品含量较高 ,但其比例逐年下降 ,纸类与塑料垃圾有增加 。The paper intends to introduce the author's proposed method on classification of the components of Municipal Solid Waste (MSW) in Beijing. There are a lot of complicated factors that may influence the municipal garbage classification. As a common practice, municipal garbage divided into eight categories, such as food, plastics, paper, glass, grass, dust, textile, tile and metal. In order to scale different living standards, the components of MSW in Beijing are listed in six areas, that is, the double-gas residential area, advanced residential area, commercial area, hospital area, business and enterprise area and bungalow area. Analyzing the MSW components in six kinds of areas in Beijing (1991-1999) and their different changing tendency, we have found that MSW components in six kinds of areas are quite different from each other. As a common practice, food waste is of the main component, and, then, plastics, and paper. While the food waste content in the double-gas residential area ranks the top waste, the dust content in bungalow area is the top one. As the plastics waste grows higher and higher in all the six kinds of areas year by year, the content of paper remains unchanged in 1990s. However, it is difficult to form a general model of prediction for the Municipal Solid Waste Components because it involves too many data to be done. In spite of this difficulty, the prediction model was still established by the artificial neural network on the basis of the yield and components of MSW in 1990s with the wide application of BP artificial neural network. With the computer-aided data-processing software, this model has been easily established and modified by newly input data. Thus, six prediction models were constructed on the basis of the learning models to predict the MSW components of six different areas in 2001, 2005, 2008 and 2010 respectively, and modified by analogy. Considering the changing proportion in MSW yield of different areas with the developments of city planning, the predicted data were calculated and

关 键 词：环境工程 生活垃圾 组分 预测 人工神经网络 

分 类 号：X799.3[环境科学与工程—环境工程]