OCCURRENCE PREDICTION AND RESOURCE UTILIZATION OF OFFICE BUILDING DECORATION WASTE IN CHINA
With the continuous promotion of the construction of Zero-Waste City,the demand for decoration waste treatment is increasing day by day.However,there are still shortcomings in the prediction of the amount of decoration waste in relevant office buildings and the research on resource utilization.In this study,the BP neural network was used to study the characteristics and trends of the source of office building decoration waste in 31 provinces and cities(except for Hong Kong,Macao and Taiwan).After the test,the goodness-of-fit of the trained BP neural network was R=0.93693＞0.9,and the model accuracy was excellent.According to the prediction results of the BP model,the total amount of office building renovation waste occurring in China will reach 7.856 million tons by 2030,with a growth rate of 36.8％compared with 2020.Using ArcGIS means to characterize the spatial and temporal distribution of office renovation waste emissions in China,the study shows that the national occurrence is mainly in the southeast coastal and Sichuan-Chongqing regions,supplemented by the central and southern regions,and the overall low occurrence in the rest of the regions.Since the waste paper-faced gypsum board accounts for the largest proportion of the waste,up to 58.9％,a study on its resource treatment and disposal was conducted.Through comparison experiments with desulfurization gypsum,it was proved that its mechanical properties was better than that of desulfurization gypsum under high dosing.The optimal amount of gypsum as a retarder and the best mechanical properties of cement samples were determined by comparing different processes.The cement performance was optimal when a 5％waste gypsum board was added.The best flexural strength of the cement samples made by mixing raw materials was 8.9 MPa and the compressive strength was 46.5 MPa.
office building decoration wasteoccurrence predictionBP neural networkspatial and temporal distribution characteristicscement retarder
NETL
NSTL
万方数据
