State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China; College of Mechanical and Energy Engineering, Jimei University, Xiamen 361021, China.
State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China.
Waste Manag. 2015 Feb;36:24-32. doi: 10.1016/j.wasman.2014.11.020. Epub 2014 Dec 20.
A rapid and cost-effective prediction method based on wet physical composition has been developed to determine the lower heating value (LHV) of municipal solid waste (MSW) for practical applications in China. The heating values (HVs) of clean combustibles were measured in detail, and the effect of combustibles, food waste, and ash content on HV was studied to develop the model. The weighted average HV can be used to predict the MSW HV with high accuracy. Based on the moisture measurements of each major real combustible and the HV of clean solid waste, a predictive model of the LHV of real MSW was developed. To assess the prediction performance, information was collected on 103 MSW samples from 31 major cities in China from 1994 to 2012. Compared with five predictive models based on the wet physical composition from different regions in the world, the predictive result of the developed model is the most accurate. The prediction performance can be improved further if the MSW is sorted better and if more information is collected on the individual moisture contents of the waste.
已开发出一种基于湿物理成分的快速且经济有效的预测方法，以确定城市固体废物（MSW）的低位热值（LHV），从而在中国实际应用。详细测量了清洁可燃物的热值（HV），并研究了可燃物、食物垃圾和灰分含量对 HV 的影响，以开发模型。加权平均 HV 可用于准确预测 MSW HV。基于每种主要实际可燃物的水分测量值和清洁固体废物的 HV，开发了实际 MSW 的 LHV 预测模型。为了评估预测性能，收集了 1994 年至 2012 年中国 31 个主要城市的 103 个 MSW 样本信息。与基于世界不同地区的湿物理成分的五个预测模型相比，开发的模型的预测结果最准确。如果对 MSW 进行更好的分类，并收集更多关于废物个别水分含量的信息，则可以进一步提高预测性能。
