A Novel Method for the Estimation of Higher Heating Value of Municipal Solid Wastes

Weiguo Dong, Zhiwen Chen, Jiacong Chen, Zhao Jia Ting, Rui Zhang, Guozhao Ji and Ming Zhao
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Weiguo Dong: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Zhiwen Chen: Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China
Jiacong Chen: Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China
Zhao Jia Ting: Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China
Rui Zhang: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Guozhao Ji: Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science & Technology, Dalian University of Technology, Dalian 116024, China
Ming Zhao: Division of Solid Waste Management, School of Environment, Tsinghua University, Beijing 100084, China

Energies, 2022, vol. 15, issue 7, 1-14

Abstract: The measurement of the higher heating value (HHV) of municipal solid wastes (MSWs) plays a key role in the disposal process, especially via thermochemical approaches. An optimized multi-variate grey model (OBGM (1, N )) is introduced to forecast the MSWs’ HHV to high accuracy with sparse data. A total of 15 cities and MSW from the respective city were considered to develop and verify the multi-variant models. Results show that the most accurate model was POBGM (1, 5) of which the least error measured 5.41% MAPE (mean absolute percentage error). Ash, being a major component in MSW, is the most important factor affecting HHV, followed by volatiles, fixed carbon and water contents. Most data can be included by using the prediction interval (PI) method with 95% confidence intervals. In addition, the estimations indicated that the MAPE from estimating the HHV for various MSW samples, collected from various cities, were in the range of 3.06–34.50%, depending on the MSW sample.

Keywords: MSWs; HHV; multi-variate grey model; modeling; biomass (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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