Urban biomass and methods of estimating municipal biomass resources

Y. Li, L.W. Zhou and R.Z. Wang

Renewable and Sustainable Energy Reviews, 2017, vol. 80, issue C, 1017-1030

Abstract: Municipal generated biomass contains significant quantities of carbohydrates, which have considerable potential to be applied as an energy source. It is necessary to investigate the characteristic of municipal biomass and quantify its energy contents. The objective of this paper is to review the recent research progress on biomass potentials in the urban area together with municipal biomass resource estimation methods. The municipal biomass can be classified into three major categories such as municipal solid waste, municipal sewage, and urban wood biomass. The study of proximate, ultimate and calorific value analysis shows that these three categories of municipal biomass are different in properties. The calorific values of the three biomass categories are approximately 7.10–19.90MJ/kg, 8.73–19.10MJ/kg and 16.96–21.59MJ/kg respectively. The origins of municipal solid waste in Asia are different from those of Europe, and the basic disposal methods are diverse. The characteristics of municipal biomass differ a lot in developing and developed countries. The possible reasons for this difference are the customs, resources, and culture of these countries. The methods of estimating municipal biomass in urban areas can be classified as measurement methods, semi-empirical methods and advanced modelling. For the measurement method, there are statistical methods for municipal solid waste, measuring of municipal sewage using flow meter and biomass remote sensing for urban wood biomass, which is quite labour and time consuming. The most widely used semi-empirical methods include the handbook method and allometric method. The allometric method is usually applied to urban wood biomass. In this paper, two advanced modelling methods in estimating municipal biomass resources are introduced, that are the artificial neural network and multiple linear regression. It is expected that the artificial neural network will be a promising method for the estimation of municipal biomass amount.

Keywords: Municipal biomass; Municipal solid waste (MSW); Municipal sewage; Urban wood biomass; Estimation methods of municipal biomass resources (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (11)

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DOI: 10.1016/j.rser.2017.05.214

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