The problem of the municipal solid waste (MSW) excessive generation exists all over the world and in Russia, in particular. The Ministry of Natural Resources and Environment of the Russian Federation reports that only 16% of produced MSW was delivered to waste processing plants in 2019. Recycling technologies are used to reduce the amount of landfilled wastes. One of the prospective ways to use the resource and energy potential of MSW is fuel production.
Waste derived fuel (RDF, SRF) is produced from classified, grinded and sometimes pelletised waste. Waste derived fuel is classified according to three main criteria: calorific value (economic criteria), chlorine content (technical) and mercury content (environmental). Calorific value is one of the most important criteria that characterizes waste derived fuel. Experimental methods are currently used to assess the calorific value. Tests are carried out on calorimeters. Laboratory determination of calorific value is labor intensive and expensive.
The heat of combustion depends on the fuel composition, so analytical methods can be used to estimate the calorific value. The analytical methods use either ultimate or morphological composition of MSW samples.
The purpose of this work is to study an analytical method for assessing energy potential of fuel from MSW. The aim was to calculate the MSW sample’s calorific value by means of existing regressions based on waste composition. Seven regressions were taken from investigations conducted in different locations (Russia, Spain, Japan, Malaysia, USA). All these regressions are based on waste composition. The values obtained were compared with the measured value. The MSW sample was collected at the MSW processing plant in the Moscow region. Composition, humidity and calorific value of the sample were measured.
Regressions presented in articles do not show a close fitting to the experimental value; the experimental calorific value was 13.55 MJ / kg, the values according to the presented formulas vary from 6,62 to 10.83 MJ / kg. Based on the results, the most promising regression is selected. Mentioned regression uses morphological composition, components’ calorific values and humidity content.
Further search for analytical methods has the aim to identify an equation for reliable estimations. For calculating new regression, the expanded experimental data is needed.
|Position of speaker||Student|
|Affiliation of speaker||ITMO University|
|Publication||International journal «Resource-Efficient Technologies»|