An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries
Antanasijević, Davor Z.
Ristić, Mirjana D.
Pocajt, Viktor V.
Article (Published version)
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Although the use of municipal solid waste to generate energy can decrease dependency on fossil fuels and consequently reduces greenhouse gases emissions and areas that waste occupies, in many countries municipal solid waste is not recognized as a valuable resource and possible alternative fuel. The aim of this study is to develop a model for the prediction of primary energy production from municipal solid waste in the European countries and then to apply it to the Balkan countries in order to assess their potentials in that field. For this purpose, general regression neural network architecture was applied, and correlation and sensitivity analyses were used for optimisation of the model. The data for 16 countries from the European Union and Norway for the period 2006-2015 was used for the development of the model. The model with the best performance (coefficient of determination R-2 = 0.995 and the mean absolute percentage error MAPE = 7.757%) was applied to the data for the Balkan cou...ntries from 2006 to 2015. The obtained results indicate that there is a significant potential for utilization of municipal solid waste for energy production, which should lead to substantial savings of fossil fuels, primarily lignite which is the most common fossil fuel in the Balkans.
Keywords:Renewable energy / Primary production of energy / Municipal solid waste / General regression neural network / Fuel substitution / Energy recovery
Source:Waste Management, 2018, 78, 955-968
- Pergamon-Elsevier Science Ltd, Oxford
Funding / projects:
- Development and Application of Methods and Materials for Monitoring New Organic Contaminants, Toxic Compounds and Heavy Metals (RS-172007)