Heating value prediction for combustible fraction of municipal solid waste in Semarang using backpropagation neural network

2015 ◽  
Author(s):  
Ainie Khuriati ◽  
Wahyu Setiabudi ◽  
Muhammad Nur ◽  
Istadi Istadi
Keyword(s):  
Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Backpropagation Neural Network ◽  
Heating Value ◽  
Value Prediction

scholarly journals Machine learning based modelling for lower heating value prediction of municipal solid waste

Fuel ◽  
2021 ◽  
Vol 283 ◽  
pp. 118906
Author(s):  
Cansu Birgen ◽  
Elisa Magnanelli ◽  
Per Carlsson ◽  
Øyvind Skreiberg ◽  
Jostein Mosby ◽  
...  
Keyword(s):  
Machine Learning ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Heating Value ◽  
Value Prediction ◽  
Lower Heating Value ◽  
Lower Heating

scholarly journals Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste

AIMS Energy ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 944-956 ◽  
Author(s):  
Obafemi O. Olatunji ◽  
◽  
Stephen Akinlabi ◽  
Nkosinathi Madushele ◽  
Paul A. Adedeji ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Multilayer Perceptron ◽  
Heating Value ◽  
Artificial Neural

scholarly journals Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques

2018 ◽  
Vol 37 (6) ◽  
pp. 578-589 ◽  
Author(s):  
Imane Boumanchar ◽  
Younes Chhiti ◽  
Fatima Ezzahrae M’hamdi Alaoui ◽  
Abdelaziz Sahibed-dine ◽  
Fouad Bentiss ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Multiple Regression ◽  
Calorific Value ◽  
Heating Value ◽  
Ultimate Analysis ◽  
Msw Management ◽  
Important Challenge ◽  
Programming Techniques

Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an oxygen bomb calorimeter. This process is, however, expensive. In this light, the purpose of this paper was to develop empirical models for the prediction of MSW higher heating value (HHV) from ultimate analysis. Two methods were used: multiple regression analysis and genetic programming formalism. Both techniques gave good results. Genetic programming, however, provides more accuracy compared to published works in terms of a great correlation coefficient (CC) and a low root mean square error (RMSE).

scholarly journals Characterization of Refuse Derived Fuel Production from Municipal Solid Waste: The Case Studies in Latvia and Lithuania

2020 ◽  
Vol 24 (3) ◽  
pp. 112-118
Author(s):  
Dace Âriņa ◽  
Rūta Bendere ◽  
Gintaras Denafas ◽  
Jānis Kalnačs ◽  
Mait Kriipsalu
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Calorific Value ◽  
Ash Content ◽  
Fuel Production ◽  
Heating Value ◽  
Refuse Derived Fuel ◽  
Morphological Composition ◽  
The Mean

AbstractThe authors determined the morphological composition of refuse derived fuel (RDF) produced in Latvia and Lithuania by manually sorting. The parameters of RDF (moisture, net calorific value, ash content, carbon, nitrogen, hydrogen, sulphur, chlorine, metals) was determined using the EN standards. Comparing obtained results with data from literature, authors have found that the content of plastic is higher but paper and cardboard is lower than typical values. Results also show that the mean parameters for RDF can be classified with the class codes: Net heating value (3); chlorine (3); mercury (1), and responds to limits stated for 3rd class of solid recovered fuel. It is recommended to separate biological waste at source to lower moisture and ash content and increase heating value for potential fuel production from waste.

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