Iranian Association of Environmental Health (IAEH)
Abstract
The application of artificial neural network on energy modeling needs
to be researched more extensively in order to appreciate and fulfill
the potential of this modeling approach. The estimation of lower
heating value is required to know the actual available energy to be
converted to heat or electricity. In this study, a feed forward
artificial neural network, trained by error back propagation algorithm
was used to predict the lower heating value of municipal solid waste.
Plastic, paper, glass, textile and food were found to be essential for
prediction of lower heating value of municipal solid waste. The lower
heating value has strong relationship with plastic, paper, glass,
textile and food. Using 60 dataset divided into 37 training dataset and
23 validating dataset, gathered from Abuja waste stream, artificial
neural network was trained and validated. The efficiency and accuracy
of the artificial neural network was measured based on absolute average
error and determination coefficient. The artificial neural network
produced results with an absolute average percentage error less than
9.13% and 9.4% for training and validating dataset, respectively, when
compared to measured data. The model provided the best fit and the
predicted trend followed the observed data closely; the determination
coefficient for training and validating dataset were 0.992 and 0.981,
respectively. These results show that artificial neural network is an
effective tool in forecasting energy content