  In this paper, multi-layer feed forward neural networks are used to predict
the lower heating value of gas (LHV), lower heating value of gasification
products including tars and entrained char (LHVp) and syngas yield during
gasification of municipal solid waste (MSW) during gasification in a fluidized
bed reactor. These artificial neural networks (ANNs) with different
architectures are trained using the Levenberg-Marquardt (LM) back-propagation
algorithm and a cross validation is also performed to ensure that the results
generalise to other unseen datasets. A rigorous study is carried out on
optimally choosing the number of hidden layers, number of neurons in the hidden
layer and activation function in a network using multiple Monte Carlo runs.
Nine input and three output parameters are used to train and test various
neural network architectures in both multiple output and single output
prediction paradigms using the available experimental datasets. The model
selection procedure is carried out to ascertain the best network architecture
in terms of predictive accuracy. The simulation results show that the ANN based
methodology is a viable alternative which can be used to predict the
performance of a fluidized bed gasifier.
