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In this paper, multi-layer feed forward neural networks are used to predictthe lower heating value of gas (LHV), lower heating value of gasificationproducts including tars and entrained char (LHVp) and syngas yield duringgasification of municipal solid waste (MSW) during gasification in a fluidizedbed reactor. These artificial neural networks (ANNs) with differentarchitectures are trained using the Levenberg-Marquardt (LM) back-propagationalgorithm and a cross validation is also performed to ensure that the resultsgeneralise to other unseen datasets. A rigorous study is carried out onoptimally choosing the number of hidden layers, number of neurons in the hiddenlayer and activation function in a network using multiple Monte Carlo runs.Nine input and three output parameters are used to train and test variousneural network architectures in both multiple output and single outputprediction paradigms using the available experimental datasets. The modelselection procedure is carried out to ascertain the best network architecturein terms of predictive accuracy. The simulation results show that the ANN basedmethodology is a viable alternative which can be used to predict theperformance of a fluidized bed gasifier.

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor