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

TL;DR

This study develops neural network models to accurately predict key outputs of municipal solid waste gasification in a fluidized bed reactor, aiding process optimization and performance assessment.

Contribution

It introduces a neural network modeling approach with optimized architecture for predicting gasification outputs, validated with experimental data.

Findings

ANN models accurately predict LHV and syngas yield

Optimal network architecture identified through rigorous validation

ANN approach outperforms traditional prediction methods

Abstract

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.