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Artificial neural network based modelling approach for municpal solid waste gasification in a fluidized bed reactor
Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J.; Kwapinski, Witold
This article corresponds to chapter 5 of Ph.D: Experimental and mathematical modelling of biowaste gasification in a bubbling fluidised bed reactor Pandey, Daya Shankar URI: http://hdl.handle.net/10344/7116 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 fluidised 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 fluidised bed gasifier.
Keyword(s): municipal solid waste; gasification; artificial neural networks; feed-forward multilayer perceptron; fluidized bed gasifier
Publication Date:
2016
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Citation(s): info:eu-repo/grantAgreement/EC/FP7/289887
Waste Management;58, pp. 202-213
http://dx.doi.org/10.1016/j.wasman.2016.08.023
289887
Publisher(s): Elsevier
First Indexed: 2018-09-01 06:26:36 Last Updated: 2018-09-02 06:26:33