Computer simulation of gas generation and transport in landfills. V : Use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning

Hu LI, Raudel SANCHEZ, S. QIN, Halil I. KAVAK, Ian A. WEBSTER, Theodore T. TSOTSIS, Muhammad SAHIMI*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

30 Citations (Scopus)

Abstract

In the first four parts of this series a three-dimensional model was developed for transport and reaction of gaseous mixtures in a landfill. An optimization technique was also utilized in order to determine a landfill's spatial distributions of the permeability, porosity, the tortuosity factors, and the total gas generation potential of the wastes, given a limited amount of experimental data. In the present paper we develop an artificial neural network (ANN) in order to make accurate short-term predictions for several important quantities in a large landfill in southern California, including the temperature, and the CH4, CO2, and O2 concentration profiles. The ANN that is developed utilizes a back-propagation algorithm. The results indicate that the ANN can be successfully trained by the experimental data, and provide accurate predictions for the important quantities in the sector of the landfill where the data had been collected. Thus, an ANN may be used by landfills' operators for short-term plannings. Moreover, we showed that a novel combination of the three-dimensional model of gas generation, flow, and transport in landfills developed in Parts I, II, and IV, the optimization technique described in Part III, and the ANN developed in the present paper is a powerful approach for developing an accurate model of a landfill for long-term predictions and planning. © 2011 Elsevier Ltd.
Original languageEnglish
Pages (from-to)2646-2659
Number of pages14
JournalChemical Engineering Science
Volume66
Issue number12
Early online date16 Mar 2011
DOIs
Publication statusPublished - 15 Jun 2011
Externally publishedYes

Bibliographical note

The authors are grateful to the California Energy Commission and ChevronTexaco for partial support of this work. Most of the computations were carried out using the large computer cluster and parallel machine of the University of Southern California Computer Center.

Keywords

  • Artificial neural networks
  • Forecasting
  • Genetic algorithm
  • Landfills
  • Methane
  • Predictions

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