Modeling of paper mill sewage sludge drying using artificial neural networks: Reduction of the training database through Taguchi's method

Drying of sewage sludge is typically modeled as simultaneous heat and mass transfer phenomena. The capability of conventional models to take into account crust formation, cracks, and shrinking is limited. Artificial neural networks (ANNs) are suitable tools for dynamic representation of drying proce...

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Bibliographic Details
Published in:Drying technology 2017-04, Vol.35 (5), p.534-544
Main Authors: Salgado, Mario A. Heredia, Neto da Silva, Fernando José
Format: Article
Language:English
Subjects:
Artificial neural networks
Cracks
Drying
Drying agents
drying kinetics
Kinetics
Learning theory
Neural networks
paper mill sewage sludge
Pulp & paper mills
Sewage sludge
Sludge
Taguchi method
Taguchi methods
Training
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Summary:Drying of sewage sludge is typically modeled as simultaneous heat and mass transfer phenomena. The capability of conventional models to take into account crust formation, cracks, and shrinking is limited. Artificial neural networks (ANNs) are suitable tools for dynamic representation of drying processes; however, obtaining a suitable database is a resource consuming task. Based on the Taguchi method, nine experiments were defined to set up a training database and to develop an ANN model. A high Pearson correlation coefficient was verified when comparing the drying kinetic curve generated by the ANN model with the one obtained during the validation experiments.
ISSN:0737-3937
1532-2300
DOI:10.1080/07373937.2016.1187162