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The Scientific World Journal
Volume 2014 (2014), Article ID 194874, 9 pages
Research Article

The Use of Artificial Neural Network for Prediction of Dissolution Kinetics

1Department of Naval Architect and Marine Engineering, Faculty of Naval Architecture & Maritime, Yildiz Technical University, 34383 Istanbul, Turkey
2Department of Mechatronics Engineering, Faculty of Mechanical Engineering, Yildiz Technical University, 34383 Istanbul, Turkey
3Department of Chemical Engineering, Faculty of Engineering, Texas A&M University, College Station, TX 77843-3122, USA

Received 14 March 2014; Revised 24 May 2014; Accepted 25 May 2014; Published 16 June 2014

Academic Editor: Christos Kordulis

Copyright © 2014 H. Elçiçek et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs) which are based on the multilayered perceptron. Reaction temperature, total pressure, stirring speed, solid/liquid ratio, particle size, and reaction time were selected as input parameters to predict the dissolution rate. Experimental dataset was used to train multilayer perceptron (MLP) networks to allow for prediction of dissolution kinetics. Developing ANNs has provided highly accurate predictions in comparison with an obtained mathematical model used through regression method. We conclude that ANNs may be a preferred alternative approach instead of conventional statistical methods for prediction of boron minerals.