Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 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.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • C. Chandre Gowda, and S. G. Mayya, “Comparison of Back Propagation Neural Network and Genetic Algorithm Neural Network for Stream Flow Prediction,” Journal of Computational Environmental Sciences, vol. 2014, pp. 1–6, 2014. View at Publisher · View at Google Scholar
  • Mohammad Hossein Ahmadi, Mohammad-Ali Ahmadi, Mehdi Mehrpooya, and Marc A. Rosen, “Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine,” Sustainability, vol. 7, no. 2, pp. 2243–2255, 2015. View at Publisher · View at Google Scholar
  • A. Çebi, E. Akdoğan, A. Celen, and A. S. Dalkilic, “Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks,” Heat and Mass Transfer, 2016. View at Publisher · View at Google Scholar
  • Hikmet Sis, Ismail Bentli, Nizamettin Demirkiran, and Ahmet Ekmekyapar, “Dissolution kinetics of colemanite in HCl solutions by measuring particle size distributions,” Separation Science and Technology, pp. 1–8, 2017. View at Publisher · View at Google Scholar
  • Julia Ofure Eichie, Onyedi David Oyedum, Moses Oludare Ajewole, and Abiodun Musa Aibinu, “Artificial Neural Network model for the determination of GSM Rxlevel from atmospheric parameters,” Engineering Science and Technology, an International Journal, vol. 20, no. 2, pp. 795–804, 2017. View at Publisher · View at Google Scholar
  • Reyhaneh Loni, E. Askari Asli-Ardeh, B. Ghobadian, M.H. Ahmadi, and Evangelos Bellos, “GMDH modeling and experimental investigation of thermal performance enhancement of hemispherical cavity receiver using MWCNT/oil nanofluid,” Solar Energy, vol. 171, pp. 790–803, 2018. View at Publisher · View at Google Scholar
  • Hüseyin Elçiçek, and Mehmet M. Kocakerim, “Leaching kinetics of ulexite ore in aqueous medium at different CO 2 partial pressures,” Brazilian Journal of Chemical Engineering, vol. 35, no. 1, pp. 111–122, 2018. View at Publisher · View at Google Scholar
  • Soheil Hassanipour, Haleh Ghaem, Morteza Arab-Zozani, Mozhgan Seif, Mohammad Fararouei, Elham Abdzadeh, Golnar Sabetian, and Shahram Paydar, “Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta-analysis,” Injury, 2019. View at Publisher · View at Google Scholar