Artikel Jurnal :: Kembali

Artikel Jurnal :: Kembali

Artificial neural network modeling and optimization of hall-heroult process for aluminum production

Reza Seif Mohaddecy, Yasser Arab Ameri (Faculty of Engineering, Universitas Indonesia, 2015)

 Abstrak

Experience in applying a hybrid artificial neural network (ANN)-genetic algorithm for modeling and optimizing the Hall-Heroult process for aluminum extraction is described in this study. During the stage of modeling, the most important and effective process variables including temperature and cell voltage, metal and bath heights, purity of CaF2 and Al2O3, and bath ratio are chosen as input variables whilst outputs of the model are product purity, ampere efficiency, and product rate. During three years of operation, 19 points were selected for building and training, 7 points for testing, and 7 data points for validating the model. Results show that a feed-forward Artificial Neural Network (ANN) model with 3 neurons in the hidden layer can acceptably simulate the mentioned output variables with the Mean Squared Error (MSE) of 0.002%, 0.108% and 0.407%, respectively. Utilizing the validated model and multi-objective genetic algorithms, aluminum purity and the rate of production are maximized by manipulating decision variables. Results show that setting these decision variables at the optimal values can increase approximately the metal purity, ampere efficiency, and product rate by 0.007%, 0.185%, and 20kg/h, respectively.

 Metadata

Jenis Koleksi : Artikel Jurnal
No. Panggil : UI-IJTECH 6:3 (2015)
Entri utama-Nama orang :
Subjek :
Penerbitan : Depok: Faculty of Engineering, Universitas Indonesia, 2015
Sumber Pengatalogan : LibUI eng rda
ISSN : 20869614
Majalah/Jurnal : International Journal of Technology
Volume : Vol. 6, No. 3, July 2015: Hal. 480-491
Tipe Konten : text
Tipe Media : unmediated
Tipe Carrier : volume
Akses Elektronik : https://doi.org/10.14716/ijtech.v6i3.887
Institusi Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 4 R. Koleksi Jurnal
  • Ketersediaan
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No. Panggil No. Barkod Ketersediaan
UI-IJTECH 6:3 (2015) 08-23-36332303 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 9999920531773
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