Vol 6, No 3 (2015) > Industrial Engineering >

Artificial Neural Network Modeling and Optimization of Hall-Heroult Process for Aluminum Production

Sepehr Sadighi, Reza Seif Mohaddecy, Yasser Arab Ameri

 

Abstract:

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.

Keywords: Aluminum production; Artificial neural network; Hall-Heroult process; Modeling; Optimization

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