### 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 CaF_{2} and Al_{2}O_{3}, 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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