Exploiting the power of AI in the heavy industry brought back satisfactory results at the kiln level, machine learning techniques allowed predictive modeling of the baking process using powerful Machine Learning models which had a great impact on the energy consumption and kiln production rate among all the models used. Large amounts of historical data were used after analysis and preparation, for which several methods were applied such as preprocessing and feature selection. All models were tested on 20% of the data, using Mean Absolute Error and Root mean squared error as metrics to evaluate our models in order to identify the in uencing variables that contribute most to the increase in energy consumption and kiln production rate.
In the context of organizing the means of production, the heavy cement industry is following the new concept of Industry 4.0, which increases the efficiency of industrial processes and increases productivity through customization and flexibility, while reducing costs and energy consumption. To do this, it uses process prediction by operating the digital transformation through a 4.0 tool for monitoring and analyzing temperature and pressure in real time. This tool monitors temperature and pressure using sensors that transform the data into a computer platform for real-time analysis, and predicts failures according to a predictive model to remedy the problem of preheater cyclone blockages. This new technology reduces incidents and increases the life of equipment [1].
Exploiting the power of AI in the heavy industry brought back satisfactory results at the kiln level, machine learning techniques allowed predictive modeling of the baking process using powerful Machine Learning models which had a great impact on the energy consumption and kiln production rate among all the models used. Large amounts of historical data were used after analysis and preparation, for which several methods were applied such as preprocessing and feature selection. All models were tested on 20% of the data, using Mean Absolute Error and Root mean squared error as metrics to evaluate our models in order to identify the influencing variables that contribute most to the increase in energy consumption and kiln production rate.
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