Optimizing industrial energy consumption through artificial intelligence: a study on cost reduction and sustainability
DOI:
https://doi.org/10.29070/1bydm604Keywords:
Artificial Intelligence, Electricity Consumption Optimization, Direct Reduction Units, Steel Industry, Machine Learning, Energy Efficiency, Sustainable Manufacturing, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Response Surface Methodology (RSM)Abstract
Effective power consumption control is essential for the steel industry, particularly for direct reduction (DR) units, in order to cut operating costs and limit the sector's impact on the environment. The reason for this is because the steel industry is one of the industrial sectors that employs the greatest amount of energy. Through the use of three cutting-edge techniques, namely Response Surface Methodology (RSM), Radial Basis Function (RBF) neural networks, and Multilayer Perceptron (MLP) neural networks, this study investigates the modeling and optimization of power consumption in direct reduction units. We made use of operational data from industrial processes in order to develop models that were capable of estimating the amount of electricity that was used under a variety of process conditions. The comparison demonstrates that artificial intelligence-based models, particularly RBF and MLP networks, deliver greater prediction accuracy when compared to traditional statistical approaches by a significant margin. It was necessary to apply the optimized model once again in order to determine the optimal operating parameters that would minimize the amount of power used without compromising the efficiency of production. Using optimization strategies that are driven by artificial intelligence might potentially result in significant cost reductions and an increase in energy efficiency, according to the research. The purpose of this project is to combine advanced predictive modeling with industrial energy management systems in order to contribute to sustainable production techniques and to promote the move toward energy-efficient steel manufacturing that is compatible with global sustainability goals.
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