Louis, Sunday (2024) Optimization of Energy Consumption in Electric-Powered Modular Cement Plants: The Role of AI Algorithms. European Journal of Material Science, 10 (1). pp. 1-22. ISSN 2055-8112(Print),2055-656X (Online)
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Abstract
Cement production is a critical industrial process that is inherently energy-intensive, contributing significantly to global carbon dioxide (CO2) emissions, with estimates indicating that it is responsible for approximately 7% of these emissions worldwide. This substantial environmental impact underscores the urgent need for innovative strategies to reduce energy consumption and enhance the sustainability of cement manufacturing. One promising approach to address this challenge is the integration of Artificial Intelligence (AI) into the energy management systems of electric-powered modular cement plants. The deployment of AI in these modular setups presents a transformative opportunity to optimize energy use, thereby reducing both energy waste and operational costs. AI algorithms, with their ability to process vast amounts of data and learn from historical patterns, offer a sophisticated means of improving the efficiency of energy consumption in cement production. By continuously analyzing data from various stages of the production process, AI can identify inefficiencies, predict energy demand, and recommend adjustments in real-time, leading to more precise energy management. This paper delves into the potential of AI to revolutionize energy management in modular cement plants. It explores the various AI techniques that can be employed to enhance the operational efficiency of these plants, including machine learning algorithms, predictive analytics, and real-time optimization. Through a thorough review of existing literature and analysis of case studies, this paper not only identifies the specific AI methodologies that have shown promise but also discusses their practical applications within the context of cement production. Furthermore, the paper proposes a structured methodology for integrating AI into the energy management systems of modular cement plants. This methodology is designed to guide practitioners in the cement industry through the process of adopting AI technologies, ensuring that they can effectively harness the power of AI to achieve significant energy savings and reduce their carbon footprint. By providing a detailed roadmap for implementation, this paper aims to facilitate the transition towards more sustainable and energy-efficient cement production practices.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 04 Sep 2024 08:17 |
| Last Modified: | 04 Sep 2024 08:17 |
| URI: | https://tudr.org/id/eprint/3353 |
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