Scheduling and Dynamic Energy Optimization in Cement Mills

Conclusion: The results indicate that integrating forecasting techniques with optimization models significantly enhances energy efficiency and production management in cement plants, while also contributing to environmental sustainability and strengthening the competitive advantage of industrial operations.

📌 3 Key Takeaways

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Objective & Hypothesis:Background/Aim: This study aims to address energy consumption challenges in cement milling operations by developing an integrated framework that incorporates production uncertainties and inventory management efficiency.

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Key Findings & Data:Methods: In this context, statistical and machine learning techniques, including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), are employed for electricity price forecasting.

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Conclusion & Impact:Conclusion: The results indicate that integrating forecasting techniques with optimization models significantly enhances energy efficiency and production management in cement plants, while also contributing to environmental sustainability and strengthening the competitive advantage of industrial operations.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:Fiyat Tahmini · Çizelgeleme · Enerji

Abstract

Background/Aim: This study aims to address energy consumption challenges in cement milling operations by developing an integrated framework that incorporates production uncertainties and inventory management efficiency. The main objective is to minimize energy costs while improving production planning and environmental sustainability. Methods: In this context, statistical and machine learning techniques, including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), are employed for electricity price forecasting. Based on the forecasting results, a Mixed Integer Linear Programming (MILP) model is developed to optimize production scheduling and energy consumption. The proposed framework incorporates forecasted electricity-price fluctuations into production scheduling to generate cost-oriented production plans while satisfying operational constraints. Results: Among the forecasting methods, the LSTM model demonstrates superior performance with the lowest error rates. Furthermore, the optimization model provides efficient production plans that satisfy operational constraints and improve resource utilization. Conclusion: The results indicate that integrating forecasting techniques with optimization models significantly enhances energy efficiency and production management in cement plants, while also contributing to environmental sustainability and strengthening the competitive advantage of industrial operations.

Özet

Bu çalışma, çimento öğütme operasyonlarında enerji tüketimiyle ilgili zorlukları ele almak amacıyla, üretim belirsizliklerini ve stok yönetimi etkinliğini bütüncül bir çerçevede ele alan bütünleşmiş bir yapı geliştirmeyi amaçlamaktadır. Temel amaç, üretim planlamasını ve çevresel sürdürülebilirliği iyileştirirken enerji maliyetlerini minimize etmektir. Bu kapsamda, elektrik fiyat tahmini için Otoregresif Entegre Hareketli Ortalama (ARIMA), Aşırı Gradyan Artırma (XGBoost) ve Uzun Kısa Süreli Bellek (LSTM) gibi istatistiksel ve makine öğrenmesi teknikleri kullanılmaktadır. Tahmin sonuçlarına dayanarak, üretim çizelgelemesini ve enerji tüketimini optimize etmek amacıyla Karma Tam Sayılı Doğrusal Programlama (MILP) modeli geliştirilmiştir. Önerilen yöntem, üretim faaliyetlerini dalgalanan elektrik fiyatlarına göre uyarlayarak enerji maliyetlerini başarıyla azaltmaktadır. Tahmin yöntemleri arasında, LSTM modeli en düşük hata oranları ile en üstün performansı göstermektedir. Ayrıca, optimizasyon modeli operasyonel kısıtları karşılayan ve kaynak kullanımını iyileştiren etkin üretim planları sunmaktadır. Elde edilen sonuçlar, tahmin tekniklerinin optimizasyon modelleriyle entegre edilmesinin, çimento tesislerinde enerji verimliliğini ve üretim yönetimini önemli ölçüde artırdığını, aynı zamanda çevresel sürdürülebilirliğe katkıda bulunduğunu ve endüstriyel operasyonların rekabet avantajını güçlendirdiğini göstermektedir.

Keywords

Fiyat Tahmini Çizelgeleme Enerji Optimizasyon Karma Tam Sayılı Doğrusal Programlama Price Forecasting Scheduling Energy Optimization Mixed-Integer Linear Programming (MILP)

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Cite This Article

Gümüştepe, M., Nursoy, İ., Kargı, A., Kaya, İ., Yelken, B. Ç. (2026). Scheduling and Dynamic Energy Optimization in Cement Mills. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 3. https://doi.org/10.65520/erciyesfen.1924331

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