Predictive Modeling and Big Data Analytics ‎for Optimizing Refractory Material ‎Composition and Performance Evaluation

  • Authors

    • A. Emmanuel Peo Mariadas Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam , Tamil Nadu, India
    • R. Radha Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai, Tamilnadu, India
    • P. Immaculate Rexi Jenifer Department of School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India
    • S. Praveen Kumar Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam , Tamil Nadu, India
    • Ravikanth Garladinne Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Gun-‎tur, Andhra Pradesh, India
    • P. Dhanalakshmi Department of Artificial Intelligence & Machine Learning, Mohan Babu University, Tirupati, India
    https://doi.org/10.14419/ghjt2t64

    Received date: July 15, 2025

    Accepted date: August 20, 2025

    Published date: September 13, 2025

  • Artificial Intelligence; Data Processing; Data Analytics; Deep Learning; Refractory Materials

  • Abstract

    Refractory materials are essential for high-temperature industrial processes, where precise composition is critical to ensuring optimal per-‎formance. However, variability in raw materials and operating conditions poses significant challenges in maintaining consistent quality. ‎Traditional trial-and-error optimization methods are inefficient and fail to leverage the vast amounts of data generated in modern manufactur-‎ing, leading to inconsistent material performance, increased costs, and prolonged development cycles. To overcome these limitations, we ‎propose the Refractory Materials using Big Data Analytics (RM-BDA) framework, which integrates AI-driven predictive modeling with ‎advanced data analytics. RM-BDA leverages both historical and real-time data—including raw material characteristics, processing parame-‎ters, and performance metrics—to accurately predict optimal formulations and improve material performance. Using machine learning algo-‎rithms and robust data processing techniques, RM-BDA enhances prediction accuracy and accelerates formulation optimization. This allows ‎manufacturers to proactively adjust compositions and operational settings to meet targeted performance requirements, reducing waste and ‎improving efficiency. Additionally, the system dynamically responds to fluctuations in material inputs and process conditions, offering real-‎time optimization recommendations. Results demonstrate that RM-BDA significantly improves the accuracy of performance predictions, ‎reduces production costs, and enhances the consistency and quality of refractory materials. By replacing inefficient traditional methods with ‎a data-driven approach, this framework marks a substantial advancement in the field of refractory material development and optimization‎.

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  • How to Cite

    Mariadas, A. E. P. ., Radha , R. ., Jenifer , P. I. R. ., Kumar, S. P. . ., Garladinne , R. ., & Dhanalakshmi , P. . (2025). Predictive Modeling and Big Data Analytics ‎for Optimizing Refractory Material ‎Composition and Performance Evaluation. International Journal of Basic and Applied Sciences, 14(5), 403-416. https://doi.org/10.14419/ghjt2t64