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@article{202448,
        author = {Vipin Kumar},
        title = {Construction of Optimized Fuzzy Control Charts: A State-of-the-Art Review on Analytics Models in Prediction of Petroleum Quality},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7590-7603},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202448},
        abstract = {Fuzzy control charts have emerged as robust tools for handling uncertainties in the monitoring and control of various industrial processes, including the multifaceted petroleum sector. When integrated with cutting-edge analytics, optimization, and machine learning models, fuzzy control charts can not only enhance the accuracy of quality predictions but also streamline production processes. This comprehensive review synthesizes the current state-of-the-art research on constructing optimized fuzzy control charts and their application in predicting petroleum quality. It explores the theoretical underpinnings of fuzzy logic and neuro-fuzzy systems, reviews existing literature on optimization techniques applied to oil and gas processes, and highlights key challenges and gaps that remain open for future inquiry. By surveying a wide range of methods—ranging from hybrid machine learning approaches to advanced evolutionary algorithms—this paper shows how fuzzy control charts can be effectively deployed to achieve more reliable quality control and improve decision-making in petroleum production. Finally, it underscores emerging trends, outlines vital challenges, and proposes future directions to spur continued innovation. The insights provided in this review will be of value to both academics and practitioners seeking to optimize petroleum quality control through advanced fuzzy-based analytics frameworks.},
        keywords = {Fuzzy control charts, Petroleum quality prediction, Optimization techniques, Machine learning, Neuro-fuzzy systems, Analytics models},
        month = {May},
        }