天美传媒

Abstract

Advanced data-driven fault detection and diagnosis (FDD) has emerged as a transformative force in the realm of industrial chemistry, offering unprecedented capabilities in enhancing safety, efficiency, and sustainability. By integrating sophisticated data analytics techniques with the intricate workings of chemical processes, modern industrial facilities can leverage vast datasets generated by sensors and monitoring devices to detect subtle deviations from normal operation. Through the application of statistical models, pattern recognition algorithms, and anomaly detection techniques, these data streams are transformed into actionable insights in real-time. This abstract explores the key advantages of advanced data-driven FDD, including its ability to preemptively identify faults, facilitate root cause analysis, and enhance the scalability and adaptability of industrial processes. Furthermore, it highlights the role of advanced FDD in promoting sustainability by optimizing resource utilization, minimizing environmental impact, and safeguarding human health. Despite challenges such as integration with legacy systems and the need for interdisciplinary expertise, the potential of advanced data-driven FDD to revolutionize industrial safety and efficiency is undeniable, promising a brighter future for the field of industrial chemistry.