Probabilistic digital twin of water treatment facilities - Wei, Yuying (Nanyang Technological University, 2024)
In recent years, the implementation of digital twin (DT) as a digital replica of the physical asset has matured significantly in smart manufacturing with the advancement of digital technologies. At the same time, for water treatment facilities which are critical infrastructures, DT is still in its infancy for real-world applications. Therefore, there is a pressing need for research that can accelerate the DT development for these critical infrastructures. The aim of this study is to improve the performance of DT for water treatment facilities using probabilistic assessment. The scope of research focuses on two main directions: first, utilizing probabilistic machine learning (ML) models to assist in real-time anomaly detection of DT, and second, developing data assimilation methods for probabilistic ML models to obtain optimized system states for process control. Anomaly detection is crucial for water treatment facilities as they can be susceptible to cyber-physical attacks that negatively impact the system's functionality, while data assimilation can improve the robustness of real-time monitoring in noisy environments by assimilating ML predictions with observations. A combined anomaly detection framework (CADF) was first developed for DT applications using probabilistic ML models. A prototype water treatment testbed facility was utilized to verify the anomaly detection framework by simulating various types of security attacks. CADF utilizes a Programmable Logic Controller (PLC)-based whitelist system to detect anomalies targeting the actuators, and a probabilistic ML model with corresponding assessment to detect anomalies targeting the sensors in this fully operational, scaled-down testbed facility. The results showed that CADF could successfully detect various attacks on the DT and reduce false alarms significantly when compared with other methods. To further enhance the performance of CADF, a real-time data processing framework was developed to update ML models in the DT by selecting suitable update intervals and training datasets to maximize their overall accuracy. It was shown to synchronize model updates and predictions effectively, leading to a significant reduction in errors. A data assimilation approach named Probabilistic Optimal Interpolation (POI) was also developed to combine the predictions from probabilistic ML models and real-time observations. The quantification of the respective uncertainties is directly included within the probabilistic ML model itself. As an application example, the performance of POI was tested using a multi-scale Lorenz 96 chaos system in both stationary and nonstationary environments. The POI implementation was able to reduce uncertainty in both environments and serve as a compromise in scenarios where the noise level of the environment was unclear. The performance of POI under scenarios with missing values was also evaluated by masking the test datasets with different missingness rates. The impact from random missing values was found negligible and assimilation was still suggested at missing points. In summary, probabilistic approaches and frameworks were developed in this study for DT of water treatment facilities, with a particular emphasis on anomaly detection and data assimilation. They were shown to be effective in enhancing the DT performance and potentially leading to more robust and secure systems in the future.