# Application of the Residue Method in Steam Superheater Fault Detection
## 1 Introduction
The second approach, which is model-based, utilizes knowledge about the system in the form of physical, equilibrium, and chemical equation data, or a black-box or grey-box model, to detect and analyze faults. In this approach, the system's behavior is modeled mathematically, and any deviation from the expected behavior is flagged as a fault. The third and most recent approach incorporates artificial intelligence, where neural networks, fuzzy systems, expert systems, grey correlation, or support vector machines (SVMs) are employed to develop a diagnostic system that can identify specific faults once trained. This approach is particularly useful when dealing with dynamic systems that have complex behaviors that are not easy to model using traditional techniques. By leveraging the power of machine learning and artificial intelligence, this approach can identify faults that are not easily detectable using traditional methods, thereby significantly improving the accuracy and reliability of the diagnostic system. Data-driven models, such as neural networks or fuzzy functions, offer a prompt and effective solution for solving diagnostic problems. In addition, signal processing techniques have proven to be highly valuable in detecting faults and diagnosing issues with moving elements. These techniques can even detect changes in signals directly from measurements or residuals from other flaw detection methodologies. The extent of technological capabilities in modern times is truly remarkable. The successful operation of a power plant is dependent on the efficiency and reliability of its various subsystems, particularly the turbine. The turbine plays a critical role in the system's functioning, and ensuring its robust operation is crucial for maintaining a smooth and consistent power output. In recent research, [PERSON] et al. [3] have proposed a novel approach for maintaining the steam temperature in the main steam temperature (MST) system of a coal-fired power plant CFPP. Their proposed strategy involves implementing an offline robust model predictive control (RMPC) technique that is offset-free and utilizes a zonotope-type uncertain model for higher accuracy, compact representation, and less complexity. The primary objective of the proposed RMPC technique is to maintain the desired steam temperature despite changes in operating conditions, with a particular focus on robustness and computational efficiency. The RMPC strategy is implemented in two stages. First, the RMPC law set is designed offline, which involves constructing a mathematical model that accurately represents the system's behavior and performance. Then, the explicit control law is selected online based on the current state of the system. To achieve an offset-free tracking performance, a manipulated variable target observer is used to update the chosen RMPC law. This approach ensures that the system maintains the desired steam temperature, even under changing operating conditions, by adjusting the manipulated variables accordingly. By utilizing this innovative approach, power plants can operate more efficiently and with greater reliability, ultimately leading to increased productivity and decreased costs. An interesting work that gives an overview of how a power plant works is the one that [PERSON], [PERSON] et al. wrote [4]. This paper aims to provide an overview of the development and application of intelligent computing tools, such as ANNs (artificial neural networks), in power plant management. It also presents some applications of intelligent computing tools in power plant operations management. Literature review techniques are used to demonstrate the use of intelligent computing tools in different power plant applications. The reviewed literature shows that ANN has the most significant potential to be the most reliable power computing and plant management tool. As technology continues to advance, new methods are being developed to detect faults in various power plant components. A recent study by [PERSON], [PERSON] et al.