In the nuclear fuel cycle (NFC) facilities, the failure of Heating Ventilation and Air Conditioning (HVAC) system starts with minor component failures and can escalate to affecting the entire system, ultimately resulting in radiological consequences to workers. In the field of air-conditioning and refrigerating engineering, the fault detection and diagnosis (FDD) of HVAC systems have been studied since faults occurring in improper routine operations and poor preventive maintenance of HVAC systems result in excessive energy consumption. This paper aims to provide a systematic review of existing FDD methods for HVAC systems therefore explore its potential application in nuclear field. For this goal, typical faults and FDD methods are investigated. The commonly occurring faults of HVAC are identified through various literature including publications from International Energy Agency (IEA) and American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). However, most literature does not explicitly addresses anomalies related to pressure, even though in nuclear facilities, abnormal pressure condition need to be carefully managed, particularly for maintaining radiological contamination differently within each zone. To build simulation model for FDD, the whole-building energy system modeling is needed because HVAC systems are major contributors to the whole building’s energy and thermal comfort, keeping the desired environment for occupants and other purposes. The whole-building energy modeling can be grouped into three categories: physics-based modeling (i.e., white-box models), hybrid modeling (i.e., grey-box models), and data-driven modeling (i.e., black-box models). To create a white-box FDD model, specialized tools such as EnergyPlus for modeling can be used. The EnergyPlus is open source program developed by US-DOE, and features heat balance calculation, enabling the dynamic simulation in transient state by heat balance calculation. The physics based modeling has the advantage of explaining clear cause-and-effect relationships between inputs and outputs based on heat and mass transfer equations, while creating accurate models requires time and effort. Creating a black-box FDD model requires a sufficient quantity and diverse types of operational data for machine learning. Since operation data for HVAC systems in existing nuclear cycle facilities are not fully available, so efforts to establish a monitoring system enabling the collection, storage, and management of sensor data indicating the status of HVAC systems and buildings should be prioritized. Once operational data are available, well-known machine learning methods such as linear regression, support vector machines, random forests, artificial neural networks, and recurrent neural networks (RNNs) can be used to classify and diagnose failures. The challenge with black-box models is the lack of access to failure data from operating facilities. To address this, one can consider developing black-box models using reference failure data provided by IEA or ASHRAE. Given the unavailability of operation data from the operating NFC facilities, there is a need for a short to medium-term plan for the development of a physics-based FDD model. Additionally, the development of a monitoring system to gather useful operation data is essential, which could serve both as a means to validate the physics-based model and as a potential foundation for building data-driven model in the long term.

The domestic representative nuclear fuel cycle facilities are post-irradiation examination facility (PIEF) and Irradiated Examination Facility (IMEF) at KAERI. They have regularly operated since 1991 and 1993, respectively. Due to the long period of use, the facilities are ageing, and maintenance costs are increasing every year. The maintenance methods have mainly been breakdown maintenance (BM) and partially preventive maintenance (PM). They involve replacing components that have problems through periodic inspections by on-site inspectors. However, these methods are not only uncertain in terms of replacement cycles due to worker’s deviation on the inspection results, but also make it difficult to respond accidents developed through failures on the critical equipment that confines radioactive material. Therefore, an advanced operation and maintenance studied in 2022 through all of nuclear facilities operated at KAERI. Advancement strategy in four categories (safety, sustainability, performance, innovativeness) was analyzed and their priorities according to a facility environment were determined so a roadmap for advanced operation and maintenance could be developed. The safety and sustainability are higher importance than the performance and innovativeness because facilities at KAERI has an emphasis on research and development rather than industrial production. Thus, strategy for advancement has focused even more on strengthening the safety and sustainability. To enhance safety, it has been identified that immediate improvement of aged structures, systems, and components (SSCs) through large-scale replacement is necessary, while consideration of implementing an ageing management program (AMP) in the medium to long term is also required. Facility sustainability requires strengthening operation expertise through training, education, and cultivation of specialized personnel for each system, and addressing outstanding regulatory issues such as approval of radiation environment report on the nuclear fuel processing facilities and improvement work according to fire hazard analysis. One of the safety enhancement methods, AMP, is a new maintenance approach that has not been previously applied, so it had to be thoroughly examined. In this study, an analysis was conducted on the procedure and method for introducing an AMP. An AMP for nuclear fuel cycle facilities was developed by analyzing the AMP applied to the BR2 research reactor in Belgium and modifying it for application to nuclear fuel cycle facilities. The ageing management for BR2 has the objective to maintain safety, availability and cost efficiency and three-step process. The first step is the classification of SSCs into four classes to apply graded approach. Secondly, ageing risk is assessed to identify critical failure modes, their frequency and precursors. Final step involves defining measures to reduce the ageing risk to an acceptable level in order to integrate the physical and economic aspects of ageing into a strategy for inspection, repair, and replacement. Similar approach was applied to the nuclear fuel cycle facility. Firstly, the SSCs of nuclear fuel cycle facilities have been classified according to their safety and quality classifications, as well as whether they are part of the confinement boundary. The SSCs involved in the confinement boundary were given more weight in the classification process, even if they are not classified as safety-class. A risk index for ageing was introduced to determine which prevention and mitigation measure should be chosen. By multiplying the health index and the impact index, the ageing risk matrix provides a numerical score that represents guidance on the prevention and mitigation of ageing effect. The health index is determined by combining the likelihood of failure and engineering evaluation of the current condition of SSCs, whereas the impact index is calculated by taking into account the severity of consequences and the duration of downtime resulting from a failure. This ageing management has to be thoroughly reviewed and modified to suit each facility before being applied to nuclear fuel cycle facilities.

In this study, a correlation analysis of odor was performed in order to assess the reliability and the field applicability of the Odorous gas sensor for continuous real-time monitoring. Hydrogen sulfide was found to have a correlation of 41.5~65.8%, and Ammonia is was found to have very low correlation in less than 200 ppb concentration. Reactivity evaluation result, hydrogen sulfide is the reactivity was higher than the low concentration condition of 100 ppb or less indicated by 31.3~36.4% in the 100 ppb or more high density condition based on the reference density value. For ammonia was very low reactivity in the low-concentration conditions below 200 ppb. TVOC and composite odor assessment did not occur Reactivity no reference concentration value, the specific comparison between both sensors showed a similar trend. In the same Odorous gas sensor accuracy between the result, 40.3~130.6% hydrogen sulfide, ammonia, 69.1~104.9%, TVOCs is 24.7~98.6%, exhibited human odor intensity from 5.5~33.2%.