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As wastewater treatment plants transition to water resource recovery facilities, the need for improved control and consequently supervision increases. Despite the large volume of research that has been performed on this topic, the use in industry is scarce. Practical implementation is challenging due to the nature of the process, and a lack of standardisation in the research results in uncertainty as to the state of the art. This is one of the main challenges identified. 

Experimental work is performed using the Benchmark Simulation Model No. 1 to identify monitoring requirements and evaluate the performance of univariate fault detection methods. For the former, residual based process fault signatures are used to determine minimal sensor requirements based on detectability and isolability goals. Sensor faults are the focus of the latter issue, using the Shewhart, cumulative sum, and exponentially weighted moving average control charts to detect bias and drift faults in a controlled variable sensor. 

The use of a standard model and known fault detection methods is useful to establish a baseline for future work. Given the lack of standardised use in industry this is considered critical. Both proposed methods emphasise ease of visualisation which is beneficial for industrial implementation. 

The municipal wastewater treatment sector is undergoing a significant transformation in the transition from traditional wastewater treatment plants to resource recovery facilities. This shift, supported by policy initiatives and targeted research efforts, necessitates advances in process supervision and control to meet new demands for resource efficiency and effluent quality. Emerging factors such as an increased interest in digitalisation and the push towards a circular society further drive these advancements. However, challenges which have historically limited developments, such as harsh operating conditions for sensors and limited implementation of supervision technologies are still critical to address in this transformation. 

To advance these developments, this research explored the impact and detection of sensor and process faults within biological wastewater treatment processes using simulation-based studies with the Benchmark Simulation Model No. 1. The effects of these faults were evaluated by observing changes in operational cost, effluent quality, and controller performance. Of the tested faults, decreases in the growth rates of the autotrophic and heterotrophic bacteria most strongly affected the performance of the process. To detect and isolate the process faults of interest, sign-based fault signatures were identified from commonly available measurements, and the identified signatures were found to be capable of fault identification. For detecting sensor faults, control chart-based methods, including the Shewhart, cumulative sum, and exponentially weighted moving average (EWMA) univariate charts, as well as the multivariate EWMA chart, were applied and compared. The EWMA-based charts showed the best performance, especially in detecting slow drift faults. 

Throughout this research, emphasis was placed on reducing monitoring requirements by identifying critical measurements for effective process fault detection, and reducing potential dependencies on hardware redundancy through improved sensor fault detection. Additionally, methods that offer easy visualisation were prioritised for their potential to enhance understanding and interpretation, in hopes of facilitating the transition from research to practical application. Looking ahead, future work should investigate the ability of these methods to handle simultaneous faults and focus on their integration into full-scale systems.