Trickling filters are one of the most commonly used wastewater treatment technologies in the UK and throughout much of the world. However, despite the vast practical experience of using trickling filters, there remains a lack of robust theoretical understanding of the factors influencing their performance and of universally applicable recommendations for maintaining or improving removal efficiencies. Previous research has shown that the performance of trickling filters can vary enormously from one wastewater treatment facility to another and with respect to time of year and/or day. If trickling filter systems fail, on-site treatment capacity is not only reduced, but there can be environmental and economic consequences such as increased risk of permit breaches for ammonia, suspended solids and biochemical oxygen demand (BOD).
Southern Water is one of the only water and sewerage companies (WASC) in the UK to have installed online telemetric monitors across its trickling filter treatment systems over such a long period of time. These monitors sample the trickling filter’s influent and final effluent for turbidity, ammonia, and flow, among other parameters. As a result, the company has more than 10 years of diurnal as well as more frequently sampled data for tricking filter operations over a large number of treatment works, which provides a unique opportunity for optimisation.
The existing models for assessing trickling filter operational performance are now several decades old and based on limited data samples and simplistic techniques. By processing the available Southern Water data and applying a wide range of classical and contemporary statistical and data mining techniques, the successful PhD candidate is expected to generate practically and theoretically significant ‘new’ knowledge in the data, which will provide a valuable insight into the operational functioning of tricking filters, including trend and pattern identification.
The first phase of the project will involve data familiarisation and is likely to involve site visits to several wastewater treatment plants employing trickling filter technologies, analyses of both academic and industry-based scientific literature, initial modelling with the help of traditional techniques such as linear regression. The findings will be set into context with existing theoretical understanding of trickling filter processes and the data published in accordance with Southern Water requirements policies and regulatory restrictions.
The subsequent direction of the project will depend significantly on the results of the first phase and on the background, interests, and goals of the successful candidate. The supervisory team anticipates at least the following options:
- an industry-led approach concerned predominantly with data-driven improvements of trickling filter operations, which may be immediately implemented on site for validation;
- an environmental sciences approach leading to an enhanced understanding of the data in the context of biological and chemophysical processes;
- a data science approach resulting in new methods, tools and recommendations for processing this specific sort of data.
Candidates should ideally have a strong background in both environmental sciences (particularly in water and sanitation), and data science or statistics, including theoretical knowledge and practical experience in data analysis and data mining. Knowledge of a suitable programming language such as R, Python or C++ is desirable. However, candidates with a significant background in one of the required areas and a willingness and aptitude to improve quickly in the other will also be considered. The successful candidate will be able to study several relevant masters=level taught modules at the University of Brighton if necessary.
The successful candidate will divide their time between the university’s Moulsecoomb campus in Brighton and at the nearby Southern Water’s offices in Falmer. Southern Water has presented a fantastic opportunity for on-site work, learning and career development, and are interested and hopeful to offer opportunities in the future for like-minded candidates.