The aim of this project is to use metabolic models, integrating empirical observations from various sources within a common mathematical framework, reflecting the current state of knowledge, to evaluate the steady-state coexistence ratio between phosphorus accumulating organisms (PAO) and glycogen accumulating organisms (GAO) with respect to a range of initial conditions characteristic of full-scale Enhanced Biological Phosphorus Removal (EBPR) installations.

This project aims to develop and deploy mathematical models to evaluate and unite elements of the composition of the microbial community, their respective ecological function, inter-phyla interactions and process design and operation. The component objectives of this project include but are not limited to: (1) assembling available data into a coherent working database; (2) investigating and assessing statistical tools to make use of the available evidence to infer the nature of relationships among different groups within EBPR microbial consortia, their relation to the process configuration and operational parameters and environmental conditions, and (3) mapping the coexistence regions in which satisfactory EBPR removal can be expected.

Project outline

Wastewater treatment plants employing EBPR are known to exhibit poor performance attributed to population imbalances of the functional microorganisms. An understanding of the root causes behind deteriorated performance, and more importantly of reliable corrective measures have thus far proven elusive. Among the perennial uncertainties is the composition of PAO relative to GAO required for ensure satisfactory EBPR performance. Although the consensus has been that process instability is caused by the proliferation of GAO, this observation does not always hold for studies at the laboratory-scale nor for those at full-scale facilities employing EBPR.

The fundamental question which this project seeks to answer is: in what ratio can PAO and GAO coexist in full-scale EBPR systems that result in satisfactory phosphorus removal? This will be addressed by formulating an integrated metabolic model taking into account anaerobic, aerobic and anoxic behaviour of the currently recognised sub-groups of PAO and GAO. Global sensitivity analysis will be used to quantify the uncertainty of model outputs based on the uncertainty of available experimental data. The steady-state coexistence ratio between PAO and GAO given different initial conditions will be mapped and regions of satisfactory EBPR performance will be identified.

The expected variance of the output will be incorporated into the map to examine any overlapping areas. This can be further explored by varying the threshold value for desired level of phosphorus removal, as the location and size of the resultant intersections would indicate the level of confidence in the interpretation of the map’s features in a way that is consistent with the current state of knowledge of the EBPR system. Similarly, extending the model with alternative metabolic pathways will shed light on the importance of knowing the precise biochemical mechanisms underpinning EBPR from the standpoint of process control and monitoring.

Science

The principal outcome of this project is a reference tool with which to diagnose operational problems in relation to the ‘health’ of resident microbial communities in wastewater treatment plants. The results of this project will be disseminated through publications in scientific journals. The novelty of this project lies in (1) the use of network theory and statistical inference to re-evaluate the quality of published datasets and (2) the use of global as opposed to local sensitivity analysis to both quantify the uncertainties inherent in the input data and model structure, as well as to side-step the need for model calibration prior to long-term simulation studies.

Impact

The ambition of this project is to improve the holistic understanding of the microbial consortia relevant to EBPR. In particular, this project will (1) enable the formulation of concrete strategies to cultivate the right communities to ensure consistently satisfactory performance, and thus compliance, of wastewater treatment facilities employing this technology, (2) provide the framework for design optimisation of future EBPR processes and (3) provide the tools to enable systematic identification of knowledge deficiencies, thereby laying-out a roadmap of key areas of future research.

More broadly, the proposed tools developed in this project could find application in the exploitation of any engineered non-mono-culture biological system, answering questions such as: which are the key parameters to monitor in full-scale treatment and/or production facilities and what is the (smallest) required sample size necessary to make this judgment with confidence.

This PhD project is supervised by Dr Ana Lanham, Dr Tim Rogers and Professor Jan Hofman.