Chemoinformatic tool for predicting removal of CECs in wastewater and stormwater treatment assets

Chemoinformatic tool for predicting removal of CECs in wastewater and stormwater treatment assets

Current tools do not allow utilities to predict the removal of chemicals of emerging concern (CECs) in wastewater treatment lagoons; thus utilities are not able to assess risk associated with the treated water, and optimise treatment conditions.


Furthermore, climate change impacts and reduced fresh water sources force utilities to utilise alternative water sources within the concept of the circular economy (including but not limited to stormwater).

To address compliance with new demanding regulations for varying sources (e.g., General Environmental Duty in Victoria) coupled with budget restrictions, utilities need a cost-effective tool to identify the most problematic CECs and their removal in natural treatment processes.

Sunlight-induced photodegradation followed by chemical and/or biological breakdown is a major removal pathway for trace organics in both surface water and wastewater lagoons. The photodegradation of CECs depends on their structure, whether it is via direct or indirect photolysis (latter involving the production of photochemically produced intermediates (PPRIs), and is correlated to a range of wastewater quality parameters.

However, models developed based on limited data using logarithms or linear functions are not able to produce accurate results, especially when the relationships between variables are highly complex and non-linear. Therefore, to further elucidate the correlations between the lagoon wastewater and plant performance in terms of removal of CECs, it is proposed that machine learning be adopted as it performs better with complex datasets. 

Project description schematic:


The overall aim is to develop a chemoinformatic tool to enable prediction of the photodegradation and thus removal of CECs during lagoon wastewater treatment. The specific objectives are to:

(i)            Develop QSAR algorithms for the prediction of 2nd order reaction rates between the 4 PPRIs and the different CEC structural groups.

(ii)           Confirm correlations between wastewater characteristics, temperature and sunlight irradiance and PPRI concentrations.

(iii)          Use a big data approach to develop algorithms or machine learning models to further build correlations between the wastewater characteristics, temperature, sunlight irradiance and PPRI concentrations.

(iv)          Trial the model at a wastewater treatment lagoon to determine the robustness of the approach.

The longer term objective is to extend the work to include more CEC moieties, a longer term onsite study, and trials at lagoon systems at other WWTPs (particularly in different climate zones).

To learn more please download below attachement.

WaterRA Contact

Dr Arash Zamyadi | Research Manager 

Amount being sought


Due Date

30th Apr, 2021