BioRTC model enables exploration of real time control strategies for stormwater biofilters

Pengfei Shen; Ana Deletic; Katia Bratieres; David T McCarthy

doi:10.1016/j.watres.2023.120793

BioRTC model enables exploration of real time control strategies for stormwater biofilters

Water Res. 2023 Dec 1:247:120793. doi: 10.1016/j.watres.2023.120793. Epub 2023 Oct 27.

Authors

Pengfei Shen¹, Ana Deletic², Katia Bratieres³, David T McCarthy⁴

Affiliations

¹ China TieGong Investment & Construction Group Co., Ltd, Beijing, PR China; Eco-Environmental Research and Development Center of China Railway Group Limited, PR China.
² School of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
³ BoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Clayton, Melbourne 3800, VIC, Australia.
⁴ School of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, Queensland, Australia; BoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Clayton, Melbourne 3800, VIC, Australia. Electronic address: david.mccarthy@qut.edu.au.

PMID: 37944196
DOI: 10.1016/j.watres.2023.120793

Abstract

Biofilters with real time control (RTC) have great potential to remove microbes from stormwater to protect human health for uses such as swimming and harvesting. However, RTC strategies need to be further explored and optimised for each specific location or end-use. This paper demonstrates that the newly developed BioRTC model can fulfil this requirement and allow effective and efficient exploration of the potential of RTC applications. We describe the development of BioRTC as the first RTC model for stormwater biofilters, including: selection of a 'base' model for microbial removal prediction, its modification to include RTC capabilities, as well as calibration and validation. BioRTC adequately predicted the performance of two previously developed RTC strategies, with Nash Sutcliffe Efficiency (E_c) ranging from 0.65 to 0.80. In addition, high parameter transferability was demonstrated during model validation, where we employed the parameter sets calibrated for another biofilter study without RTC to predict the performance of RTC biofilters. We then employed the BioRTC model to explore RTC applications on a hypothetical biofilter system located at the outlet of an existing catchment. With different scenarios, we tested the impact of input parameters such as RTC set-points and design characteristics, and evaluated the influence of operational conditions on the microbial removal performance of the hypothetical biofilter with RTC. The results showed that strategy rules, set-point values, and biofilter design all govern the performance of RTC biofilters, and that operational conditions could impact the suitability of different RTC strategies. Particularly, the presence of Pareto fronts established that muti-objective optimisation is necessary to balance competing needs. These results underscore the importance of RTC, which allows for local experimentation, climate change adaptation, and adjustment to changing demands for the harvested water. Furthermore, they illustrate the practical use of the newly developed BioRTC model, enabling researchers and practitioners to explore and assess potential RTC strategies and scenarios quickly and cost-effectively.

Keywords: E. coli; Microbes; Modelling; Real time control; Stormwater biofilters; Stormwater harvesting.

MeSH terms

Calibration
Escherichia coli
Filtration / methods
Humans
Rain
Water Purification* / methods