Shen Pengfei, Deletic Ana, Bratieres Katia, McCarthy David T
China TieGong Investment & Construction Group Co., Ltd, Beijing, PR China; Eco-Environmental Research and Development Center of China Railway Group Limited, PR China.
School of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
Water Res. 2023 Dec 1;247:120793. doi: 10.1016/j.watres.2023.120793. Epub 2023 Oct 27.
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) 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.
具有实时控制（RTC）功能的生物滤池在去除雨水微生物以保护人类健康（如游泳和取水用途）方面具有巨大潜力。然而，针对每个特定地点或最终用途，RTC策略仍需进一步探索和优化。本文表明，新开发的BioRTC模型可以满足这一要求，并能有效且高效地探索RTC应用的潜力。我们将BioRTC的开发描述为首个用于雨水生物滤池的RTC模型，包括：选择用于微生物去除预测的“基础”模型、对其进行修改以纳入RTC功能，以及校准和验证。BioRTC充分预测了两种先前开发的RTC策略的性能，纳什-萨特克利夫效率（E）在0.65至0.80之间。此外，在模型验证过程中展示了较高的参数可转移性，我们使用为另一项无RTC的生物滤池研究校准的参数集来预测RTC生物滤池的性能。然后，我们使用BioRTC模型探索在现有集水区出口处的假设生物滤池系统上的RTC应用。在不同场景下，我们测试了诸如RTC设定点和设计特征等输入参数的影响，并评估了运行条件对具有RTC的假设生物滤池微生物去除性能的影响。结果表明，策略规则、设定点值和生物滤池设计均决定了RTC生物滤池的性能，并且运行条件可能影响不同RTC策略的适用性。特别是，帕累托前沿的存在表明需要进行多目标优化以平衡相互竞争的需求。这些结果强调了RTC的重要性，它允许进行本地试验、适应气候变化以及根据对收集水不断变化的需求进行调整。此外，它们说明了新开发的BioRTC模型的实际用途，使研究人员和从业者能够快速且经济高效地探索和评估潜在的RTC策略及场景。
