Advanced Water Management Centre, Building 60, Research Road, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia.
Advanced Water Management Centre, Building 60, Research Road, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia; School of Automation Science & Engineering, South China University of Technology, Wushang Road, Guang Zhou, 510640, China.
Water Res. 2019 Feb 1;149:311-321. doi: 10.1016/j.watres.2018.11.021. Epub 2018 Nov 13.
Chemical dosing is a commonly used strategy for mitigating sewer corrosion and odour problems caused by sulfide production. Prediction of sewage flow variation in real-time is critical for the optimization of chemical dosing to achieve cost-effective mitigation of hydrogen sulfide (HS). Autoregressive (AR) models have previously been used for real-time sewage prediction. However, the prediction showed significant delays in wet weather conditions. In this paper, autoregressive with exogenous inputs (ARX) models are employed to reduce the delays with rainfall data used as model inputs. The model is applied to predicting sewage flows at two real-life sewage pumping stations (SPSs) with different hydraulic characteristics and climatic conditions. The calibrated models were capable of predicting flow rates in both cases, much more accurately than previously developed AR models under wet weather conditions. Simulation of on-line chemical dosing control based on the predicted flows showed excellent sulfide mitigation performance at reduced cost.
化学投加是一种常用的策略，用于减轻由于硫化物产生而导致的污水腐蚀和气味问题。实时预测污水流量变化对于优化化学投加以实现经济高效地减轻硫化氢（HS）至关重要。自回归（AR）模型以前曾用于实时污水预测。然而，在湿天气条件下，预测显示出明显的延迟。在本文中，使用带外生输入的自回归（ARX）模型来减少降雨数据作为模型输入所带来的延迟。该模型应用于预测两个具有不同水力特性和气候条件的实际污水泵站（SPS）的污水流量。校准后的模型能够在两种情况下预测流量，在湿天气条件下，其预测精度远远高于以前开发的 AR 模型。基于预测流量的在线化学投加控制模拟显示，在降低成本的同时，具有出色的硫化物缓解性能。
