Department of Civil & Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576, Singapore.
Research and Development Department, Xylem Water Solutions Singapore Pte Ltd, 3A International Business Park, Tower B, #10-10/18, ICON@IBP, 609935, Singapore.
Chemosphere. 2023 Nov;341:140011. doi: 10.1016/j.chemosphere.2023.140011. Epub 2023 Aug 30.
The quality of water changes from source to tap, presenting challenges in maintaining consistent water quality across the system. Predicting water quality in distribution systems, including disinfectant residual loss and by-product formation, has been the subject of research since the early 1990s. Although numerous models have been proposed to predict residual chlorine decay, disputes exist among researchers and experts over the superiority of certain models. Accordingly, this study modified the existing process-based bulk decay models by replacing the initial Total Residual Chlorine (TRC) concentration parameter with TRC demand, leading to an improvement in the models' performance. The modification resulted in a 38.03%, 28.02%, 23.11%, and 33.29% average improvement in Mean Squared Error (MSE) values for the First Order Model (FOM), Parallel First Order Model (PFOM), Second Order Model (SOM), and Parallel Second Order Model (PSOM), respectively. The study also introduced an online predictive method based on a Machine Learning (ML) algorithm that predicts the first-order TRC bulk decay rate by using water quality parameters as inputs. A Gaussian Process Regression (GPR) model was used to predict the kinetic parameters in FOM, which accurately predicted the test sets for most of the cases. In addition, a new methodology was proposed in this study for predicting TRC in water distribution systems that incorporates the variability of source natural organic matter, operational actions, and water demands. This method seeks to develop high-fidelity and robust water quality predictions that provide operational decision support for optimized distribution system management. In conclusion, this study emphasizes the importance of understanding water quality changes from source to tap and the challenges of maintaining consistent water quality across the system. The study suggests modifying existing models and introducing a novel methodology for predicting residual chlorine in water distribution systems that can improve water quality management and, ultimately, better public health outcomes.
水质从源头到龙头都会发生变化，这给整个系统保持水质的一致性带来了挑战。自 20 世纪 90 年代初以来，预测包括消毒剂残留损失和副产物形成在内的配水系统水质一直是研究的主题。虽然已经提出了许多模型来预测余氯衰减，但研究人员和专家在某些模型的优越性方面存在争议。因此，本研究通过用余氯需求代替初始总余氯（TRC）浓度参数，对现有的基于过程的批量衰减模型进行了修改，从而提高了模型的性能。该修改使一阶模型（FOM）、平行一阶模型（PFOM）、二阶模型（SOM）和平行二阶模型（PSOM）的平均均方误差（MSE）值分别提高了 38.03%、28.02%、23.11%和 33.29%。本研究还引入了一种基于机器学习（ML）算法的在线预测方法，该方法通过将水质参数作为输入来预测一阶 TRC 批量衰减速率。使用高斯过程回归（GPR）模型预测 FOM 中的动力学参数，该模型能够准确预测大多数情况下的测试集。此外，本研究提出了一种新的方法来预测配水系统中的 TRC，该方法考虑了水源天然有机物、操作动作和水需求的可变性。这种方法旨在开发高保真度和稳健的水质预测模型，为优化的配水系统管理提供操作决策支持。总之，本研究强调了从源头到龙头了解水质变化以及在整个系统中保持水质一致性的重要性。研究建议修改现有的模型并引入一种新的方法来预测配水系统中的余氯，以改善水质管理，最终改善公众健康状况。
