Technische Universität Dresden, Institute of Urban Water Management, 01062 Dresden, Germany.
Fundação Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia, Av. Costa e Silva, s/n° - Bairro Universitário, CEP: 79070-900 Campo Grande, MS, Brazil.
Sci Total Environ. 2018 Sep 1;634:705-714. doi: 10.1016/j.scitotenv.2018.03.364. Epub 2018 Apr 9.
This study evaluated the influences of model structure and calibration data size on the modelling performance for the prediction of chlorine residuals in household drinking water storage tanks. The tank models, which consisted of two modules, i.e., hydraulic mixing and water quality modelling processes, were evaluated under identical calibration conditions. The hydraulic mixing modelling processes investigated included the continuously stirred tank reactor (CSTR) and multi-compartment (MC) methods, and the water quality modelling processes included first order (FO), single-reactant second order (SRSO), and variable reaction rate coefficients (VRRC) second order chlorine decay kinetics. Different combinations of these hydraulic mixing and water quality methods formed six tank models. Results show that by applying the same calibration datasets, the tank models that included the MC method for modelling the hydraulic mixing provided better predictions compared to the CSTR method. In terms of water quality modelling, VRRC kinetics showed better predictive abilities compared to FO and SRSO kinetics. It was also found that the overall tank model performance could be substantially improved when a proper method was chosen for the simulation of hydraulic mixing, i.e., the accuracy of the hydraulic mixing modelling plays a critical role in the accuracy of the tank model. Advances in water quality modelling improve the calibration process, i.e., the size of the datasets used for calibration could be reduced when a suitable kinetics method was applied. Although the accuracies of all six models increased with increasing calibration dataset size, the tank model that consisted of the MC and VRRC methods was the most suitable of the tank models as it could satisfactorily predict chlorine residuals in household tanks by using invariant parameters calibrated against the minimum dataset size.
本研究评估了模型结构和校准数据大小对家庭饮用水水箱中余氯预测模型性能的影响。在相同的校准条件下，评估了由两个模块组成的水箱模型，即水力混合和水质建模过程。研究的水力混合建模过程包括连续搅拌槽反应器（CSTR）和多腔（MC）方法，水质建模过程包括一级（FO）、单反应物二级（SRSO）和可变反应速率系数（VRRC）二级氯衰减动力学。这些水力混合和水质方法的不同组合形成了六个水箱模型。结果表明，通过应用相同的校准数据集，用于模拟水力混合的 MC 方法的水箱模型提供了比 CSTR 方法更好的预测结果。在水质建模方面，VRRC 动力学比 FO 和 SRSO 动力学具有更好的预测能力。还发现，当选择适当的方法模拟水力混合时，整体水箱模型性能可以得到显著提高，即水力混合建模的准确性对水箱模型的准确性起着至关重要的作用。水质建模的进步改进了校准过程，即当应用合适的动力学方法时，可以减少用于校准的数据集的大小。尽管所有六个模型的精度都随着校准数据集大小的增加而提高，但由 MC 和 VRRC 方法组成的水箱模型是最适合的模型，因为它可以使用针对最小数据集大小校准的不变参数来满意地预测家庭水箱中的余氯。
