Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach
Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach
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DOI：
10.3390/w11020391
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发表时间：
2019-02-01
期刊：
影响因子：
3.4
通讯作者：
Shen, Chao
中科院分区：
文献类型：
--
作者：
Liu, Chuankun;Hu, Yue;Shen, Chao
人工湖系统工程造价与水质处理之间的权衡对工程决策有重要影响。然而，决策者几乎没有科学工具来平衡工程成本和相应的水处理。在这项研究中，提出了一个框架，集成数值模拟，代理模型和多目标优化。该框架被应用于中国成都的一个实际案例。开发了一个水质模型（MIKE 21），为替代建模提供训练数据集。利用人工神经网络和支持向量机训练代理模型。两种替代模型均经验证，决定系数（R-2）大于0.98。SVM在有限的训练数据量下表现得更稳定，而ANN在更多的训练样本下表现出更高的精度。采用遗传算法建立了以降低工程造价和目标水体污染物浓度为目标的多目标优化模型。确定了处理后的最佳目标浓度，其特征在于人工湖中的氨浓度（1.3 mg/L）。此外，不同的水质在上游河流的情景进行了评估。在假设未来上游水质恶化的情况下，预处理在总成本中的最优比例不断增加。
The tradeoff between engineering costs and water treatment of the artificial lake system has a significant effect on engineering decision-making. However, decision-makers have little access to scientific tools to balance engineering costs against corresponding water treatment. In this study, a framework integrating numerical modeling, surrogate models and multi-objective optimization is proposed. This framework was applied to a practical case in Chengdu, China. A water quality model (MIKE21) was developed, providing training datasets for surrogate modeling. The Artificial Neural Network (ANN) and Support Vector Machine (SVM) were utilized for training surrogate models. Both surrogate models were validated with the coefficient of determinations (R-2) greater than 0.98. SVM performed more stably with limited training data sizes while ANN demonstrated higher accuracies with more training samples. The multi-objective optimization model was developed using the genetic algorithm, with targets of reducing both engineering costs and target aquatic pollutant concentrations. An optimal target concentration after treatment was identified, characterized by the ammonia concentration (1.3 mg/L) in the artificial lake. Furthermore, scenarios with varying water quality in the upstream river were evaluated. Given the assumption of deteriorated upstream water quality in the future, the optimal proportion of pre-treatment in the total costs is increasing.