Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence
Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence
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DOI：
10.1155/2018/2480365
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Z. Geem;S. Chung;Jin-Hong Kim
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文献类型：
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作者：
Z. Geem;S. Chung;Jin-Hong Kim
废水污染对河流水环境的可持续性造成严重的负面影响。因此，这一复杂系统的准确建模及其经济有效的处理和再利用决策是非常重要的，因为这个优化过程涉及到经济支出，社会健康和环境恶化。为了优化这个复杂的系统，我们可以考虑三个处理或回用方案，如微筛过滤，硝化和施肥灌溉的两个现有的选择，如沉降和生物氧化。这种环境优化的目标是最大限度地减少生命周期成本的经济支出，同时满足地下水质量方面的公共卫生标准和河流水质方面的环境标准。特别是，本研究改进了现有的优化模型，通过解析微分法精确定位溶解氧下降曲线的临界亏损位置。此外，建议的配方考虑更实际的限制，如灌溉面积的最大尺寸和最小量的过滤处理过程。通过使用进化算法，命名为参数设置自由和声搜索算法，所得到的结果表明，该模型成功地找到最优解，同时方便地定位临界赤字点。
River water pollution by wastewater can cause significant negative impact on the aquatic sustainability. Hence, accurate modeling of this complicated system and its cost-effective treatment and reuse decision is very important because this optimization process is related to economic expenditure, societal health, and environmental deterioration. In order to optimize this complex system, we may consider three treatment or reuse options such as microscreening filtration, nitrification, and fertilization-oriented irrigation on top of two existing options such as settling and biological oxidation. The objective of this environmental optimization is to minimize the economic expenditure of life cycle costs while satisfying the public health standard in terms of groundwater quality and the environmental standard in terms of river water quality. Particularly, this study improves existing optimization model by pinpointing the critical deficit location of dissolved oxygen sag curve by using analytic differentiation. Also, the proposed formulation considers more practical constraints such as maximal size of irrigation area and minimal amount of filtration treatment process. The results obtained by using an evolutionary algorithm, named a parameter-setting-free harmony search algorithm, show that the proposed model successfully finds optimal solutions while conveniently locating the critical deficit point.