不确定性流域污染负荷优化分配模型及其应用

Uncertainty in watershed pollutants load allocation optimization model and its application

流域优化决策模型以最优化建模方法指导流域管理决策过程,然而流域系统的不确定性会导致决策存在一定风险.本研究通过建立区间参数机会约束线性规划(ICILP)模型来处理流域决策过程中的不确定性,并将该模型运用于太滆运河流域优化决策中,探讨在不同违反概率下系统最优解.结果表明,随着允许入湖量约束违反概率增加,系统对污染物削减量和削减成本有所降低.由于受到经济成本和削减量约束,系统优先减小环境代价较大、削减效率较低的工程项目规模.但受到最低处理率约束,违反概率增加到一定水平时各工程项目趋于定值.虽然较高的违反概率使系统成本降低,但也会导致削减效率降低,不利于流域保护.因此,在实际管理中应根据管理需求选择合适的削减方案以达到保护流域水质的目标.

Optimization model can be used to guide the decision making process in watershed management, but the uncertainty of watershed system could lead to some risk decision. In this study, an interval-parameter chance-constrained linear programming （ICILP） model was developed to deal with the uncertainty in watershed decision making process, and was applied to Taige Canal Watershed optimization, to explore the optimal system solution in different violation levels. The results show that with the increase of violation levels of water environmental capacity, pollutants and costs reduction within the system decreased. Due to the cost and reduction constraint, the model will be prone to reduce the project with larger environmental consumption and/or with lower cut efficiency. But under the constraint of minimum treatment rate, each project reaches a fixed value when the violation probability increases to a certain level. Higher violation probability could reduce system costs, but would also reduce cut efficiency, thus there is no benefit to watershed protection. Therefore, in practice, appropriate reduction plan should be selected according to the management demand to achieve the protection of basin water quality.