考虑污染源强随机变化的感潮河流环境容量优化  

Optimization of waste load allocation with stochastic pollutant discharge in a tidal river basin

作　　者：林高松[1] 李适宇[1] 江峰[1] 

机构地区：[1]中山大学环境科学与工程学院,广东广州510275

出　　处：《水科学进展》2006年第3期317-322,共6页Advances in Water Science

基　　金：广东省"科技计划百项工程"资助项目(4202112)

摘　　要：考虑污染源强随机变化和感潮河流潮周期内动态水文条件对水质的影响,建立了优化污染负荷分配的流域水质管理模型。模型以总的允许排污量最大为目标函数,流域的水质控制点达标为约束条件。假设排污量是服从对数正态分布的随机变量,并且以潮周期内水质达标的概率作为衡量控制点达标的依据。采用遗传算法对该随机规划模型进行求解。研究结果表明,污染负荷优化分配结果能够满足随机条件下的水质达标率要求,并且与传统的确定性线性规划模型的分配结果相比有着明显差别。同时证实了遗传算法能够有效地解决复杂的随机规划模型。A river quality management model is proposed for the optimal waste load allocation in a tidal river basin, taking into account the impacts of stochastic pollutant discharge and dynamic hydrological conditions on water quality during a tidal cycle. The model is known as the maximization of total allowable waste load, subject to meeting water quality standards at checkpoints along the river. The waste load is regarded as a stochastic variable following the log-normal probability distribution based on statistical data, and the constrains on water quality levels are expressed in a probability form. A genetic algorithm is used to solve this stochastic programming model. A case study shows that the optimal waste load allocation can achieve water quality standards in stochastic conditions, and the model＇ s outcomes are clearly different from those of the conventional deterministic linear programming model. The computational results imply that a genetic algorithm is effective to solve complicated stochastic programming models.

关 键 词：环境容量 水质达标率 遗传算法 随机规划 感潮河流 

分 类 号：X213[环境科学与工程—环境科学]