饮用水处理厂的设计必须考虑几个目标和满足 多重约束。使用数学规划技术可以帮助确定 最佳处理厂设计。不幸的是，通常的做法是假设原水 特征和模型参数是已知的（完美信息），实际上，它们包括 自然变化或实验不确定性。包括环境中的可变性和不确定性 设计框架允许稳健的设计。提出了一个包含可变性的框架 以及常规处理下颗粒物去除设计公式的不确定性 （快速混合、絮凝、沉淀和过滤）。例如，确定性设计 这假设执行了完美的信息，并且显示出对影响因素的鲁棒性不强 可变性和模型参数不确定性。分别包含四个变量中的一个 设计过程中的进水参数或三个不确定模型参数增加了设计难度 成本高达2.21%。然而，由此产生的设计对个体而言是稳健的 易变性/不确定性。包括多个变量/不确定参数，导致 设计成本大于单个可变性/不确定性值之和。 包括13个参考文献、表格、图表。

The design of drinking water treatment plants must consider several objectives and satisfy multiple constraints. The use of mathematical programming techniques can assist in determining the optimal treatment plant design. Unfortunately, common practice assumes that raw water characteristics and model parameters are known (perfect information) when, in fact, they include either natural variation or experimental uncertainty. Including variability and uncertainty in the design framework allows for a robust design. A framework is presented for including variability and uncertainty into the design formulation for particulate removal under conventional treatment (rapid mix, flocculation, sedimentation, and filtration). As an example, a deterministic design that assumes perfect information is performed and shown not to be robust with respect to influent variability and model parameter uncertainty. Individually incorporating one of four variable influent parameters or three uncertain model parameters in the design process increased design costs up to 21.2%. The resulting designs were, however, robust with respect to the individual variabilities/uncertainties. Including multiple variable/uncertain parameters resulted in even greater design costs than the sum of the individual variability/uncertainty values. Includes 13 references, tables, figures.