一种已建立的直接测定温度的方法 管壁反应引起的氯衰变动力学 目前不存在。壁厚变化大 反应参数，可归因于不同的特性 对于管道，如年龄、材料和直径，也可以 估计这些参数的任务充其量是乏味的。 一种精确估计水的逆模型 建立了质量反应参数。参数 估计问题被描述为一个无约束问题 优化问题，其逆建模 使用水质模拟模型和 随机搜索遗传算法（GA）作为程序 采用了计算程序。 使用 GA，它试图通过 模仿遗传学中的自然选择过程， 可以为任何客户自动化和简化流程 适用的反应动力学。此外，基于遗传算法的 水质校准器适用于 许多未知的个体、整体或群体反应 参数。 将逆模型应用于两个实际网络 展示了模型计算各种 未知反应参数的类型。GA是 被发现是最适合与 大量的参数。包括22个参考文献、表格和图表。

An established method for directly determining the kinetics of chlorine decay attributable to pipe-wall reactions does not currently exist. Wide variability in wall reaction parameters, attributable to varying characteristics of a pipe such as age, material, and diameter, also make the task of estimating these parameters tedious at best. An inverse model that accurately estimates water quality reaction parameters is developed. The parameter estimation problem is formulated as an unconstrained optimization problem, for which an inverse modeling approach using a water quality simulation model and a stochastic search genetic algorithm (GA) as routines in the computational procedure is adopted. Calibrating a water distribution model using GA, which attempts to find the best solution by mimicking the natural selection process in genetics, can automate and simplify the process for any applicable reaction kinetics. In addition, a GA-based water quality calibrator is applicable to many unknown individual, global, or grouped reaction parameters. Applying the inverse model to two real-life networks demonstrated the model's ability to compute the various types of unknown reaction parameters. GA was found to be the most appropriate method to use with a large number of parameters. Includes 22 references, tables, figures.