Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.

代数建模语言（AML）大大简化了代数优化问题的实现。但是，仍然存在许多类型的优化问题，这些问题在大多数AML中都不容易体现。这些类别的问题通常在实施之前重新制定，这需要建模者付出大量的精力和时间，并且掩盖了原始问题的结构或环境。在这项工作中，我们演示了如何使用高级建模结构将Pyomo AML用来表示复杂的优化问题。我们专注于不确定性下的动态系统的操作，并演示了Pyomo扩展用于动态优化和随机规划的组合。我们使用动态半间歇式反应器模型和大型鼓泡流化床吸附器模型作为测试案例。