Real-time optimization (RTO) is an established technology, where the process economics are optimized using rigourous steady-state models. However, a fundamental limiting factor of current static RTO implementation is the steady-state wait time. We propose a “hybrid” approach where the model adaptation is done using dynamic models and transient measurements and the optimization is performed using static models. Using an oil production network optimization as case study, we show that the Hybrid RTO can provide similar performance to dynamic optimization in terms of convergence rate to the optimal point, at computation times similar to static RTO. The paper also provides some discussions on static versus dynamic optimization problem formulations.

实时优化（RTO）是一项成熟的技术，其中使用严格的稳态模型对过程经济性进行了优化。但是，当前静态RTO实现的基本限制因素是稳态等待时间。我们提出了一种“混合”方法，其中使用动态模型和瞬态测量来完成模型调整，并使用静态模型来进行优化。使用石油生产网络优化作为案例研究，我们证明了混合RTO可以在收敛到最佳点的速度上提供与动态优化相似的性能，并且计算时间与静态RTO相似。本文还提供了一些关于静态优化与动态优化问题公式的讨论。