论文标题
使用自适应和探索性框架对工业制冷过程进行安全优化
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework
论文作者
论文摘要
许多工业应用依赖于实时优化来改善关键绩效指标。在未知的过程特征的情况下，实时优化变得具有挑战性，尤其是为了满足安全限制。在本文中，我们演示了适应性和探索性的实时优化框架在工业制冷过程中的应用，在该过程中，我们通过过程控制目标的变化和探索来学习过程特征，以满足安全性约束。我们通过使用高斯工艺量化了冷藏厂未知压缩机特性的不确定性，并将这种不确定性纳入实时优化问题的目标函数中，作为加权成本项。我们适应地控制该术语的重量以推动探索。我们的仿真实验的结果表明，所提出的方法可以帮助提高所考虑的制冷过程的能效，并紧密近似于具有有关压缩机性能特征的完整信息的解决方案的性能。
Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. In this paper, we demonstrate the application of an adaptive and explorative real-time optimization framework to an industrial refrigeration process, where we learn the process characteristics through changes in process control targets and through exploration to satisfy safety constraints. We quantify the uncertainty in unknown compressor characteristics of the refrigeration plant by using Gaussian processes and incorporate this uncertainty into the objective function of the real-time optimization problem as a weighted cost term. We adaptively control the weight of this term to drive exploration. The results of our simulation experiments indicate the proposed approach can help to increase the energy efficiency of the considered refrigeration process, closely approximating the performance of a solution that has complete information about the compressor performance characteristics.
