This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in the integration of concepts from the fields of Bayesian optimization and derivative-free optimization. The proposed schemes embed a physical model and rely on trust-region ideas to minimize risk during the exploration, while employing Gaussian process regression to capture the plant-model mismatch in a non-parametric way and drive the exploration by means of acquisition functions. The benefits of using an acquisition function, knowing the process noise level, or specifying a nominal process model are analyzed on numerical case studies, including a semi-batch photobioreactor optimization problem with a dozen decision variables.

本文研究了一类新的修饰符自适应方案，以克服不确定过程的实时优化中的植物模型不匹配。主要贡献在于贝叶斯优化和无导数优化领域的概念集成。拟议的方案嵌入了一个物理模型，并依靠信任区域的思想将勘探过程中的风险降到最低，同时采用高斯过程回归以非参数的方式捕获工厂模型的不匹配，并通过获取函数来驱动勘探。在数字案例研究中分析了使用获取功能，了解过程噪声水平或指定标称过程模型的好处，其中包括带有十二个决策变量的半批量光生物反应器优化问题。