In the processing industry, dynamic product transitions are essential for achieving high product quality, minimizing the use of raw materials and energy and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this article, a data‐driven Bayesian approach for optimal dynamic product transitions is proposed. The proposed approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first‐principles dynamic mathematical model for drawing optimal solutions. The approach is applied to three case studies, and the results are comparable in performance quality with those obtained using a traditional gradient‐based optimization approach. The results show that the proposed approach is able to find optimal transition trajectories that meet the product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real‐world applications. The proposed approach has several advantages over traditional optimization approaches, including being data driven, able to cope with noisy measurements, computationally efficient, and it requires modest computational effort. Complex online optimal control problems can benefit from adopting a data‐driven Bayesian optimization scheme.

中文翻译：

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用于最佳动态产品转换的数据驱动贝叶斯方法

在加工业中，动态产品转型对于实现高产品质量、最大限度地减少原材料和能源的使用以及降低生产成本至关重要。然而，由于过程的复杂动态性和测量的不确定性，优化动态产品转换是一项具有挑战性的任务。在本文中，提出了一种用于最佳动态产品转换的数据驱动贝叶斯方法。所提出的方法基于使用贝叶斯优化算法解决的动态优化问题。这种方法用于流程优化任务的优点之一是它不需要第一原理动态数学模型来得出最佳解决方案。该方法应用于三个案例研究，其结果在性能质量上与使用传统的基于梯度的优化方法获得的结果相当。结果表明，所提出的方法能够使用平滑的控制动作找到满足产品成分要求的最佳过渡轨迹。该方法还能够应对噪声测量，这是现实应用中的一个重要功能。与传统优化方法相比，所提出的方法具有多个优点，包括数据驱动、能够应对噪声测量、计算效率高，并且需要适度的计算工作。采用数据驱动的贝叶斯优化方案可以使复杂的在线最优控制问题受益。