Methodological and computational framework for model-based design of parallel experiment campaigns under uncertainty

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Marco Sandrin , Constantinos C. Pantelides , Benoît Chachuat
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引用次数: 0

Abstract

The model-based determination of maximally-informative campaigns involving multiple parallel experimental runs remains a challenging task. Effort-based methodologies are well suited to the design of such experiment campaigns through discretizing the experiment control domain into a finite sample of candidate experiments. However, this approach can lead to suboptimal results if the discretization fails to cover the experiment domain sufficiently well. We present a comprehensive computational framework that combines an effort-based optimization step with a gradient-based refinement as part of an iterative procedure. The convexity of classical design criteria in the effort space allows for a globally optimal effort selection over the discretization, which is exploited to warm-start the gradient-based search for a refined discretization. Our framework also considers parametric model uncertainty by formulating risk-inclined, risk-neutral and risk-averse design criteria, and it enables the solution of exact designs in the effort-based step. Through the case study of a fed-batch fermentation, we show that the integrated effort-based optimization with gradient-based refinement procedure consistently outperforms an effort-only optimization. The results demonstrate the benefits of robust design approaches compared to their local counterparts, and establish the computational tractability of the framework in computing robust experiment campaigns with up to a dozen dimensions.
不确定条件下基于模型的并行实验设计方法与计算框架
基于模型的确定包含多个并行实验运行的信息量最大的运动仍然是一项具有挑战性的任务。通过将实验控制域离散为候选实验的有限样本,基于努力的方法非常适合于此类实验活动的设计。然而,如果离散化不能很好地覆盖实验域,这种方法可能导致次优结果。我们提出了一个综合的计算框架,将基于努力的优化步骤与基于梯度的细化相结合,作为迭代过程的一部分。经典设计准则在努力空间中的凸性允许在离散化过程中进行全局最优努力选择,并利用这一点来热启动基于梯度的优化离散化搜索。我们的框架还通过制定风险倾向、风险中性和风险厌恶的设计标准来考虑参数模型的不确定性,并且它能够在基于努力的步骤中解决精确的设计。通过对补料分批发酵的案例研究,我们证明了基于努力的优化与基于梯度的优化过程的集成始终优于仅基于努力的优化。结果证明了鲁棒设计方法与本地同类方法相比的优势,并建立了该框架在计算多达十二个维度的鲁棒实验活动时的计算可追溯性。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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