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.
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.