Multi-purpose economic optimal experiment design applied to model based optimal control. (2nd November 2016)

• Record Type: Journal Article
• Authors: Telen, D.; Houska, B.; Logist, F.; Van Impe, J.
• Is Part Of: Computers & chemical engineering. Volume 94(2016)
• Page Start: 212
• Page End: 220
• Publication Date: 2016-11-02
• Subjects: Optimal experiment design -- Optimality loss -- Multi-purpose design -- Optimal control -- Variance–covariance matrix; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.07.004
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract : Highlights: Formulation of economic optimal experiment design is discussed. A min–max formulation for multi-purpose economic experiment design is derived. A reformulation strategy is proposed to allow solution in standard software. Illustration of the proposed technique on two biochemical case studies. Abstract: In contrast to classical experiment design methods, often based on alphabetic criteria, economic optimal experiment design assumes that our ultimate goal is to solve an optimization or optimal control problem. As the system parameters of physical models are in practice always estimated from measurements, they cannot be assumed to be exact. Thus, if we solve the model based optimization problem using the estimated, non-exact parameters, an inevitable loss of optimality is faced. The aim of economic optimal experiment design is precisely to plan an experiment in such a way that the expected loss of optimality in the optimization is minimized. This paper analyzes the question how to design economic experiments under the assumption that we have more than one candidate objective function. Here, we want to take measurements and estimate the parameters before we actually decide which objective we want to minimize.