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| Hauptseite > Publikationsdatenbank > Optimal experimental design of physical property measurements for optimal chemical process simulations |
| Hauptseite > Publikationsdatenbank > Optimal experimental design of physical property measurements for optimal chemical process simulations |
| Journal Article | FZJ-2022-04528 |
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2022
Science Direct
New York, NY [u.a.]
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Please use a persistent id in citations: http://hdl.handle.net/2128/32525 doi:10.1016/j.fluid.2022.113420
Abstract: Chemical process simulations depend on physical properties, which are usually available through property models with parameters estimated from experiments. The required experimental effort can be reduced using the method of Optimal Experimental Design (OED). OED reduces the number of experiments by minimising the expected uncertainty of the estimated parameters. In chemical engineering, however, the main purpose of an experiment is usually not to determine property parameters with minimum uncertainty but to simulate processes accurately. Therefore, this paper presents the OED of physical property measurements resulting in the most accurate process simulations: c-optimal experimental design (c-OED). c-OED aims to minimise the uncertainty of the process simulation results, which is estimated by linear uncertainty propagation from uncertain property parameters through the process model. We use c-OED to design liquid-liquid equilibrium and diffusion experiments minimising thermodynamic and economic performance metrics of three solvent-based processes. In all three case studies, the c-optimal design substantially reduces the experimental effort for the same simulation accuracy compared to state-of-the-art OED that neglects the process. Our findings are confirmed by a Monte-Carlo simulation of the designed experiments. Furthermore, we assess the limits of c-OED for highly nonlinear process models. Thus, the work shows how c-OED can successfully reduce experimental effort required for accurate process simulations by tailoring experimental designs to the process model.
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