Decision support for the development, simulation and optimization of dynamic process models

doi:10.1007/s11705-021-2046-x

Front. Chem. Sci. Eng.

2022, Vol. 16

Issue (2) : 210-220 https://doi.org/10.1007/s11705-021-2046-x

RESEARCH ARTICLE

Decision support for the development, simulation and optimization of dynamic process models

Norbert Asprion¹(

), Roger Böttcher¹, Jan Schwientek², Johannes Höller², Patrick Schwartz², Charlie Vanaret², Michael Bortz²

¹. BASF SE, 67063 Ludwigshafen, Germany
². Fraunhofer ITWM, 67663 Kaiserslautern, Germany

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Abstract

Simulation is besides experimentation the major method for designing, analyzing and optimizing chemical processes. The ability of simulations to reflect real process behavior strongly depends on model quality. Validation and adaption of process models are usually based on available plant data. Using such a model in various simulation and optimization studies can support the process designer in his task. Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation. In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.

Keywords decision support multicriteria optimization model validation dynamic model sensitivity analysis

Corresponding Author(s): Norbert Asprion

Just Accepted Date: 11 March 2021 Online First Date: 28 April 2021 Issue Date: 10 January 2022

Cite this article:

Norbert Asprion,Roger Böttcher,Jan Schwientek, et al. Decision support for the development, simulation and optimization of dynamic process models[J]. Front. Chem. Sci. Eng., 2022, 16(2): 210-220.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-021-2046-x
https://academic.hep.com.cn/fcse/EN/Y2022/V16/I2/210

Fig.1 Modeling, simulation and optimization workflow to support decision making in the development of dynamic chemical processes.

Fig.2 Principle of combining models with data to set up the least squares problem for parameter estimation.

Fig.3 Scheme of a batch distillation with different sample lines for product (P) and slop cuts (S).

Tab.1 Initial values for the controls

Tab.2 Final, optimal values for the controls

Fig.4 Comparison of the dynamic simulation based on the initial (gray lines) and the optimized values (black lines) with the measured vapor concentration at the top of the column (symbols): …, △ Cyclohexane; —, ○ n-Heptane; – –, □ Toluene.

Fig.5 Scheme of the semi-batch Williams-Otto reactor.

Fig.6 (a) Pareto-optimal solution of the semi-batch Williams-Otto reactor problem in the MCO Navigator of the CHEMADIS results (Red squares are showing the reference runs, which are results of a single-objective optimization. In pastel-red the reoptimized reference runs are shown these are obtained by optimizing the other objective with the constraint not to become worse in the objective of the reference run. The green symbols show the results of minimizing a weighted sum. The weights are determined by the sandwiching algorithm [10]). Visualization of trajectories of selected indicators of the semi-batch Williams-Otto reactor problem: (b) reactor temperature (CONSTANT Tr); (c) reaction volume (CONSTANT Vr); (d) control for the feed (FUNCTION FBSoll); (e) control for the wall temperature (FUNCTION TWSoll is scaled with factor 1000). The trajectory of the selected point in (a) is shown with a bigger symbol and a bold line.

Fig.7 Principle of the bioreactor for the production of lysine (P) (Reactor holdup (volume V) includes besides the product P, biomass X and substrate S. The feed rate u has a substrate concentration C_S,F).

Fig.8 Results of the multicriteria optimization of the lysine example with non-convex Pareto frontier and non-convex navigation (orange): (a) slider view with navigation; (b) 2D projection of Pareto frontier; (c) trajectories of the Pareto-optimal points (For the symbols and lines the same color code than in Fig. 6 is used. Additionally, there are blue symbols and lines indicating Pascoletti-Serafini runs resulting from the hyperboxing algorithm (for details see ref. [10])).

Tab.3 Parameters of the discretized control variable of the solution for a total batch time of t_F = 24.882 h investigated with SA

Fig.9 Results of SA with factorial design for the solution of Table 3: trajectories for (a) mass of product P, (b) the objective reactor productivity

J 1

and (c) the feed flow rate as control variable u.

Fig.10 Results of MCO for enumerated uncertainty scenarios:

μ p

;

π p,2

; 0.9

μ p

; 1.1

π p,2

; 1.1

μ p

; 1.1

π p,2

; 1.1

μ p

; 0.9

π p,2

; 0.9

μ p

; 0.9

π p,2

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