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. 2019 Mar;91(3):323-335.
doi: 10.1002/cite.201800118. Epub 2018 Dec 21.

Dynamic Optimization and Non-linear Model Predictive Control to Achieve Targeted Particle Morphologies

Wolfgang Gerlinger  1 , José Maria Asua  2 , Tomáš Chaloupka  3 , Johannes M M Faust  4 , Fredrik Gjertsen  5 , Shaghayegh Hamzehlou  2 , Svein Olav Hauger  5 , Ekkehard Jahns  1 , Preet J Joy  4 , Juraj Kosek  3 , Alexei Lapkin  6 , Jose Ramon Leiza  2 , Adel Mhamdi  4 , Alexander Mitsos  4 , Omar Naeem  1 , Noushin Rajabalinia  2 , Peter Singstad  5 , John Suberu  6
Affiliations

Dynamic Optimization and Non-linear Model Predictive Control to Achieve Targeted Particle Morphologies

Wolfgang Gerlinger et al. Chem Ing Tech. 2019 Mar.

Abstract

An event-driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot-plant reactors are presented.

Keywords: Dynamic optimization; Emulsion polymerization; Nonlinear model predictive control; Particle morphology; Pilot‐plant reactor test; Process monitoring.

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Figures

Figure 1
Figure 1
Exemplary progress in polymerization monitored by off‐line dynamic light scattering and static liquid TEM.
Figure 2
Figure 2
Scheme of the inclusion of the surrogate function and HMC model into control tools.
Figure 3
Figure 3
Final distribution of equilibrium and non‐equilibrium clusters. (a) increasing the aggregation rate; (b) increasing the cluster migration rate; (c) decreasing the cluster nucleation rate; (d) increasing the diffusion rate; always from left to right.
Figure 4
Figure 4
Final distribution of the equilibrium and non‐equilibrium clusters and representative morphologies of 10 randomly selected particles among 1 000 000 sampling particles, (a) hemispherical equilibrium case A harder system, (b) hemispherical equilibrium case A, softer system.
Figure 5
Figure 5
Conversion evolution of the weighted distribution for equilibrium and non‐equilibrium clusters and relevant 3D, 2D and TEM‐like morphology images related to each distribution for 6 randomly selected particles among all (reproduced from 29).
Figure 6
Figure 6
Results of dynamic offline optimization of the semi‐batch polymerization process.
Figure 7
Figure 7
Comparison of model prediction and measurements of unreacted monomers in a semi‐batch experiment.
Figure 8
Figure 8
Temperature, dosing and concentration evolutions for morphology control batch in lab scale.
Figure 9
Figure 9
Trajectories for monomer and initiator dosing as well as monomer concentration in the demonstration at pilot scale.
Figure 10
Figure 10
Comparison of NMPC batch with conventional practice.
See this image and copyright information in PMC

References

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