Prof. Dr.-Ing. Kai Sundmacher
Prof. Dr.-Ing. Kai Sundmacher
Phone: +49 391 6110-351
Fax: +49 391 6110-353
Room: N. 309
Links: Publications

Team Leaders

Dr. Teng Zhou
Dr. Teng Zhou
Phone: +49 391 6110 406
Room: N 2.12


Dr.-Ing. Kevin McBride
Dr.-Ing. Kevin McBride
Phone: +49 391 6110 275
Room: N 3.15
M. Sc. Karsten Hans Georg Raetze
M. Sc. Karsten Hans Georg Raetze
Phone: +49 391 6110 254
Room: N 3.15
M. Sc. Michael Jokiel
Phone: +49 391 6110 444
Room: N 3.08
Steffen Linke, M.Sc.
Phone: +49 391 6110 330
Room: N 3.15
Dr. Zhen Song
Dr. Zhen Song
Phone: +49 391 6110 373

Chemical Production Systems

InPROMPT: Integrated Chemical Processes with Liquid Multiphase Systems

This project is part of the DFG funded research center CRC/TR 63 "Integrated Chemical Processes in Liquid Multiphase Systems", in which the PSE group collaborates closely with colleagues from TU Berlin, TU Dortmund and Otto von Guericke University.

By applying the Elementary Process Functions Methodology (EPF) to multi-phase systems, innovative processes are optimally designed while taking multiple process levels into account. From optimal reactor networks with highly efficient downstream processing and process integration to optimal process control, the design methodology allows for an all-encompassing view of chemical process design and operation. As a model reaction, the hydroformylation of long-chain olefins has been intensively investigated (Fig. 1). This is a homogeneously catalyzed reaction and highly relevant for the production of detergents, softeners, surfactants, odorants and solvents. One major focus lies on the chemo- and regio-selectivity towards the desired products which can be tuned using suitable operating conditions and ligands for the catalyst.

To start, the design of optimal multi-phase reactor units is formulated as a dynamic optimization problem with the objective to maximize the selectivity towards the desired product. Since model-based process design strongly relies on the underlying process model, uncertainties in thermodynamic/kinetic model parameters may have an influence on the resulting process design. To quantify the influence of these parameter uncertainties and to increase the robustness of the optimal design, we introduced a probabilistic approach to process design using the sigma-point method, see Fig. 2 [1].

After performing the dynamic optimization of a (semi-)batch reactor, optimal mass and energy flux profiles are available. Based on these profiles, the newly introduced Flux Profile Analysis (FPA) allows for deriving reactor network candidates while taking into account micro- and macro-mixing, process-wide recycle streams, dosing of substrates, heating, and cooling [2]. These reactor-network candidates may either be continuously operated or realized using (semi-)batch reactors, in an overall production process (Fig. 3). As shown for the hydroformylation of 1-dodecene, these reactor concepts may have different advantages with respect to their operating mode but are comparable in terms of performance [3].

The process design task does not only focus on optimal reactor strategies but also incorporates the subsequent downstream process. Thermomorphic multi-phase systems (TMS) can be used in the hydroformylation process to minimize the leaching of the expensive homogeneous catalysts (Rhodium-BiPhePhos). At reaction conditions, these solvent systems enable homogeneous reactions while exhibiting biphasic behavior upon cooling. Under ideal conditions, the catalyst and the reaction products are recovered in different phases, enabling efficient catalyst recycling [4]. A new project of ours within the CRC/TR 63 focuses on designing new TMS with green solvents to enhance the ecological attributes and sustainability of this process (Fig. 4).

In addition to our model-based simulations, we also perform the necessary experimental validation of the process designs. Therefore, we construct and operate new reactor-network designs at the mini-plant scale using state-of-the-art process analytics and industrially employed process control systems, see Fig. 5. Long-term campaigns are used to assess the performance and process stability of the reactor designs and to verify theoretical considerations [5, 6, 7].

After the design, construction and operation of the reactor-networks, optimal control is a key factor for an efficient chemical process. In recent years, nonlinear model predictive control (NMPC) became more and more attractive due to increasing computational capacities, especially for continuously operated processes. However, optimal control using detailed process models still provides challenges in terms of online optimization. Indirect optimization approaches like Pontryagin’s Minimum Principle (PMP) show significantly better performance in certain scenarios, presenting itself as a valid alternative. For the use in (path-)constrained (semi-)batch processes, we were able to present a remedy for the shortcomings of PMP, leading to a significant reduction in the computational load, see Fig. 6 [8]. Further tailoring of the control approach using parsimonious parameterization of the control variables resulted in even better performance of shrinking-horizon NMPC of the hydroformylation of 1-dodecene [9, 10, 11].

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[1] Kaiser, N.M., Flassig, R.J., and Sundmacher, K. (2016) Probabilistic reactor design in the framework of elementary process functions. Computers & Chemical Engineering. 94 45–59.

[2] Kaiser, N.M., Flassig, R.J., and Sundmacher, K. (2018) Reactor-network synthesis via flux profile analysis. Chemical Engineering Journal. 335 1018–1030.

[3] Kaiser, N.M., Jokiel, M., McBride, K., Flassig, R.J., and Sundmacher, K. (2017) Optimal Reactor Design via Flux Profile Analysis for an Integrated Hydroformylation Process. Industrial & Engineering Chemistry Research. 56 (40), 11507–11518.

[4] McBride, K., Gaide, T., Vorholt, A., Behr, A. and Sundmacher, K. (2015). Thermomorphic solvent selection for homogeneous catalyst recovery based on COSMO-RS. Chemical Engineering and Processing – Process Intensification, online available.

[5]Jokiel, M., Kaiser, N.M., Kováts, P., Mansour, M., Zähringer, K., Nigam, K.D.P., et al. (2018) Helically coiled segmented flow tubular reactor for the hydroformylation of long-chain olefins in a thermomorphic multiphase system. Chemical Engineering Journal, in press.

[6] Rätze, K. H. G., Jokiel, M., Kaiser, N. M., & Sundmacher, K. (2018). Cyclic operation of a semi-batch reactor for the hydroformylation of long-chain olefins and integration in a continuous production process. Chem. Eng. Journal, under review.

[7] Jokiel, M., Rätze, K. H. G., Kaiser, N. M., Künnemann, K., Hollenbeck, J. P., Dreimann, J., Vogt, D., & Sundmacher, K.(2018). Process intensification in a tandem reactor system for the hydroformylation of long-chain olefins. Industrial &Engineering Chemistry Research, under review.

[8] Aydin, E., Bonvin, D., and Sundmacher, K. (2017) Dynamic optimization of constrained semi-batch processes using Pontryagin’s minimum principle—An effective quasi-Newton approach. Computers & Chemical Engineering. 99 135–144.

[9] Aydin, E., Bonvin, D., and Sundmacher, K. (2018) Toward Fast Dynamic Optimization: An Indirect Algorithm That Uses Parsimonious Input Parameterization. Industrial & Engineering Chemistry Research. 57 (30), 10038–10048.

[10] Aydin, E., Bonvin, D., and Sundmacher, K. (2018) NMPC using Pontryagin’s Minimum Principle-Application to a two-phase semi-batch hydroformylation reactor under uncertainty. Computers & Chemical Engineering. 108 47–56.

[11] Aydin, E., Bonvin, D., and Sundmacher, K. (2018) Computationally efficient NMPC for batch and semi-batch processes using parsimonious input parameterization. Journal of Process Control. 66 12–22.

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