FlexPro: Flexible Power-to-Gas Processes

In December 2019, the European Council endorsed the objective of making the EU climate-neutral by 2050. Because of this transformation, large amounts of low-cost, surplus electrical energy (e.g. from wind farms and photovoltaics) are expected to become available periodically, depending on weather conditions. To assure efficient integration of this "green" energy, the existing infrastructures for energy supply must be extended by so-called Power-to-X (P2X) processes. However, the multitude of possible process pathways and target products (X) is often challenging to oversee and requires novel methods for process synthesis and process analysis. Moreover, such processes must be operated not only at a fixed nominal load, but at a wide range of different partial loads (load flexibility). The PSE group addresses these aspects to the production of gaseous target molecules methane (CH4) and syngas (H2/CO).

A vast variety of different system level configurations for the Power-to-Gas concept were evaluated in terms of energy use by our group [1]. For the purpose of process synthesis, a superstructure MINLP optimization approach, that includes heat integration options, was developed in collaboration with Prof. Sager (Algorithmic Mathematical Optimization Group at OVGU) [2]. As shown in Fig. 1, a two-stage reactor concept turned out to be optimal in terms of efficiency and CAPEX. To reduce the computational complexity of such optimization problems, the PSE group also collaborated with Prof. Biegler (CMU) and found significant computational speed-ups when applying trust-region filter algorithms [3].

In collaboration with Prof. Sager (OVGU) and Prof. Findeisen (OVGU), our group also established control strategies for identifying time-minimal set point transitions that enable fast load changes of process configurations similar to the one shown in Fig. 1 [4, 5, 6].

On the process unit level of the Power-to-Gas concept, the PSE group performed extensive numerical and also experimental studies on fixed-bed reactors and catalyst particles for CO2 methanation. It was shown that an excessive temperature excursion in the hotspot region of the fixed-bed can be avoided by making use of unstable stationary operating conditions, which were fundamentally investigated by a novel bifurcation approach [7]. The exothermic nature can also be controlled by using tailor-made Ni/Al2O3 catalyst particles. If the active catalyst core is surrounded by a thin porous inert shell, the temperature sensitivity of the effective reaction rate is lowered to such an extent that the reactor can no longer get into thermal runaway (Fig. 2) [8, 9]. In order to achieve the required catalyst particle design (porosity, pore size, core-shell structure) a close collaboration has been established with Prof. Gläser (University of Leipzig) and Dr. Sheppard (KIT) within the DFG SPP2080 program [10].
The PSE group was also assigned to a BMBF-funded Power-to-Chemicals (P2Chem) project, dealing with new mixed-integer optimization methods for the efficient synthesis and flexible management, in particular for the Power-to-Syngas concept. Therefore, the research focus was on superstructure optimization considering DAC, electrolysis, and several reactor-separator networks [11, 12, 13, 14].


[1] Uebbing, J., Rihko-Struckmann, L., & Sundmacher, K. (2019). Exergetic assessment of CO2 methanation processes for the chemical storage of renewable energies. Applied Energy, 233–234, 271–282. doi: 10.1016/j.apenergy.2018.10.014.

[2] Uebbing, J., Rihko-Struckmann, L., Sager, S., & Sundmacher, K. (2020). CO2 methanation process synthesis by superstructure optimization. Journal of CO2 Utilization, 40: 101228. doi: 10.1016/j.jcou.2020.101228.

[3] Uebbing, J., Biegler, L. T., Rihko-Struckmann, L., Sager, S., & Sundmacher, K. (2021). Optimization of pressure swing adsorption via a trust-region filter algorithm and equilibrium theory. Computers & Chemical Engineering: 107340. doi: 10.1016/j.compchemeng.2021.107340.

[4] Himmel, A., Sager, S., & Sundmacher, K. (2020). Time-minimal set point transition for nonlinear SISO systems under different constraints. Automatica, 114: 108806. doi: 10.1016/j.automatica.2020.108806.

[5] Matschek, J., Himmel, A., Sundmacher, K., & Findeisen, R. (2020). Constrained gaussian process learning for model predictive control. In: 21st IFAC World Congress: IFAC-PapersOnLine, 971–976. doi: 10.1016/j.ifacol.2020.12.1269.

[6] Himmel, A. (2021). Optimization-based operation strategy and storage design for coupled processes. PhD Thesis, Magdeburg: Otto-von-Guericke-Universität.

[7] Bremer, J. & Sundmacher, K. (2021). Novel multiplicity and stability criteria for non-isothermal fixed-bed reactors. Frontiers in Energy Research, 8. doi: 10.3389/fenrg.2020.549298.

[8] Zimmermann, R. T., Bremer, J., & Sundmacher, K. (2020). Optimal catalyst particle design for flexible fixed-bed CO2 methanation reactors. Chemical Engineering Journal, 387: 123704. doi: 10.1016/j.cej.2019.123704.

[9] Zimmermann, R. T., Bremer, J., & Sundmacher, K. (2022). Load-flexible fixed-bed reactors by multi-period design optimization. Chemical Engineering Journal, 428: 130771. doi: 10.1016/j.cej.2021.130771.

[10] Weber, S., Abel, K. L., Zimmermann, R. T., Huang, X., Bremer, J., Rihko-Struckmann, L., Batey, D., Cipiccia, S., Titus, J., Poppitz, D., Kübel, C., Sundmacher, K., Gläser, R., & Sheppard, T. L. (2020). Porosity and structure of hierarchically porous Ni/Al2O3 catalysts for CO2 methanation. Catalysts, 10 (12): 1471. doi: 10.3390/catal10121471.

[11] Maggi, A., Wenzel, M., & Sundmacher, K. (2020). Mixed-Integer Linear Programming (MILP) approach for the synthesis of efficient power-to-syngas processes. Frontiers in Energy Research, 8: 161. doi: 10.3389/fenrg.2020.00161.

[12] Maggi, A., Garmatter, D., Sager, S., Stoll, M., Sundmacher, K. (2021). Power-to-Syngas: a parareal optimal control approach. Frontiers in Energy Research, doi: 10.3389/fenrg.2021.720489.

[13] Maggi, A., Wenzel, M., & Sundmacher, K. (2020). Power-to-Syngas processes by reactor-separator superstructure optimization. In: 30th European Symposium on Computer Aided Process Engineering, 1387–1392. ISBN: 9780128233771. doi: 10.1016/B978-0-12-823377-1.50232-9.

[14] Garmatter, D., Maggi, A., Wenzel, M., Monem Adbelhafez, S., Hahn, M., Stoll, M., Sager, S., Benner, P., & Sundmacher, K. (2021). Power-to-Chemicals: a superstructure problem for sustainable syngas production. In: Göttlich, S., Herty, M., Milde, A. (Eds.) Mathematical Modeling, Simulation and Optimization for Power Engineering and Management, Springer Nature, Mathematics in Industry, pp. 145–168. doi: 10.1007/978-3-030-62732-4_7.




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