2025 AIChE Annual Meeting

(529f) Dynamic Optimization of Multi-Timescale Processes Using Adaptive Timescale Coupling

Authors

Nishant Vinayak Giridhar - Presenter, West Virginia University
Debangsu Bhattacharyya, West Virginia University
Douglas A. Allan, University of Wisconsin Madison
Sangbum Lee, National Energy Technology Laboratory
With the increasing need for the adoption of novel process systems in the energy and chemical production industries, there is a growing need for tailored solution algorithms for simulation and optimization problems for load following and non-steady operation. Studies often restrict the modeling and decision domain to a single timescale while assuming the dynamics of other timescales are at steady state. In the case of control-oriented studies, it is common to neglect the slow dynamics associated with ageing, deactivation or degradation of the system performance. On the other hand, studies focusing on long-term considerations such as equipment replacement, planning and life-cycle analyses tend to assume steady-state or quasi-state operation of the day-to-day dynamics of the process in order to simplify the problem. However, modern and emerging energy technologies are expected to operate primarily in non-steady state regimes over the course of daily operation while also having to incur substantial replacement and/ or maintenance costs associated with catalyst deactivation [1], materials degradation [2], structural fatigue [3-4], etc. It is therefore important to consider both timescales simultaneously in a single modeling framework and to develop solution strategies to ensure computational tractability.

In this work we present a generic formulation of a multi-timescale dynamic optimization that can be applied to a variety of problems involving dynamics and decisions over multiple timescales. The framework allows for the consideration of market parameters such as product demands and energy pricing at both seasonal and diurnal timescales. The problem is expressed as a multi-period optimization model [5] with variable period durations. To ensure continuity between each period, a surrogate-based projection approach is used to bridge the slow and fast timescales. The optimization model permits the user to specify which equipment has a lifespan shorter than the plant lifespan, allowing the replacement schedule to be optimized. Period durations are adjusted to ensure the fast-timescale model is run once slow-timescale variables change past a user-specified threshold. However, the user can also specify a minimum number of coupling events for the model.

We demonstrate these methods on two test problems: a reaction system with catalyst deactivation and optimization of a reversible fuel cell system under long-term performance degradation with both short- and long-term structural deformation. In both cases, we discuss the utility of considering decisions over multiple timescales and compare the computational details of this approach with existing approaches such as time-scale elimination and grid refinement. These methods are implemented in the Python based algebraic programming language Pyomo [6] using modeling components from the IDAES process modeling framework [7].

Acknowledgments:

This work was conducted as part of the Institute for the Design of Advanced Energy Systems (IDAES) with support from the U.S. Department of Energy’s Office of Fossil Energy and Carbon Management (FECM) through the Simulation-Based Engineering Program.

Disclaimer:

This project was funded by the United States Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, expressor implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or any of their contractors.

References

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[2] N. V. Giridhar, D. A. Allan, M. Li, S. E. Zitney, L. T. Biegler, and D. Bhattacharyya, “Optimal operation of solid-oxide electrolysis cells considering long-term chemical degradation,” Energy Conversion and Management, vol. 319, p. 118950, Nov. 2024, doi: 10.1016/j.enconman.2024.118950.

[3] N. V. Giridhar, Q. M. Le, D. Bhattacharyya, D. A. Allan, E. Liese, and S. E. Zitney, “Maximizing Stack Life and Efficiency of Solid-Oxide Electrolyzer-Based H2 Production Plants Considering Time-Dependent Stress Evolution and Creep Deformation,” [Under Review] Energy & Environmental Science, 2025.

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[6] B. Nicholson, J. D. Siirola, J.-P. Watson, V. M. Zavala, and L. T. Biegler, “pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations,” Math. Prog. Comp., vol. 10, no. 2, pp. 187–223, Jun. 2018, doi: 10.1007/s12532-017-0127-0.

[7] A. Lee et al., “The IDAES process modeling framework and model library—Flexibility for process simulation and optimization,” Journal of Advanced Manufacturing and Processing, vol. 3, no. 3, pp. 1–30, 2021, doi: 10.1002/amp2.10095.