(186a) Optimization-Based Retrofit of a Cryogenic Air Separation Unit for Flexible Operation

Authors: 
Schweidtmann, A. M., RWTH Aachen University
Schäfer, P., RWTH Aachen University
Caspari, A., RWTH Aachen University
Seele, H., RWTH Aachen University
Padberg, P., RWTH Aachen University
Offermanns, C., RWTH Aachen University
Mitsos, A., RWTH Aachen University
Mhamdi, A., RWTH Aachen University
The increasing advancement and use of intermittent renewable energies results in fast-changing electricity prices that motivate flexible operation of industrial processes for demand side management (e.g., [1,2]). Previous literature has identified a considerable potential for air separation units (ASU) to increase their economic profitability by demand side management [3-5], since ASUs are large electrical energy consumers due to necessary compression of air for cryogenic separation and on-demand supply of gaseous products via pipelines.

Recent publications for dynamic operation of ASUs have mainly focused on either advanced process control strategies for existing plants (e.g., [6]) or on long-term scheduling of production cycles considering storage and vaporization of products (e.g., [3,7]). They found that adding further production equipment is economically desirable for cases with high utilization of the plant. However, they did not consider the transition time between operating modes in particular. Cao et al. [8] assessed design limitations to the dynamic behavior of the high-pressure nitrogen column in response to demand fluctuations and proposed the introduction of liquid nitrogen as an additional reflux to improve the plant’s agility.

In this work, we propose a methodology for the redesign of the ASU process that considers the process dynamics, i.e., its transient behavior. This process design problem results in a large-scale dynamic optimization problem due to dynamic balances on the ASU columns’ trays. In order to improve the computational efficiency, the rigorous dynamic stage-wise model is reduced using the collocation-based approach. Herein, dynamic balance equations are only considered for few trays, which reduces the number of differential equations and variables. The remaining trays are coupled using Lagrange polynomials. Then, a characteristic load change of the plant is optimized using a single-shooting dynamic optimization algorithm. In a post-optimal sensitivity analysis, we further identify bottlenecks that limit the plant's agility and propose new design modifications to overcome those limitations. The results show that resizing of column equipment, e.g., the volume of column sumps, and additional liquid buffer tanks can enhance process agility.

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[3] Cao, Yanan, Swartz, Christopher L.E., & Flores-Cerrillo, Jesus. 2017. Preemptive dynamic operation of cryogenic air separation units. AIChE Journal, 24(5), 467.

[4] Pattison, Richard C., & Baldea, Michael. 2014. Optimal Design of Air Separation Plants with Variable Electricity Pricing. Pages 393–398 of: Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design. Computer Aided Chemical Engineering, vol. 34. Elsevier.

[5] Zhang, Qi. 2016. Enterprise-Wide Optimization for Industrial Demand Side Management. Dissertation, Carnegie Mellon University, Pittsburgh, Pennsylvania.

[6] Xu, Zuhua, Zhao, Jun, Chen, Xi, Shao, Zhijiang, Qian, Jixin, Zhu, Lingyu, Zhou, Zhiyong, & Qin, Haizhong. 2011. Automatic load change system of cryogenic air separation process. Separation and Purification Technology, 81(3), 451–465.

[7] Pattison, Richard C., Touretzky, Cara R., Johansson, Ted, Harjunkoski, Iiro, & Baldea, Michael. 2016. Optimal Process Operations in Fast-Changing Electricity Markets: Framework for Scheduling with Low-Order Dynamic Models and an Air Separation Application. Industrial & Engineering Chemistry Research, 55(16), 4562–4584.

[8] Cao, Yanan, Swartz, Christopher L.E., Baldea, Michael, & Blouin, Stéphane. 2015. Optimization-based assessment of design limitations to air separation plant agility in demand response scenarios. Journal of Process Control, 33, 37–48.