(170b) Dynamic Modeling and Explicit Model Predictive Control of Absorption of Hydrogen in a LaNi5 Bed

Hydrogen is an important energy carrier that is pivotal to the evolution of the energy sector. Compounds containing hydrogen absorption energy from the environment to produce hydrogen gas, which has higher gravimetric energy density. Hydrogen is stored in solids by adsorption - where hydrogen physically or chemically adheres to the surface of a material with high affinity and surface area (ex. Nanomaterial like carbon nanotubes) or by absorption – when hydrogen percolates into the lattice of a material consequently forming a different material (ex. Metal hydrides). Technologies have been developed for storing compressed hydrogen in specialized cylinder at high pressure (~ 10,000 psi) onboard a light duty fuel Cell Vehicle (FCV). However, Metal Hydride (MH) hydrogen storage remain an inherently safer storage medium for onboard storage. Two of the performance target areas listed by DOE [1] is the cooling capacity and thermal management of the MH. The temperature surge and plummet during the hydriding and dehydriding processe respectively are inherent properties of the MH. Design and control are basic considerations for performance improvement of the operation of MH systems. Here we design an explicit model predictive controller for the absorption of hydrogen into the MH bed using the PAROC framework. The schematic of the proposed framework can be found in [2]. First, we present a detailed high-fidelity dynamic model of the processes based on first principles. Several studies have investigated the various aspect of metal hydride processes [3-7]. Optimization and controls can play an important role in the thermal management of the MH process[7]. Several studies have appeared in the open literature describing the optimization and design but only a few studies address the control of adsorption and desorption processes of the metal hydride. Panos et al [8] presented a robust explicit model predictive control of hydrogen storage in MH tanks focusing on the desorption of hydrogen.

The high-fidelity dynamic model described in this work is based on first principles according to [7, 9] and accounts for the spatial variations of some thermodynamic properties. The model makes the following assumptions; the ideal gas law holds in the gas phase, the solid and fluid temperatures are essentially equal, axial and radial dispersion are included in the mass balances for the interstitial fluid, the axial and radial pressure drops in the bed depend linearly on the superficial velocity through Darcy's law. The model is developed in gPROMS software and it is used to simulate the absorption process. It is subsequently validated with experimental data. An optimization procedure is implemented on the model to determine optimal operation condition and results of the optimization will be used to determine appropriate constraints for the control design. The optimization strategy minimizes the storing time and the output pressure while satisfying, a number of operating and safety constraints. In the second part of this work, we develop a reduced order approximate model suitable for multi-parametric model predictive control (mp-MPC) based on prior analysis. The reduced order state-space (SS) model (usually linear) is developed using model identification. The SS model is developed such that it can approximate the high fidelity model. The main contribution is the design of a multi-parametric/explicit model predictive controller to regulate the absorption of hydrogen in the LaNi₅bed. Unlike the MPC the mp-MPC[10] MPC avoids the online optimization procedure. The optimization is done offline to determine the control action at every state obtained while simultaneously accounting for physical and operational constraints. For system with faster dynamics (like the MH), mp-MPC is ideal. The explicit controller is designed in MATLAB and it is linked to gPROMS through gO:MATLAB.

Reference

1. Satyapal, S., J. Petrovic, C. Read, G. Thomas, and G. Ordaz, The U.S. Department of Energy's National Hydrogen Storage Project: Progress towards meeting hydrogen-powered vehicle requirements. Catalysis Today, 2007. 120(3–4): p. 246-256.

2. Pistikopoulos, E.N., N.A. Diangelakis, R. Oberdieck, M.M. Papathanasiou, I. Nascu, and M. Sun, PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 2015. 136: p. 115-138.

3. Silin, N. and M. Melnichuk, A case study of a hydride container performance applying non dimensional parameters. International Journal of Hydrogen Energy, 2014. 39(36): p. 21060-21067.

4. Choi, H. and A.F. Mills, Heat and mass transfer in metal hydride beds for heat pump applications. International Journal of Heat and Mass Transfer, 1990. 33(6): p. 1281-1288.

5. Askri, F., A. Jemni, and S. Ben Nasrallah, Study of two-dimensional and dynamic heat and mass transfer in a metal–hydrogen reactor. International Journal of Hydrogen Energy, 2003. 28(5): p. 537-557.

6. Demircan, A., M. Demiralp, Y. Kaplan, M.D. Mat, and T.N. Veziroglu, Experimental and theoretical analysis of hydrogen absorption in – reactors. International Journal of Hydrogen Energy, 2005. 30(13–14): p. 1437-1446.

7. Kikkinides, E.S., M.C. Georgiadis, and A.K. Stubos, On the optimization of hydrogen storage in metal hydride beds. International Journal of Hydrogen Energy, 2006. 31(6): p. 737-751.

8. Panos, C., K.I. Kouramas, M.C. Georgiadis, and E.N. Pistikopoulos, Dynamic optimization and robust explicit model predictive control of hydrogen storage tank. Computers & Chemical Engineering, 2010. 34(9): p. 1341-1347.

9. Jemni, A. and S.B. Nasrallah, Study of two-dimensional heat and mass transfer during absorption in a metal-hydrogen reactor. International Journal of Hydrogen Energy, 1995. 20(1): p. 43-52.

10. Pistikopoulos, E.N., V. Dua, N.A. Bozinis, A. Bemporad, and M. Morari, On-line optimization via off-line parametric optimization tools. Computers & Chemical Engineering, 2002. 26(2): p. 175-185.