One-dimensional dynamic modeling of a high-pressure water electrolysis system for hydrogen production

Kim, Huiyong; Park, Mikyoung; Lee, Kwang Soon

doi:10.1016/j.ijhydene.2012.12.006

Cited by 82 publications

(25 citation statements)

References 24 publications

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“…The osmotic drag coefficient, n d , represents the number of water molecules carried by each hydrogen ions and it has been measured experimentally by different authors and resulted values have shown large variance. Awasthi et al considered a value n d = 5 which is in line with the relationship below that is function of temperature and pressure [17]:…”

Section: Electro Osmotic Dragmentioning

confidence: 73%

“…Water transport due to pressure asymmetry, ∆p, between anode and cathode depends on permeability of the membrane and can be calculated using the Darcy's Law. Similar approach was followed by [7][8][9]17]. The relationship is function of the membrane permeability to water, K darcy , the viscosity, µ H 2 O , and the molar mass of water, M m,H 2 O :…”

Section: Hydraulic Pressurementioning

confidence: 99%

“…We can assume that the water concentration at the electrode/membrane interface is approximated with the water concentration in the electrode channel. Assuming a linear water concentration gradient, we can simply calculate the concentration gradients across the membrane as a difference instead of using integral function [8,9,11,17]. With this assumption in mind, we calculate the water molar flow rate due to diffusion as: The diffusion process of a multi-component gas mixture across the electrode porous media is accounted using the Stefan-Maxwell approach, which considers an effective binary diffusion coefficient.…”

Section: Water Diffusionmentioning

confidence: 99%

“…Such a coefficient is used to estimate the diffusivity as a function of temperature, pressure and other geometric parameters [18]. Similarly to the approach described in [3,8], we can calculate the water concentration using the Fick's law of diffusion in the electrolyte as shown in the equations below: Assuming a linear water concentration gradient, we can simply calculate the concentration gradients across the membrane as a difference instead of using integral function [8,9,11,17]. With this assumption in mind, we calculate the water molar flow rate due to diffusion as:…”

Section: Water Diffusionmentioning

confidence: 99%

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Modelling and Experimental Analysis of a Polymer Electrolyte Membrane Water Electrolysis Cell at Different Operating Temperatures

et al. 2018

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In this paper, a simplified model of a Polymer Electrolyte Membrane (PEM) water electrolysis cell is presented and compared with experimental data at 60 °C and 80 °C. The model utilizes the same modelling approach used in previous work where the electrolyzer cell is divided in four subsections: cathode, anode, membrane and voltage. The model of the electrodes includes key electrochemical reactions and gas transport mechanism (i.e., H2, O2 and H2O) whereas the model of the membrane includes physical mechanisms such as water diffusion, electro osmotic drag and hydraulic pressure. Voltage was modelled including main overpotentials (i.e., activation, ohmic, concentration). First and second law efficiencies were defined. Key empirical parameters depending on temperature were identified in the activation and ohmic overpotentials. The electrodes reference exchange current densities and change transfer coefficients were related to activation overpotentials whereas hydrogen ion diffusion to Ohmic overvoltages. These model parameters were empirically fitted so that polarization curve obtained by the model predicted well the voltage at different current found by the experimental results. Finally, from the efficiency calculation, it was shown that at low current densities the electrolyzer cell absorbs heat from the surroundings. The model is not able to describe the transients involved during the cell electrochemical reactions, however these processes are assumed relatively fast. For this reason, the model can be implemented in system dynamic modelling for hydrogen production and storage where components dynamic is generally slower compared to the cell electrochemical reactions dynamics.

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Section: Electro Osmotic Dragmentioning

confidence: 73%

Section: Hydraulic Pressurementioning

confidence: 99%

Section: Water Diffusionmentioning

confidence: 99%

Section: Water Diffusionmentioning

confidence: 99%

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Modelling and Experimental Analysis of a Polymer Electrolyte Membrane Water Electrolysis Cell at Different Operating Temperatures

et al. 2018

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“…They show a clear emphasis on scheduling [6][7][8][9][10][11][12][13][14] aspects [15][16][17], investigate model reduction [18,12], and elaborate design towards flexibility [19][20][21]. Increasingly, electrochemical processes, e.g., chlor-alkali process [2,[22][23][24][25][26][27][28][29][30][31] and water electrolysis [32][33][34][35][36][37][38][39][40][41][42], come into focus.…”

Section: Demand Response -Fluctuation Of Steady-state Processes Reacmentioning

confidence: 99%

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Esche

Repke

2020

Chemie Ingenieur Technik

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Participating in electricity markets through demand response causes new requirements for optimizing process control of chemical plants. The last ten years have brought great advances in the formulation and solution of economic nonlinear model predictive control and state estimation to support operation of processes under dynamic constraints. However, gaps remain regarding the availabilities of suitable plant models capable of describing processes active in demand response as well as of robust schemes for state estimation and economic nonlinear model predictive control in commercial tools.

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