Dynamic modelling of alkaline self-pressurized electrolyzers: a phenomenological-based semiphysical approach

David, Martín; Álvarez, Hernán; Ocampo‐Martínez, Carlos; Peña, Ricardo

doi:10.1016/j.ijhydene.2020.06.038

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…Based on Figure 8, one can note that for alkaline electrolyzers with KOH liquid solution, the specific electrolyte conductivity is higher compared to alkaline electrolyzers with NaOH solution. Indeed, as it has been demonstrated in previous papers [18,42], the specific electrolyte conductivity of KOH is optimal for mass fractions between 25 and 35 wt.% Based on Figure 8, one can note that for alkaline electrolyzers with KOH liquid solution, the specific electrolyte conductivity is higher compared to alkaline electrolyzers with NaOH solution. Indeed, as it has been demonstrated in previous papers [18,42], the specific electrolyte conductivity of KOH is optimal for mass fractions between 25 and 35 wt.% and a temperature range from 50 to 80 • C. The use of a liquid solution based on NaOH offers a cheaper option than KOH but features a lower specific electrolyte conductivity.…”

Section: Specific Electrolyte Conductivitysupporting

confidence: 67%

“…Indeed, as it has been demonstrated in previous papers [18,42], the specific electrolyte conductivity of KOH is optimal for mass fractions between 25 and 35 wt.% Based on Figure 8, one can note that for alkaline electrolyzers with KOH liquid solution, the specific electrolyte conductivity is higher compared to alkaline electrolyzers with NaOH solution. Indeed, as it has been demonstrated in previous papers [18,42], the specific electrolyte conductivity of KOH is optimal for mass fractions between 25 and 35 wt.% and a temperature range from 50 to 80 • C. The use of a liquid solution based on NaOH offers a cheaper option than KOH but features a lower specific electrolyte conductivity. For example, at 50 • C, the maximum specific electrolyte conductivity of KOH is equal to 95 S•m −1 , whereas for NaOH the conductivity is equal to 65 S•m −1 .…”

Section: Specific Electrolyte Conductivitysupporting

confidence: 67%

“…To determine the different parameters in Equations ( 6)- (8), experimental data are compared to the model through the use of a numerical regression method mainly based on least square algorithms [41][42][43]. Relying on previous works carried out to determine the parameters of the models ( 6)-( 8), Table 5 has been made to summarize the values of the different parameters.…”

Section: Semi-empirical Modelingmentioning

confidence: 99%

“…The specific electrolyte conductivity for KOH and NaOH is given in Equations ( 41) and (42). Equation ( 41) is valid for temperature T ( • K) ranging from 258.15 to 373.15 • K and KOH mass fraction w KOH between 0.15 and 0.45, while Equation ( 42) is suitable for temperatures θ ( • C) between 25 and 50 • C and NaOH mass fraction w NaOH from 0.08 to 0.25 as reported in the article [22].…”

Section: Specific Electrolyte Conductivitymentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Survey of Alkaline Electrolyzer Modeling: Electrical Domain and Specific Electrolyte Conductivity

et al. 2022

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Alkaline electrolyzers are the most widespread technology due to their maturity, low cost, and large capacity in generating hydrogen. However, compared to proton exchange membrane (PEM) electrolyzers, they request the use of potassium hydroxide (KOH) or sodium hydroxide (NaOH) since the electrolyte relies on a liquid solution. For this reason, the performances of alkaline electrolyzers are governed by the electrolyte concentration and operating temperature. Due to the growing development of the water electrolysis process based on alkaline electrolyzers to generate green hydrogen from renewable energy sources, the main purpose of this paper is to carry out a comprehensive survey on alkaline electrolyzers, and more specifically about their electrical domain and specific electrolytic conductivity. Besides, this survey will allow emphasizing the remaining key issues from the modeling point of view.

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Section: Specific Electrolyte Conductivitysupporting

confidence: 67%

Section: Specific Electrolyte Conductivitysupporting

confidence: 67%

Section: Semi-empirical Modelingmentioning

confidence: 99%

Section: Specific Electrolyte Conductivitymentioning

confidence: 99%

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A Comprehensive Survey of Alkaline Electrolyzer Modeling: Electrical Domain and Specific Electrolyte Conductivity

et al. 2022

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“…Water electrolysis is the most common method for renewable energy-based P2H production [17]. Mainstream technical routes include alkaline water electrolysis (AEL), proton exchange membrane electrolysis (PEMEL), and solid oxide cell electrolysis (SOCEL) [18].…”

Section: Motivationmentioning

confidence: 99%

Extended Load Flexibility of Utility-Scale P2H Plants: Optimal Production Scheduling Considering Dynamic Thermal and HTO Impurity Effects

Qiu¹,

Zhou²,

Zang³

et al. 2023

Preprint

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In the conversion toward a clear and sustainable energy system, the flexibility of power-to-hydrogen (P2H) production enables the admittance of volatile renewable energies on a utility scale and provides the connected electrical power system with ancillary services. To extend the load flexibility and thus improve the profitability of green hydrogen production, this paper presents an optimal production scheduling approach for utility-scale P2H plants composed of multiple alkaline electrolyzers. Unlike existing works, this work discards the conservative constant steady-state constraints and first leverages the dynamic thermal and hydrogen-to-oxygen (HTO) impurity crossover processes of electrolyzers. Doing this optimizes their effects on the loading range and energy conversion efficiency, therefore improving the load flexibility of P2H production. The proposed multiphysics-aware scheduling model is formulated as mixed-integer linear programming (MILP). It coordinates the electrolyzers' operation state transitions and load allocation subject to comprehensive thermodynamic and mass transfer constraints. A decomposition-based solution method, SDM-GS-ALM, is followingly adopted to address the scalability issue for scheduling large-scale P2H plants composed of tens of electrolyzers. With an experiment-verified dynamic electrolyzer model, case studies up to 22 electrolyzers show that the proposed method remarkably improves the hydrogen output and profit of P2H production powered by either solar or wind energy compared to the existing scheduling approach.

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Phenomenological‐based model of direct blue 2 adsorption on corncob in a fixed‐bed

Presiga‐Posada,

Alvarez,

Hormaza

2023

Can J Chem Eng

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This work presents a phenomenologically based semi‐physical model (PBSM) for the liquid–solid adsorption process, which has been experimentally validated. Two modes of operation of adsorption of direct blue 2 dye (DB2) on corn cobs in a fixed bed are discussed by applying the PBSM. The first mode of operation is the typical continuous liquid flow, whereas the second is a semicontinuous mode with the recirculation of liquid leaving the column. These modes of operation were used to illustrate different ways in which the system reaches equilibrium conditions. The equilibrium distribution curve (EDC) for low DB2 concentration was reached with the best fit using the Langmuir–Freundlich model (). The local mass transfer coefficient for the solid phase was evaluated by the PBSM deduction, and subsequently, the diffusivity of the surface solid was experimentally evaluated through comparison in terms of the PBSM. Regardless of the mode of operation, surface diffusion is associated with only the nature of the adsorbent material rather than the concentration of the liquid phase. In addition, the PBSM exhibited liquid and solid concentration changes at different points in the column. Moreover, different operating curves of the process were constructed and validated, exhibiting similar results when comparing the predictions of the model and the experimental data. These findings support the strength of the constructed model, enabling its extension to other adsorbent–dye systems.

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Dynamic modelling of alkaline self-pressurized electrolyzers: a phenomenological-based semiphysical approach

Cited by 35 publications

References 27 publications

A Comprehensive Survey of Alkaline Electrolyzer Modeling: Electrical Domain and Specific Electrolyte Conductivity

A Comprehensive Survey of Alkaline Electrolyzer Modeling: Electrical Domain and Specific Electrolyte Conductivity

Extended Load Flexibility of Utility-Scale P2H Plants: Optimal Production Scheduling Considering Dynamic Thermal and HTO Impurity Effects

Phenomenological‐based model of direct blue 2 adsorption on corncob in a fixed‐bed

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