Evolutionary Design Optimization of an Alkaline Water Electrolysis Cell for Hydrogen Production

Abstract

Hydrogen is an excellent energy source for long-term storage and free of greenhouse gases. However, its high production cost remains an obstacle to its advancement. The two main parameters contributing to the high cost include the cost of electricity and the cost of initial financial investment. It is possible to reduce the latter by the optimization of system design and operation conditions, allowing the reduction of the cell voltage. Because the CAPEX (initial cost divided by total hydrogen production of the electrolyzer) decreases according to current density but the OPEX (operating cost depending on the cell voltage) increases depending on the current density, there exists an optimal current density. In this paper, a genetic algorithm has been developed to find the optimal evolution parameters and to determine an optimum electrolyzer design. The optimal current density has been increased by 10% and the hydrogen cost has been decreased by 1%.

Pathways towards Achieving High Current Density Water Electrolysis: from Material Perspective to System Configuration

“…In Equation ( 14), Bideau et al showed the ohmic electrolyte resistance's (R electrolyte ) dependency on the void fraction (ɛ), diphasic boundary thickness (h) or the interelectrode gap, and the electrolyte conductivity (σ) with the assumption that the gas is diffused throughout the thickness of the electrolyte. [404] Figure 22. Minimum values of roughness factor to promote the complete spreading of liquid on a surface (Reproduced with permission; [400] Copyright 2010, American Chemical Society).…”

“…In Equation (14), Bideau et al. showed the ohmic electrolyte resistance's ( R electrolyte ) dependency on the void fraction ( ϵ ), diphasic boundary thickness ( h ) or the interelectrode gap, and the electrolyte conductivity ( σ ) with the assumption that the gas is diffused throughout the thickness of the electrolyte [404] $$$\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr {R}_{electrolyte}\left(\varepsilon{}, T,Y\right)={{h}\over{\sigma \left(\varepsilon{}, T,Y\right)}}={{V}\over{I}}\hfill\cr}}$$$ …”

Numerical Study on Hydrodynamic Characteristics and Electrochemical Performance of Alkaline Water Electrolyzer by Micro-Nano Surface Electrode

“…In addition to the drag force hindering the flow in the AWE, in fact, with the continuous generation and accumulation of bubbles, the collision between bubbles increases, which triggers the diffusion of bubbles from a high concentration to a low concentration. Therefore, based on previous work, drag force and turbulent dispersion force are considered in the model, as shown in Equation (15) and Equation (16), respectively [ 45 , 46 ]. where is the drag coefficient, K g is the diffusion factor of mixed bubbles in m s −2 , and is the bubble diameter in m. The drag coefficient is defined by the Schiller–Naumann model as shown in Equation (17).…”

“…In addition to the drag force hindering the flow in the AWE, in fact, with the continuous generation and accumulation of bubbles, the collision between bubbles increases, which triggers the diffusion of bubbles from a high concentration to a low concentration. Therefore, based on previous work, drag force and turbulent dispersion force are considered in the model, as shown in Equation (15) and Equation (16), respectively [45,46].…”

10th Anniversary of Applied Sciences: Invited Papers in Electrical, Electronics and Communications Engineering Section

“…Alkaline water electrolysis is also featured in this Special Issue with the contribution of one paper. Indeed, in [8], Bideau et al proposed a strategy to find the best cell design and operating conditions of a diphasic electrochemical cell using an evolutionary algorithm. More specifically, a meta-model was developed to simplify the physics of the void fraction and a genetic algorithm was used to find the optimal design.…”