Optimisation of Mass Transport Parameters in a Polymer Electrolyte Membrane Electrolyser Using Factorial Design-of-Experiment

Abstract

Optimised mass transport is crucial for high current density operations in Polymer Electrolyte Membrane Water Electrolysers (PEMWEs). This study investigates the effect and interactions of mass transport parameters on the performance of a PEMWE using a 23 full-factorial Design-of-Experiments (DoE) approach with replication. The effects of anode flow-field design, anode porous transport layer (PTL) and water flow rate on the cell performance were studied. At 95% confidence level, the result shows that all three factors and their two-way interactions significantly affect the cell performance. Among them, the water flow rate showed the most significant contribution, followed by the interaction between the flow-field and the PTL. A regression model was developed to relate the cell performance and the mass transfer parameters. Results of analysis of variance (ANOVA), regression analysis and R2 test indicated good accuracy of the model. The best PEMWE cell performance was obtained with a parallel flow-field configuration, a small average pore diameter of PTL and high anode water flow rate. The DoE is shown to be a suitable method for investigating interactions and optimising the operating conditions to maximise PEMWE performance.

Advances in Oxygen Evolution Electrocatalysts for Proton Exchange Membrane Water Electrolyzers

“…Researches show that high anode flow rate leads to reduced mass transfer loss, while low flow rate will improve ohmic impedance. [ 266 ] We recommend that the flow rate should be related to the structure of flow field and PTL in practice to properly evaluate the optimal conditions. Furthermore, PEMWEs allow pure water to pass through the anode side to ensure the production of high‐purity dry hydrogen gas at the cathode, which has an advantage over AEMWE with bilateral alkali electrolyte.…”

“…Researches show that high anode flow rate leads to reduced mass transfer loss, while low flow rate will improve ohmic impedance. [266] We @1.52 V 39 mV dec - 1 10 h @40 mA cm -2 [36] Ru-N-C 0.5 m H 2 SO 4 267 mV @10 mA cm -2 0.280 mg cm -2 3348 O 2 h -1 @1.497 V 3571 A g metal -1 @1.497 V 52.6 mV dec -1 30 h @1.5 V RHE [26] Co-RuIr alloy 0.1 m HClO 4 235 mV @10 mA cm -2 66.9 mV dec - 1 25 h @10 mA cm -2 [46] Ir/Fe 30 h @10 mA cm -2 [237] Ir@N-G-750 0.5 m H 2 SO 4 303 mV @10 mA cm -2 23 µg cm -2 2.42 A mg -1 @1.6 V 50 mV dec -1 20 h @20 mA cm -2 [121] Amorphous Ir nanosheets 0.1 m HClO 4 255 mV @10 mA cm -2 0.2 mg cm -2 0.16 s -1 @1.53 V 221.8 A g -1 @1.53 V 40 mV dec -1 8 h @1.485 V [112] Fcc Ru octahedral 0.05 m H 2 SO 4 168 mV @10 mA cm -2 47.8 mV dec -1 [130] Mesoporous metallic Ir nanosheets 0.5 m H 2 SO 4 240 mV @10 mA cm -2 9.6 µg cm -2 260 mA mg -1 @1.5 V 49 mV dec -1 8 h @10 mA cm -2 [137] Ir-SA@Fe@NCNT 0.5 m H 2 SO 4 250 mV @10 mA cm -2 0.285 mg cm -2 4.4 s -1 13.7 A mg Ir -1 @1.5 V 58.2 mV dec -1 12 h @1.48 V [142] Ir doped SrTiO 3 0.1 m HClO 4 247 mV @10 mA cm -2 0.21 mg cm -2 820 A g Ir -1 @1.525 V 43 mV dec -1 20 h @10 mA cm -2 1 h @30 mA cm -2 [140] Ru@IrO x core-shell nanocrystal 0.05 m H 2 SO 4 282 mV @10 mA cm -2 2.3 mA cm ox -2 @1.56 V 1.45 mg cm -2 650 A g ox -1 @ 1.56 V 69.1 mV dec -1 24 h @1.55 V RHE [158] IrCo@IrO x−n L NDs 0.05 m H 2 SO 4 247 mV @10 mA cm -2 25.5 µg cm -2 49 mV dec -1 10 h @10 mA cm -2 [139] (Mn 0.8 Ir 0.2 )O 2 :10F…”

Multiphysics modeling of proton exchange membrane water electrolysis: From steady to dynamic behavior

“…This is because the mass transport and charge transfer processes strongly affect the electrochemical performance and electrolytic efficiency during system upscaling and industrial-scale operations with significant interactions. 8 However, conventional trial-and-errors lead to experimental overrunning cost to identify the critical factors and interactions for system design and optimization, especially for temporal behavior. Besides, trial-and-errors are limited by severe and extreme operating conditions.…”

“…It is noted that all these issues of material development are closely related to thorough investigation of the multiphase flow dynamics and charge transfer in micro‐ and macroscales in the electrolysis system. This is because the mass transport and charge transfer processes strongly affect the electrochemical performance and electrolytic efficiency during system upscaling and industrial‐scale operations with significant interactions 8 . However, conventional trial‐and‐errors lead to experimental overrunning cost to identify the critical factors and interactions for system design and optimization, especially for temporal behavior.…”

Optimization of Chromium and Lead Biosorption in Wastewater Using 33 Factorial Design

“…Where 𝑌 represents the response variable (e.g., the removal efficiency or the biosorption capacity), 𝐴, 𝐵 and 𝐶 are the independent variables (at low, medium and high levels), 𝐴 𝑖 , 𝐵 𝑗 , 𝐶 𝑘 (𝑖 = 𝑗 = 𝑘 = 1,2,3) represents the estimation of the main effect of the factor, whereas 𝐴𝐵 𝑖𝑗 , 𝐴𝐶 𝑖𝑘 and 𝐵𝐶 𝑗𝑘 represents the estimation of the second order interaction effect between factor 𝑖 and 𝑗, 𝑖 and 𝑘 , 𝑗 and 𝑘 for the response variable. Moreover, the coefficient µ is constant term, 𝐴𝐵𝐶 𝑖𝑗𝑘 shows the third order interactions' estimation, and finally 𝜀 𝑖𝑗𝑘 is a random error or residual component [8]. The main hypotheses regarding in this study are given in below.…”