Production & Supply Chain

The electrochemical modelling of proton exchange membrane electrolyzers (PEMEZs) relies on the precise determination of several unknown parameters. Achieving this accuracy requires addressing a challenging optimization problem characterized by nonlinearity multimodality and multiple interdependent variables. Thus a novel approach for determining the unknown parameters of a detailed PEMEZ electrochemical model is proposed using the weighted mean of vectors algorithm (WMVA). An objective function based on mean square deviation (MSD) is proposed to quantify the difference between experimental and estimated voltages. Practical validation was carried out on three commercial PEMEZ stacks from different manufacturers (Giner Electrochemical Systems and HGenerators™). The first two stacks were tested under two distinct pressure-temperature settings yielding five V–J data sets in total for assessing the WMVA-based model. The results demonstrate that WMVA outperforms all optimizers achieving MSDs of 1.73366e−06 1.91934e−06 1.09306e−05 6.18248e−05 and 4.41586e−06 corresponding to improvements of approximately 88% 82.9% 82.4% 54.5% and 59.5% over the poorest-performing algorithm in each case respectively. Moreover comparative analyses statistical studies and convergence curves confirm the robustness and reliability of the proposed optimizer. Additionally the effects of temperature and hydrogen pressure variations on the electrical and physical steady-state performance of the PEMEZ are carefully investigated. The findings are further reinforced by a dynamic simulation that illustrates the impact of temperature and supplied current on hydrogen production. Accordingly the article facilitates better PEMEZ modelling and optimizing hydrogen production performance across various operating conditions.

Solid oxide electrolysers (SOEs) can decarbonise H2 supply when powered by renewable electricity but remain constrained by high electrical demand and integration penalties. Our objective is to minimise the electrical (Pel) and thermal (Qth) energy demand per mole of H2 by jointly tuning cell temperature steam fraction steam utilisation pressure and current density. Compared with prior single-variable or thermo-neutral-constrained studies we develop and validate a steady-state process-level optimisation framework that couples an Aspen Plus SOE model with electrochemical post-processing and heat caused by ohmic resistance recovery. A Box–Behnken design explores five key operating parameters to capture synergies and trade-offs between Qth and Pel energy inputs. Single-objective optimisation yields Pel = 170.1 kJ mol⁻¹ H2 a 41.4% reduction versus literature baselines. Multi-objective optimisation using an equal-weighted composite desirability function aggregating thermal and electrical demands further reduces Pel by 21.2% while balancing thermal input 4–8% lower than single-objective baselines at moderate temperature (~781 °C) and pressure (~17.5 bar). Findings demonstrate a clear process intensification advantage over previous studies by simultaneously leveraging operating parameter synergies and heat-integration. However results are bounded by steady-state perfectly mixed isothermal assumptions. The identified operating windows are mechanistically grounded targets that warrant stack-scale and plantlevel validation.