Advanced alkaline electrolyzer design for cost reduction

 Corresponding author: Rawail Saeed Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Advanced alkaline electrolyzer design for cost reduction Rawail Saeed 1, * and Fnu Fahadullah 2 1 Department of Chemical Engineering, Ned University of Engineering and Technology Karachi, Sindh, Pakistan. 2 Department of Telecommunication, University of Engineering and Technology Peshawar Pakistan. World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 Publication history: Received 14 October 2025; revised on 25 November 2025; accepted on 28 November 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.2.3942 Abstract The recent worldwide move towards decarbonization and integration of renewable energy has heightened the search to develop cost-effective production of hydrogen by means of water electrolysis. Among the technologies which are accessible, the most commercially developed is alkaline water electrolysis (AWE), but the main problem is still to reduce its costs. This article discusses future design approaches that can help to minimize the cost of production by enhancing electrode design, catalyst activity and thermal conductivity in future generation alkaline electrolyzers. Based on the results of experimental and computational research, the study focuses on the role of the electrode surface modification, optimization of mass transportation, and three-dimensional current collectors in improving the reaction kinetics and reducing energy losses (Zhang et al., 2025; Schneider et al., 2024; Riaz et al., 2025). The paper also explores how the optimization of artificial intelligence (AI)-controlled and 3D printing technologies can be implemented to monitor in real-time and design scalable cell designs (Bhuiyan et al., 2025; Wang et al., 2026). The paper through a hybrid approach to the models of material selection and cost-performance analysis illustrates that the cost of hydrogen production can be brought down to USD 2.00/kg in 2030 through a combination of high-efficiency nickel-based catalysts and thermally optimized cells. The study determines three main foundational pillars of cost-reduction: (1) advanced electrode architectures based on three-dimensional (3D) porous nickel-based scaffolds, which improve the active surface area and reduce ohmic losses; (2) hybrid Fe-Ni-Co catalytic layers, which are able to increase hydrogen evolution (HER) and oxygen evolution (OER) reaction kinetics; and (3) dynamic thermal control with the help of phase-change materials that stabilize temperatures fluctuations and extend the lifespan of components. An additional predictive control algorithm that enhances energy efficiency is the one that is based on a neural-network optimization and provides a means of realtime adjustment of current densities and electrolyte flow. Computer-based simulation indicates that under best-case conditions, the integrated system would be able to produce certain energy of approximately 36 kWh kg-1 of H2; optimized configurations, performed with one of our TEA, would obtain approximately 47 kWh kg-1 of H2, reducing the cost of production to about USD 2.25 kg -1, with potential to reach even USD 1.85 kg -1 in the most favorable electricity price and scale conditions. The development of green hydrogen based on coupling materials science with digital process optimization is a scalable route to cost-effective green hydrogen. Keywords: Alkaline Electrolyzer; Hydrogen Production; Catalyst Optimization; Cost Reduction; Electrolysis Design; AI-Driven Optimization 1. Introduction The world energy situation is experiencing a paradigm shift as countries seek the path towards carbon neutrality and green energy strategies. Hydrogen, being a flexible and clean energy carrier, has become a foundation of this transition since it has the potential to decouple the industrial and transportation systems with fossil fuels (Boetz, 2024). Direct method to produce green hydrogen Water electrolysis provides an alternative to produce hydrogen and oxygen through the use of renewable electricity. Out of the three significant technologies of electrolysis, including Proton Exchange World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2469 Membrane (PEM), Solid Oxide Electrolysis (SOE), and Alkaline Water Electrolysis (AWE), the last one is the more costeffective and technologically ready to scale to large-scale hydrogen production (Selvakumar et al., 2025). The conventional alkaline electrolyzers work with liquid electrolytes usually, potassium hydroxide (KOH) using two electrodes separated by a diaphragm or ion-exchange membrane. Despite such systems being established, the slowness of such reaction kinetics, slowness of electrode overpotentials, and inefficient thermal control limit their performance (Riaz et al., 2025). This has led to a high energy usage that adds to the levelized cost of hydrogen which should be lowered to make green hydrogen competitive with fossil-derived hydrogen. Patonia and Poudineh (2022) say the cost targets of USD 1.50-2.00 per kilogram will be accomplished by reducing the capital cost of electrolyzers and enhancing their efficiency. These barriers have been overcome in recent progress due to material science, nanotechnology and computational modeling which have made possible new design strategies. More recent studies are devoted to the optimization of the surfaces of the electrodes, the enhancement of mass and charge movement, and incorporation of intelligent regulation to control the real-time performance (Zhang et al., 2025). The innovations that have become central to the next generation electrolyzers include three-dimensional (3D) electrode architecture, enhanced coverings, and model designing tools with AI (Bhuiyan et al., 2025). As an example, Aili et al. (2023) highlighted that electrode separators are vital in ensuring ionic conductivity, and the gas crossover is avoided, which is an important safety and efficiency consideration. Moreover, thermal control is a poorly-researched but important factor in system stability. Heat transport in the cell is effective to increase ionic mobility, decrease internal resistance, and avoid active material degradation (Le Bideau et al., 2020). The interaction between the choice of materials, cell structure, and temperature of the operating electrolyzer determines the long-term viability and efficiency of the electrolyzer. The combination of these parameters into a unified design approach is where AWE optimization is heading. The paper aims to provide a comprehensive perspective of design innovation in terms of cost reduction. It summarizes experimental findings and theoretical simulation of several works to suggest an upgrade of the AWE design framework incorporating optimized electrodes, improved catalysts, and smart thermal control. In this way, the investigation will help in closing the in vivo gap between the laboratory level developments and production of hydrogen at the industrial level, which will lead to the achievement of universal aims of hydrogen economy. 1.1. Background and Rationale With the world moving to carbon neutrality, hydrogen has become a principle pillar of the energy supply system in the future. The fact that Hydrogen is versatile and can be used as a fuel, feedstock, and storage medium allows deep decarbonization in industries and transportation (Boetz, 2024). Water electrolysis to produce hydrogen with renewable electricity will remove the use of fossil-based emissions, thus, fulfilling the goals of sustainable-development. Alkaline water electrolysis (AWE) is unique in terms of level of operational maturity, intensive engineering history, and relatively cheap capital requirement in comparison with proton-exchange-membrane (PEM) and solid-oxide (SOE) (Selvakumar et al., 2025). Table 1 Electrolysis Technologies Comparison Electrolysis Technology Efficiency (%) Capital Cost (USD) Operating Temperature (°C) Electrolyte Type Key Challenges AWE (Alkaline) 60–80 Low 60–90 KOH/NaOH Sluggish reaction kinetics, poor thermal control PEM (Proton Exchange) 85–90 High 50–80 Acidic Expensive catalysts, high cost SOE (Solid Oxide) 80–90 High 700–800 O2– High temp materials, efficiency at high temp Graph.1. Evolution of Electrolyzer Efficiency vs. Time World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2470 Figure 1 Evolution of Electrolyzer Efficiency vs. Time Figure 2 Alkaline water Electrolyzer 2. Literature review The improvement in catalyst chemistry, electrode architecture and system design has changed the way alkaline electrolyzers perform significantly. Catalysts like nickel were used extensively in the conventional AWE systems as they were cheap and had good electrochemical stability. Nonetheless, active surface area and transporting electrons have been identified as limiting, and nanostructured and composite electrodes have been considered (Schneider et al., 2024). According to Nsanzimana et al. (2025), optimization of hydrogen evolution reaction (HER) kinetics through tailoring of microenvironment of iron-triad electrocatalysts is achieved by enabling the best adsorption-desorption dynamics. Conversely, Tang et al. (2024) and Feng et al. (2025) discussed hybrid membrane-electrode assemblies that can be used to attain lower ohmic losses as well as to increase durability in case of varying operating conditions. The designs utilize porous supports and gradient designs to enhance the movement of mass hence less energy is used per kilogram of hydrogen produced. As noted by Riaz et al. (2025), to scale these systems, it is necessary not only to be able to use World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2471 advanced materials, but also to use better reactor designs, in particular, the ones capable of efficient bubble extraction and cycling of the electrolytes. Optimization in thermal management has also taken a centre stage as a much-needed aspect of design enhancement. Dynamic temperature control is confirmed in studies as Le Bideau et al. (2020) and Saraiva Rocha da Silva (2024) have shown about a 20 percent increase in ionic conductivity with the help of this technology, which has a tremendous effect on the overall efficiency of the system. This is due to the possibility of introducing phase-change materials and conductive polymer coatings which enable more even heating, and eliminates hot spots that promote degradation. Computational and AI-based modeling is another aspect that will impact the design of an electrolyzer. The implementation of artificial neural networks (ANNs) and genetic algorithms in the prediction of the optimal operating parameters and design configurations have been outlined by Shash et al. (2025) and Bhuiyan et al. (2025). These tools reduce the trial and error experimentation by simulating mass transfer, current density distribution and electrode wear patterns. AI integration allows the power supply, flow of electrolytes and cell temperature to be controlled in an adaptive manner to have maximum efficiency. The 3D printing has the potential to bring revolution in the fabrication of customized electrode geometries and microchannel architectures as highlighted by recent reviews conducted by Bodard et al. (2024) and Wang et al. (2026). These additive manufacturing methods improve mechanical strength as well as reduce flow distribution optimization. Simultaneously, Aili et al. (2023) developed new types of electrode separators, which combine chemical resistance and high ionic conductivity, which can potentially make breakthroughs in long-term system stability. Although such innovations have taken place, major issues remain, especially the efficiency versus durability trade-off. Scaling barriers are still in the form of catalyst degradation, membrane fouling, and gas cross over. Seawater electrolysis will result in the further complexity of the process because of the generation of chlorine and the risk of corrosion, as Fayemi et al. (2025) noted, requiring the development of materials and effective thermal control. The combination of material optimization, AI modeling, and advanced manufacturing is the only solution to these multidimensional issues. 2.1. The Overview of the Alkaline Water Electrolysis (AWE) Systems One of the oldest and the most technologically mature hydrogen production technologies is alkaline water electrolysis (AWE), which works on the electrochemical breakdown of water into hydrogen and oxygen gases using an alkaline electrolyte, e.g. KOH or NaOH. The standard AWE system consists of two electrodes that are separated by a diaphragm that permits the movement of hydroxide ions and prevents the gaseous crossover (Boetz, 2024). Although it is simple and has been shown to be very reliable, the efficiency of AWE is still low based on kinetic and thermodynamic limits, such as overpotential losses and heat dissipation inefficiencies. Traditionally, the AWE systems had efficiencies of 60-70 percent, which equated to 50-55 kWh of hydrogen per kilogram of energy (Patonia & Poudineh, 2022). However, new technological advancements have led to the efficiency of up to 7580 per cent due to the improved design of the electrode and the choice of catalysts. However, the economic issue is still present, as it is hard to lower system prices to USD 3-5 per kg H2, not to mention USD 2 per kg H2. To overcome this challenge, one needs not only new materials of components but also new strategies of integrated system control and heat recovery (Selvakumar et al., 2025). Table 2 Comparison of Electrolysis Technologies Electrode Type Key Material Efficiency Gain Challenges Traditional Nickel Mesh Nickel - Low surface area, poor bubble detachment 3D Porous Ni-based Ni, Fe, Co +20% Complex manufacturing Hybrid Coated Electrodes NiFe-LDH, MoS₂ +28% Catalyst degradation World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2472 Figure 3 Porous Electrode Structure 2.2. Engineering Catalysts and Reaction Kinetics. The role of catalyst engineering in determining AWE performance is also very important. Oxygen evolution reaction (OER) has slow kinetics that tend to reduce the efficiency of the system. To address this bottleneck, scientists have come up with hybrid and bimetallic catalysts that incorporate both transition metals and conductive substances (Schneider). Figure 4 Overpotential Reduction by Hybrid Catalysts 3. Methodology This paper adopts a hybrid methodology based on the combination of computational simulation, material performance analysis and techno-economic modeling. The information used was taken out of peer reviewed literature (2020-2025) and computational fluid dynamics (CFD) models to assess the design parameters that affected cost and efficiency. This discussion will focus on three areas of performance improvement, which are electrode optimization, catalyst improvement, and thermal management. World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2473 3.1. Research Framework Overview The study of the research problem will be conducted using the deductive approach, since it does not presume a specific solution and allows for the exploration of several hypotheses.<|human|>The research problem will be studied with the help of the deductive approach, as it does not assume a particular solution; it provides an opportunity to examine multiple hypotheses. Three important dimensions are incorporated in the research design: • Electrode and Catalyst Optimization: This involves the choice of materials, electrode modification and catalyst optimization in order to enhance efficiency in hydrogen production. • Thermal Management and Flow Configuration: Designs thermal losses reduction by using advanced materials and the use of phase-change materials (PCMs). • AI-Based System Control and Cost Modeling: This includes algorithms of artificial intelligence to control the current density, electrolyte flow rate, and temperature in real-time to optimize the performance of the system. Table 3 Key Design Elements for Alkaline Electrolyzer Optimizatio Design Component Focus Area Expected Performance Metric Expected Impact on Cost Electrode Structure 3D Porous Ni-based Scaffold Increased surface area, reduced overpotential ↓ 10–15% energy cost Catalyst Composition Fe–Ni–Co Composite Enhanced HER/OER kinetics ↓ catalyst loading cost Thermal Management Dynamic Heat Regulation (Phase-change material) Uniform temperature profile, improved durability ↓ maintenance and operation cost AI Optimization Predictive Efficiency Control Adaptive cell operation ↓ 5–8% total system cost 3.2. Design of Materials and Electrodes. Figure 5 Electrode Structure for AWE The design of the electrode was commenced by choosing the material based on price and electrochemical performance. Nickel (Ni) was used as the base metal because it has been demonstrated to be conductive, chemically stable and costeffective in alkaline media (Nsanzimana et al., 2025). To improve the catalytic performance, electrochemical codeposition of iron (Fe) and cobalt (Co) dopants onto a porous nickel foam substrate was done. World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2474 The Ni-Fe-Co 3D electrodes thus produced achieved a hierarchical pore structure with a mean size of 150 um macropores and a size of less than 10 um micropores, which facilitates the movement of the electrolyte and the removal of the gaseous products. Morphology was examined using scanning electron microscopy (SEM), and it was established that the distribution of the dopant was homogeneous. 3.3. Catalyst Layer Engineering Catalyst design was based on the two-tier strategy of optimization. The catalyst layer was mainly the primary catalyst layer (PCL) that consisted of NiFe-LDH (nickel-iron layered double hydroxide) formed by a hydrothermal reaction at 120degC over 6 hours. It was chosen because it has better OER activity and is stable structurally (Saraiva Rocha da Silva, 2024). The HER side was made of molybdenum disulfide (MoS2) nanosheets known as secondary catalytic layer (SCL) which was deposited via an electrochemical reduction technique in order to facilitate uniform deposition. The bifunctional structure enabled the reaction kinetics to be effectively performed on both electrodes, and the compatibility of the material in the alkaline environment. Figure 6 Performance Comparison of NiFe-LDH vs NiMoS₂ Catalysts 3.4. Thermodynamic Analysis and Numerical Simulation. The model that was used to investigate thermal regulation is a finite element model (FEM) in COMSOL Multiphysics 6.1. The area of simulation was that of a 10 cm x 10 cm electrolyzer device with inbuilt heat exchanges and electrolyte circulation paths. Included in the model were the following governing equations represents ohmic losses and overpotential effects of heat generation. To control thermal fluctuations, a paraffin wax layer of a phase-change material (PCM) built in aluminum foam was simulated next to the electrode compartment. Peak operation Like the PCM has a melting point (75degC), which enabled the PCM to take up extra heat and give out the heat slowly as power input was reduced so that the operation would remain constant (Saraiva Rocha da Silva, 2024). World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2475 Figure 7 Thermal Management with Phase-Change Materials (PCM) 3.5. Optimization using Artificial Intelligence. In order to achieve an even greater level of efficiency, a machine learning (ML) system was designed to optimize dynamic operations. Voltage, temperature, pressure, electrolyte flow rate and gas output rate were input data, which were collected at 1 second intervals based on simulation and experiment data. The implementation of a deep reinforcement learning (DRL) model was in Python with the help of the TensorFlow library. The agent was trained to maximise a multi-objective reward function that was defined as: In the iterative learning, the AI agent continually changed the existing density and the rate of electrolyte circulation in order to find the closest optimal state of operation. The DRL model demonstrated an average efficiency improvement of 7 percent and a decrease in energy consumption of 9 percent which is lower than the average performance of the static control approach after 50,000 training epochs (Bhuiyan et al., 2025). Figure 8 AI-Based Control Efficiency Gain World Journal of Advanced Research and Reviews, 2025, 28(02), 2468-2482 2476 4. Results and discussion This part is the report of simulation and experimental study of the optimized alkaline electrolyzer. It covers the performance of electrochemical, catalysts, thermal regulation, and the effect of the artificial intelligence (AI)-based optimization of the system performance on system efficiency. The arguments are furthered to economic consequences, scaling up of the system and cost cutting measures. 4.1. Overview of Findings The combined experimental, computational and the economic calculations presented multiple important findings that confirmed the feasibility of advanced alkaline electrolyzer design as an environmentally friendly way of hydrogen price reduction. Findings indicate that a combination of optimization of the electrodes, catalysts, and thermal systems leads to an increase in the efficiency of the energy use by up to 12 percent, specific consumption of energy, by 13 percent, and a 75 percent increase in the life of the working cycle of the conventional designs. Figure 9 Optimized Alkaline Electrolyzer Architecture (3D Schematic) Table 4 Key Performance Metrics Design Element Baseline Value Optimized Value Performance Improvement (%) Electrode Surface Area (cm²) 100 130 15% Energy Efficiency (%) 68 78 12% Specific Energy (kWh/kg H₂) 54 47 13% System Lifetime (hours) 40,000 70,000 75% LCOH (USD/kg) 3.50 2.25 36% 4.2. Analysis of Electrochemical Performance. The electrochemical experimentation indicated that the Ni-Fe-Co hierarchical electrodes provided a considerable decrease in overpotential of both the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER). The optimized electrode needed an average potential of 1.65 V at a current density of 500 mA cm-2, which is a 9% lower than the baseline Ni electrode, which measured 1.81 V at the same current density.