This thesis presents a life cycle assessment (LCA) comparing the environmental impacts of three electrolyzer technologies: Smart Alkaline Electrolyzer (SAE), Proton Exchange Membrane (PEM), and conventional Alkaline Electrolyzer (AEC). SAE, a novel technology distinguished by its modularity and scalability, offers seamless integration with renewable energy sources (RES). The study's scope, encompassing a cradle-to-gate analysis for 1 MW electrolyzers in Spain powered by solar PV, highlights the critical influence of electricity sources on environmental impacts. SAE outperformed PEM in seven of the eight assessed impact categories—climate change, terrestrial acidification, freshwater eutrophication, marine eutrophication, terrestrial ecotoxicity, ozone depletion, and photochemical oxidant formation potential (PCOF)—with a notable reduction in Global Warming Potential (GWP) at 7.39 kg CO₂ eq per kg H₂. Compared to AEC, SAE performed better in three impact categories and similar results in others. Key drivers of environmental impact were identified, with copper usage in power electronics being a significant contributor. The sensitivity analysis showed that 30% reduction in copper usage improved SAE's performance across 5 impact categories compared to AEC (climate change, acidification: terrestrial, eutrophication: marine, ozone depletion, PCOF: terrestrial ecosystems). The study identifies the key drivers of impacts within each electrolyzer and clearly delineates the subsystems of the BoP to make the comparison and analysis clear and to assess the overall impacts. The study also provides a structured and consistent inventory for the electrolyzers, enabling easy inclusion of other electrolyzer technologies, which several studies recommend.

Due to greenhouse gas emissions, global warming has severe environmental consequences. To tackle this issue, XINTC produces green hydrogen using alkaline electrolysis to replace fossil fuels. Green hydrogen’s primary challenge is its dependence on intermittent energy sources for electrolysis. Hydrogen storage is necessary for the hydrogen economy to meet production and demand peaks. This work focused on hydrogen storage using double membrane gas holders storing hydrogen at atmospheric pressure, a novel hydrogen storage method. The main goal of the storage is to avoid power cycling the downstream hydrogen compressor. Energy and mass balances are calculated considering molecule permeation through the membranes and water condensation. Weather conditions are also taken into account. To regulate the pressure in the storage, a control system consisting of PID controllers connected to pressure relief valves was designed.
The model shows the behaviour as a response to changing weather conditions. It has been proven that the water in the system will condense and, at certain points, even freeze. A drain is necessary to evacuate the water forming in the system. The control system manages the system accordingly, opening the valves to the desired amounts at the desired moments.
It was also proven that one should not have to worry about forming a combustible hydrogen-oxygen mixture. If the storage is filled at 10% of its maximum value, it takes more than 500 days to reach that value.
The permeation of nitrogen through the membrane is a bigger issue. If the storage is filled to 10% of its maximum value, the maximum concentration of 300 PPM is exceeded within three days at a standstill. Thus, the storage will have to be drained long before safety issues arise.
The model is fully modular, meaning that it can be run for all locations around the world. It can easily compare storage behaviour and performance.
The outcome of this research has led to a better understanding of the behaviour of hydrogen storage in membrane gas holders. The model developed can be used to effectively manage hydrogen storage, reducing any hydrogen losses and maximising storage efficiency

The world is witnessing unprecedented climate changes, which have worsened over the past couple of years. Despite significant progress in renewable energy, achieving a reduction of global warming remains challenging. Renewable energy sources like solar, wind, and hydroelectric technologies offer promising solutions, but challenges persist due to their intermittency and geographical dependency. Green hydrogen, generated through electrolysis using renewable energy, has come up as a solution for storing electric energy and providing a constant energy supply.

Alkaline water electrolysis has emerged as one of the most promising electrolysis methods due to its large-scale operation, long durability and low costs. This does not come without challenges, one of them being leakage currents, which reduces the efficiency of the electrolyser. Leakage currents occur when not all current is used for hydrogen production, but some of it leaks into, for example, the produced hydrogen stream. To reduce these leakage currents, more research into the origins is needed. The first step is modelling the electrolysis while considering as much as possible of the physics happening inside the system. Current models of alkaline water electrolysers are either modelled using only mathematical equations or neglect the operating parameters, which makes the results highly variable per system. Other models do use analytical methods with experimental results, but these models only comprise one electrolysis cell rather than a full stack.

This work consists of developing two three-dimensional models, validating them using experiments, and using them to predict the effect of changes in geometry. The first model was made using COMSOL Multiphysics software and used to research the water electrolysis stack, which comprised one or eight cells using electrochemical relations and physical data. It was found that a model could be made that fitted the experiments within the error margin of the experiments (<2.5%). It lacked flexibility but overall showed good results for an electrolyser stack of one or eight cells. The second model was made using an equivalent electrical circuit (EEC) of the electrolyser in Python via the PySpice module. A steady-state model, including the leakage currents, could be developed by calculating all system resistances, namely the cell, inlet/outlet, and manifold resistances. This model overestimated the performance of the electrolyser by 10-15% for low current densities and 2-4% for high current densities. Nevertheless, it was highly adaptable for different scenarios, making it valuable for research into optimising the electrolysis stack. Both models were used to predict the effect of changes in geometry; the effect of the length of the inlets, and the number of cells. This showed that the EEC model was better suited for this research.

The increasing adoption of hydrogen in industrial applications is driven by its potential to decarbonize various industries. Among the various methods of hydrogen production, water electrolysis is considered one of the environmentally friendliest options. However, hydrogen produced from water electrolysis contains impurities such as oxygen and water vapour, and the required level of purity varies depending on the specific industrial application. To address this issue, catalytic recombination of hydrogen and oxygen into water is selected as a method for oxygen removal due to its high efficacy in completely converting oxygen. Subsequently, hydrogen drying is achieved using Pressure Swing Adsorption (PSA) following the catalytic recombination process. This thesis work primarily focuses on the modelling and sizing of adsorption columns within the PSA system. One-dimensional dynamic models describing the pressurization, adsorption, depressurization, and desorption steps of PSA are mathematically derived and developed in Python. The adsorption modelling approach is validated using experimental data from a scientific paper. Insightful information was obtained during the model validation process, shedding light on the consequences of the assumptions made to simplify the energy balance, as well as revealing the decrease in adsorption capacity during the pressurization process. The PSA system is designed to process 400 kg of hydrogen per day with the aim of reducing the water vapour content below 5 ppm. While pressure plays a central role in PSA control, it has been discovered that the primary design challenge relates to temperature control within the operating range. Therefore, adsorption column sizing is optimized, taking into account PSA performance and required energy input. A sensitivity analysis is conducted to identify the optimal adsorbent, considering zeolite 3A and silica gel. Based on the results, a column length of 2 meters and a diameter of 0.0914 meters are considered optimal for zeolite-packed adsorption columns, resulting in a productivity of 35.62 mol/hr/kg and requiring 50.21 kJ during the desorption step. The optimal size for silica gel-packed adsorption columns has not been determined due to a significant temperature drop during desorption, which could risk ice formation and subsequent flow blockage. Nevertheless, silica gel, with its higher adsorption capacity leading to a longer adsorption step, remains a viable option from an operational perspective and should not be disregarded as a potential choice.

The unprecedented increase in mean ambient temperature due to immoderate CO2 emissions has shifted the energy policy towards the replacement of fossil fuels with renewable energy sources. Nevertheless, the intermittency of these technologies renders them unsuitable for reliable energy supply. A prominent solution is the utilization of renewable energy to produce green hydrogen that can be stored for later use.

The most environmental-friendly viable method for green hydrogen production is the electrolysis of water. Between the already existing types, alkaline electrolyser is the one with the highest technological maturity and lowest cost of hydrogen production. Despite that, the low energy efficiency, and the energy losses associated with the materials and the geometrical configuration make it difficult to produce hydrogen at competitive prices compared to fossil fuels. The connection with renewables exposes the electrolyser to variable loads that negatively affect the life of materials and the purity of hydrogen since the operating conditions like temperature and electrical current change constantly. Previous research work has shown that the energy losses owing to the electrode kinetics, diaphragm, and electrolyte resistivity are responsible for higher energy consumption than the thermodynamic minimum. These losses are a strong function of temperature and current density. This research project aims to investigate the performance of the electrolyser under fluctuating working conditions to get an insight into how energy consumption and efficiency are affected.

The research objectives are encountered by building a physical model that accurately predicts the electrochemical behavior of the cell under various circumstances and simulates the thermal response of the electrolyser. The numerous experiments performed at XINTC’s laboratory enabled the improvement of the model’s accuracy and the consideration of effects that are not easily confronted in a purely theoretical investigation. The results showed that the model accurately describes the experimental data of the cell with less than a 2% discrepancy. The model was also used for simulating the cell behavior under different pressures, electrodes, and diaphragm materials. The elevated temperatures and highly electroactive materials such as Nickel significantly reduce the voltage of the cell. Regarding the thermal model of the theoretical electrolysis system, sensitivity analysis of the current density, ambient conditions, and electrolyte flow was performed. The temperature across the system was uniform at an electrolyte volume flow of 5 Lt/min and the cooling load accounted for 20% of the electrolyser power. The thermal modeling of the real experimental setup was successful (<1.5°C deviation) at high current densities indicating the sound assumptions of the model. On the contrary, at low current densities, the discrepancy between the model and the experiments reached 10% due to the additional heat generation induced by the shunt currents. For that reason, a simplified electrical circuit analysis was formulated and included in the model. This led to reducing the deviation to less than 5% and at the same time calculation of the current efficiency in the absence of measuring devices was achieved.

Climate change due to the extensive use of fossil fuels has led to the deployment of alternative green ones, such as hydrogen. Green hydrogen is produced by renewable electricity and is CO₂-free. This thesis focuses on the production of hydrogen by implementing alkaline water electrolysis as the core technology. 
Due to the intermittency of renewable sources, alkaline water electrolysers are forced to operate in their part-load range. The cathodic hydrogen species that remains dissolved in the liquid electrolyte can end up to the anodic compartment, and hence lower the purity of the produced gaseous oxygen. This phenomenon is prominent in the part-load range and is called gas crossover. When the concentration of hydrogen in oxygen reaches the Lower Explosive Limit which is 4 vol%, spontaneous combustion can occur. Therefore, the electrolyser is forced to shut down for safety reasons.
This thesis focuses on understanding the mass transfer mechanisms of gas crossover in alkaline water electrolysis, in the part-load range. A literature study has been conducted in which the gas crossover mechanisms are thoroughly analyzed. The mitigation of gas crossover can lead the operation to lower current density ranges. From the mitigation strategies, a focus is given on the “dynamic switching of the electrolyte cycles". The dynamic switching of the electrolyte cycles is based on the periodic changeover of the operative electrolyte cycles between the partly-separated and the mixed mode. The anodic hydrogen content acquires a sinusoidal response, where the average value is less than the impurity in traditional operation. 
The gas crossover steady-state and dynamic models are mathematically derived and developed in Python. The models consider the mechanisms of gas crossover through the diaphragm and the electrolyte mixing. Therefore, the anodic hydrogen and cathodic oxygen content are calculated in the steady state and dynamically. The dynamic switching of electrolyte cycles can be simulated with the dynamic model.
The experiments are conducted to define the anodic hydrogen and cathodic oxygen content in a single cell configuration. The first experiment outputs the steady-state impurity as a function of the current density. The steady-state impurities show a descending tendency with an increasing current density. Next, the dynamic switching is performed and the anodic hydrogen content is recorded as a function of time. The average impurity in the dynamic switching is smaller than the result in the steady-state experiment.
The steady-state model sufficiently validates the literature data and verifies the experimental results. The dynamic switching model validates the literature data. Furthermore, it verifies the experimental results, when a correction factor is applied to the total volume of the separator tanks. The correction factor is required because the experimental impurities were measured at the exit of the single cell, resulting in faster system response. Finally, a sensitivity analysis is conducted to test the robustness of the dynamic model. The sensitivity analysis shows that the dynamic model can successfully simulate the operation of an alkaline water electrolyser.