Production & Supply Chain

This article analyzes and compares three methodologies for identifying suitable regions for solar hydrogen production using photovoltaic panels: AHP (Analytic Hierarchy Process) FAHP (Fuzzy Analytic Hierarchy Process) and MC-FAHP (Monte Carlo FAHP) integrated with GIS (Geographic Information Systems). The study employs ten criteria across technical (Global Horizontal Irra diance temperature slope elevation orientation) economic (distance from transportation and electrical networks) and social (population density proximity to residential areas) factors. Environmental and exclusion criteria define restrictive zones. The analysis reveals that while all three methods agree on areas of low suitability they diverge in their classification of "Suitable" "Highly Suitable" and "Most Suitable" regions. FAHP identifies 229.573 km2 as "Highly Suitable" compared to AHP’s 222.048 km2 and MC-FAHP’s 230.299 km2 for "Suitable" areas. Despite these differences the energy potential is consistent across methods totaling around 79000 TWh/year with MC-FAHP estimating the highest hydrogen production potential at 1.51 billion tons/year. The study concludes that fuzzy-based methods (FAHP and MC-FAHP) better handle uncertainties than traditional AHP. The MC-FAHP method in particular performs well in managing stochastic variability and yielding more reliable results. The findings are validated through a case study in Guider and Maroua highlighting the importance of socio-economic and environmental criteria in decision-making. A sensitivity analysis reveals that economic and social criteria significantly influence land suitability underscoring the importance of criteria selection in decision-making.

Green hydrogen represents a critical underpinning technology for achievingcarbon neutrality. Although researchers often fixate on its energy inputs atruly ‘green’ hydrogen production process would also be sustainable in termsof water and materials inputs. To address this holistic challenge we demon-strate a new green hydrogen production system which can utilize secondarywastewater as the input (preserving scarce fresh water supplies for drinkingand sanitation). The enabling feature of the proposed system is a self-grownbifunctional CoNi electrode which consists of ultrathin spontaneously depos-ited CoNi nanosheets on a three-dimensional nickel foam. As such a greensynthesis process was developed using an immersion procedure at room-temperature with zero net energy input. Testing revealed that the synthesizedCoNi electrodes can reach a current density of 10 mA cm2 at a small overpo-tential of 197 mV for the hydrogen evolution reaction and 315 mV for the oxy-gen evolution reaction in alkalified wastewater. The values are 16.5%and 6.5% smaller than that from precious catalysts (20 wt% Pt/C and RuO 2 respectively). Importantly this CoNi catalyst offers outstanding durability foroverall wastewater splitting. A prototype solar-energy-powered rooftop waste-water splitting system was constructed and can produce more than 100 Lhydrogen on a sunny day in Sydney Australia. Taken together these resultsindicate that it is promising to unlock holistically green routes for hydrogenproduction by wastewater uplifting with regards to water energy and mate-rials synthesis.

Advancements in water electrolysis technologies are crucial for green hydrogen production. Proton exchange membrane water electrolysis (PEMWE) is characterized by its efficiency and environmental benefits. The pre diction and optimization of hydrogen production rates (HPRs) in PEMWE systems is difficult and still challenging because of the complexity of the system as well as the operational parameters. The integration of artificial in telligence (AI) and machine learning (ML) appears to be effective in optimization within the energy sector. Hence this work employs the artificial neural network (ANN) to develop a model that accurately predicts HPR in PEMWE setups. A novel approach is introduced by employing the Levenberg–Marquardt backpropagation (LMBP) algorithm for training the ANN. This model is designed to predict HPR based on critical operational parameters including anode and cathode areas (mm2 ) cell voltage (V) and current (A) water flow rate (mL/ min) power (W) and temperature (K). The optimized ANN configuration features an architecture with 7 input nodes two hidden layers of 64 neurons each and a single output node. The performance of the ANN model was evaluated against conventional regression models using key metrics: mean squared error (MSE) coefficient of determination (R2 ) and mean absolute error (MAE). The findings of this study reveal that the developed ANN model significantly outperforms traditional models achieving an R2 value of 0.9989 and an MAE of 0.012. In comparison random forest (R2 = 0.9795) linear regression (R2 = 0.9697) and support vector machines (R2 = − 0.4812) show lower predictive accuracy underscoring the ANN model’s superior performance. This work demonstrates the efficiency of the LMBP in enhancing hydrogen production forecasts and sets a foundation for future improvements in PEMWE efficiency. By enabling precise control and optimization of operational pa rameters this study contributes to the broader goal of advancing green hydrogen production as a viable and scalable alternative to fossil fuels offering both immediate and long-term benefits to sustainable energy initiatives.

The energy sector constitutes a dynamic and complex system indicating that its actions are influenced not just by its individual components but also by the emergent behavior resulting from interactions among them. Moreover there are crucial limitations of previous approaches for addressing the sustainability challenge of the energy sector. Changing transforming and integrating paradigms are the most relevant leverage points for transforming a given system. In other words nowadays the integration of new predominant paradigms in order to provide a unified framework could aim at this actual transformation looking for a sustainable future. This research aims to develop a new unified framework for the integration of the following three paradigms: (1) Sustainability (2) Complexity and (3) Systems Thinking which will be applied to achieving sustainable energy production (using hydrogen production as a case study). The novelty of this work relies on providing a holistic perspective through the integration of the aforementioned paradigms considering the multiple and complex interdependencies among the economy the environment and the economy. For this purpose an integrated seven-stage approach is introduced which explores from the starting point of the integration of paradigms to the application of this integration to sustainable energy production. After applying the Three-Paradigm approach for sustainable hydrogen production as a case study 216 feedback loops are identified due to the emerged complexity linked to the analyzed system. Additionally three system dynamics-based models are developed (by increasing the level of complexity) as part of the application of the Three-Paradigm approach. This research can be of interest to a broad professional audience (e.g. engineers policymakers) as looks into the sustainability of the energy sector from a holistic perspective considering a newly developed Three-Paradigm model considering complexity and using a Systems Thinking approach.

In response to the European Union’s initiative toward achieving carbon neutrality the utilization of water electrolysis for hydrogen production has emerged as a promising avenue for decarbonizing current energy systems. Among the various approaches Solid Oxide Electrolysis Cell (SOEC) presents an attractive solution especially due to its potential to utilize impure water sources. This study focuses on modeling a SOEC supplied with four distinct streams of treated municipal wastewaters using the Aspen Plus software. Through the simulation analysis it was determined that two of the wastewater streams could be effectively evaporated and treated within the cell without generating waste liquids containing excessive pollutant concentrations. Specifically by evaporating 27% of the first current and 10% of the second it was estimated that 26.2 kg/m3 and 9.7 kg/m3 of green hydrogen could be produced respectively. Considering the EU’s target for Italy is to have 5 GW of installed power capacity by 2030 and the mass flowrate of the analyzed wastewater streams this hydrogen production could meet anywhere from 0.4% to 20% of Italy’s projected electricity demand.

Hydrogen is envisaged to play a major role in decarbonizing our future energy systems. Hydrogen is ideal for storing renewable energy over longer durations strengthening energy security. It can be used to provide electricity renewable heat power long-haul transport shipping and aviation and in decarbonizing several industrial processes. The cost of green hydrogen produced from renewable via electrolysis is dominated by the cost of electricity used. Operating electrolyzers only during periods of low electricity prices will limit production capacity and underutilize high investment costs in electrolyzer plants. Hydrogen production from deep offshore wind energy is a promising solution to unlock affordable electrolytic hydrogen at scale. Deep offshore locations can result in an increased capacity factor of generated wind power to 60–70% 4–5 times that of onshore locations. Dedicated wind farms for electrolysis can use the majority >80% of the produced energy to generate economical hydrogen. In some scenarios hydrogen can be the optimal carrier to transport the generated energy onshore. This review discusses the opportunities and challenges in offshore hydrogen production using electrolysis from wind energy and seawater. This includes the impact of site selection size of the electrolyzer and direct use of seawater without deionization. The review compares overall electrolysis system efficiency cost and lifetime when operating with direct seawater feed and deionized water feed using reverse osmosis and flash evaporation systems. In the short to medium term it is advised to install a reverse osmosis plant with an ion exchanger to feed the electrolysis instead of using seawater directly.

Coastal regions have abundant off-shore wind energy resources and surrounding areas have large-scale liquefied natural gas (LNG) receiving stations. From the engineering perspectives there are limitations in unstable off-shore wind energy and fluctuating LNG loads. This article offers a new energy scheme to combine these 2 energy units which uses surplus wind energy to produce hydrogen and use LNG cold energy to liquefy and store hydrogen. In addition in order to improve the efficiency of utilizing LNG cold energy and reduce electricity consumption for liquid hydrogen (LH2) production at coastal regions this article introduces the liquid air energy storage (LAES) technology as the intermediate stage which can stably store the cold energy from LNG gasification. A new scheme for LNG-LAES-LH2 hybrid LH2 production is built. The case study is based on a real LNG receiving station at Hainan province China and this article presents the design of hydrogen production/liquefaction process and carries out the optimizations at key nodes and proves the feasibility using specific energy consumption and exergy analysis. In a 100 MW system the liquid air storage round-trip efficiency is 71.0% and the specific energy consumption is 0.189 kWh/kg and the liquid hydrogen specific energy consumption is 7.87 kWh/kg and the exergy efficiency is 46.44%. Meanwhile the corresponding techno-economic model is built and for a LNGLAES-LH2 system with LH2 daily production 140.4 tons the shortest dynamic payback period is 9.56 years. Overall this novel hybrid energy scheme can produce green hydrogen using a more efficient and economical method and also can make full use of surplus off-shore wind energy and coastal LNG cold energy.

Proton exchange membrane (PEM) electrolyser stands as a promising candidate for sustainable hydrogen pro duction from renewable energy sources (RESs). Given the fluctuating nature of RESs accurate modelling of the PEM electrolyser is crucial. Nonetheless complex models of the PEM electrolyser demand substantial time and resource investments when integrating them into a large-scale power system. The majority of introduced models in the literature are either overly intricate or fail to effectively reproduce the dynamic behaviour of the PEM electrolyser. To this end this article aims to develop a model that not only captures the dynamic response of the PEM electrolyser crucial for conducting flexibility studies in the power system but also avoids complexity for seamless integration into large-scale simulations without comprising accuracy. To verify the model it is vali dated against static and dynamic experimental data. Compared to the investigated experimental cases the model exhibited an average error of 0.66% and 3.93% in the static and dynamic operation modes respectively.

The cost of polymer electrolyte water electrolysis (PEWE) is dominated by the price of electricity used to power the water splitting reaction. We present a liquid water fed polymer electrolyte water electrolyzer cell operated at a cell temperature of 100 °C in comparison to a cell operated at state-of-the-art operation temperature of 60 °C over a 300 h constant current period. The hydrogen conversion efficiency increases by up to 5% at elevated temperature and makes green hydrogen cheaper. However temperature is a stress factor that accelerates degradation causes in the cell. The PEWE cell operated at a cell temperature of 100 °C shows a 5 times increased cell voltage loss rate compared to the PEWE cell at 60 °C. The initial performance gain was found to be consumed after a projected operation time of 3500 h. Elevated temperature operation is only viable if a voltage loss rate of less than 5.8 μV h−1 can be attained. The major degradation phenomena that impact performance loss at 100 °C are ohmic (49%) and anode kinetic losses (45%). Damage to components was identified by post-test electron-microscopic analysis of the catalyst coated membrane and measurement of cation content in the drag water. The chemical decomposition of the ionomer increases by a factor of 10 at 100 °C vs 60 °C. Failure by short circuit formation was estimated to be a failure mode after a projected lifetime 3700 h. At elevated temperature and differential pressure operation hydrogen gas cross-over is limiting since a content of 4% hydrogen in oxygen represents the lower explosion limit.

The research motivation of this paper is to utilize the large amount of energy wasted during the LNG (liquefied natural gas) gasification process and proposes a synergistic liquefied hydrogen (LH2) production and storage process scheme for LNG receiving station and methane reforming hydrogen production process - SMR-LNG combined liquefied hydrogen production system which uses the cold energy from LNG to pre-cool the hydrogen and subsequently uses an expander to complete the liquefaction of hydrogen. The proposed process is modeled and simulated by Aspen HYSYS software and its efficiency is evaluated and sensitivity analysis is carried out. The simulation results show that the system can produce liquefied hydrogen with a flow rate of 5.89t/h with 99.99% purity when the LNG supply rate is 50t/h. The power consumption of liquefied hydrogen is 46.6kWh/kg LH2; meanwhile the energy consumption of the HL subsystem is 15.9kWh/kg LH2 lower than traditional value of 17~19kWh/kg LH2. The efficiency of the hydrogen production subsystem was 16.9%; the efficiency of the hydrogen liquefaction (HL) subsystem was 29.61% which was significantly higher than the conventional industrial value of 21%; the overall energy efficiency (EE1) of the system was 56.52% with the exergy efficiency (EE2) of 22.2% reflecting a relatively good thermodynamic perfection. The energy consumption of liquefied hydrogen per unit product is 98.71 GJ/kg LH2.