Search results for: applied Buddhism

Authors: Murat Arıbaş, Uğur Özcan

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Due to the increasing number of universities and academics, the fund of the universities for research activities and grants/supports given by government institutions have increased number and quality of academic research projects. Although every academic research project has a specific purpose and importance, limited resources (money, time, manpower etc.) require choosing the best ones from all (Amiri, 2010). It is a pretty hard process to compare and determine which project is better such that the projects serve different purposes. In addition, the evaluation process has become complicated since there are more than one evaluator and multiple criteria for the evaluation (Dodangeh, Mojahed and Yusuff, 2009). Mehrez and Sinuany-Stern (1983) determined project selection problem as a Multi Criteria Decision Making (MCDM) problem. If a decision problem involves multiple criteria and objectives, it is called as a Multi Attribute Decision Making problem (Ömürbek & Kınay, 2013). There are many MCDM methods in the literature for the solution of such problems. These methods are AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), UTADIS (Utilities Additives Discriminantes), ELECTRE (Elimination et Choix Traduisant la Realite), MAUT (Multiattribute Utility Theory), GRA (Grey Relational Analysis) etc. Teach method has some advantages compared with others (Ömürbek, Blacksmith & Akalın, 2013). Hence, to decide which MCDM method will be used for solution of the problem, factors like the nature of the problem, types of choices, measurement scales, type of uncertainty, dependency among the attributes, expectations of decision maker, and quantity and quality of the data should be considered (Tavana & Hatami-Marbini, 2011). By this study, it is aimed to develop a systematic decision process for the grant support applications that are expected to be evaluated according to their scientific adequacy by multiple evaluators under certain criteria. In this context, project evaluation process applied by The Scientific and Technological Research Council of Turkey (TÜBİTAK) the leading institutions in our country, was investigated. Firstly in the study, criteria that will be used on the project evaluation were decided. The main criteria were selected among TÜBİTAK evaluation criteria. These criteria were originality of project, methodology, project management/team and research opportunities and extensive impact of project. Moreover, for each main criteria, 2-4 sub criteria were defined, hence it was decided to evaluate projects over 13 sub-criterion in total. Due to superiority of determination criteria weights AHP method and provided opportunity ranking great number of alternatives TOPSIS method, they are used together. AHP method, developed by Saaty (1977), is based on selection by pairwise comparisons. Because of its simple structure and being easy to understand, AHP is the very popular method in the literature for determining criteria weights in MCDM problems. Besides, the TOPSIS method developed by Hwang and Yoon (1981) as a MCDM technique is an alternative to ELECTRE method and it is used in many areas. In the method, distance from each decision point to ideal and to negative ideal solution point was calculated by using Euclidian Distance Approach. In the study, main criteria and sub-criteria were compared on their own merits by using questionnaires that were developed based on an importance scale by four relative groups of people (i.e. TUBITAK specialists, TUBITAK managers, academics and individuals from business world ) After these pairwise comparisons, weight of the each main criteria and sub-criteria were calculated by using AHP method. Then these calculated criteria’ weights used as an input in TOPSİS method, a sample consisting 200 projects were ranked on their own merits. This new system supported to opportunity to get views of the people that take part of project process including preparation, evaluation and implementation on the evaluation of academic research projects. Moreover, instead of using four main criteria in equal weight to evaluate projects, by using weighted 13 sub-criteria and decision point’s distance from the ideal solution, systematic decision making process was developed. By this evaluation process, new approach was created to determine importance of academic research projects.

Keywords: Academic projects, Ahp method, Research projects evaluation, Topsis method.

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Authors: Katerina Zaharieva, Katya Milenova, Zara Cherkezova-Zheleva, Alexander Eliyas, Boris Kunev, Ivan Mitov

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The synthesized nanosized manganese ferrite-type of samples have been tested as photocatalysts in the reaction of oxidative degradation of model contaminant Reactive Black 5 (RB5) dye in aqueous solutions under UV irradiation. As it is known this azo dye is applied in the textile-coloring industry and it is discharged into the waterways causing pollution. The co-precipitation procedure has been used for the synthesis of manganese ferrite-type of materials: Sample 1 - Mn0.25Fe2.75O4, Sample 2 - Mn0.5Fe2.5O4 and Sample 3 - MnFe2O4 from 0.03M aqueous solutions of MnCl2•4H2O, FeCl2•4H2O and/or FeCl3•6H2O and 0.3M NaOH in appropriate amounts. The mechanochemical activation of co-precipitated ferrite-type of samples has been performed in argon (Samples 1 and 2) or in air atmosphere (Sample 3) for 2 hours at a milling speed of 500 rpm. The mechano-chemical treatment has been carried out in a high energy planetary ball mill type PM 100, Retsch, Germany. The mass ratio between balls and powder was 30:1. As a result mechanochemically activated Sample 4 - Mn0.25Fe2.75O4, Sample 5 - Mn0.5Fe2.5O4 and Sample 6 - MnFe2O4 have been obtained. The synthesized manganese ferrite-type photocatalysts have been characterized by X-ray diffraction method and Moessbauer spectroscopy. The registered X-ray diffraction patterns and Moessbauer spectra of co-precipitated ferrite-type of materials show the presence of manganese ferrite and additional akaganeite phase. The presence of manganese ferrite and small amounts of iron phases is established in the mechanochemically treated samples. The calculated average crystallite size of manganese ferrites varies within the range 7 – 13 nm. This result is confirmed by Moessbauer study. The registered spectra show superparamagnetic behavior of the prepared materials at room temperature. The photocatalytic investigations have been made using polychromatic UV-A light lamp (Sylvania BLB, 18 W) illumination with wavelength maximum at 365 nm. The intensity of light irradiation upon the manganese ferrite-type photocatalysts was 0.66 mW.cm-2. The photocatalytic reaction of oxidative degradation of RB5 dye was carried out in a semi-batch slurry photocatalytic reactor with 0.15 g of ferrite-type powder, 150 ml of 20 ppm dye aqueous solution under magnetic stirring at rate 400 rpm and continuously feeding air flow. The samples achieved adsorption-desorption equilibrium in the dark period for 30 min and then the UV-light was turned on. After regular time intervals aliquot parts from the suspension were taken out and centrifuged to separate the powder from solution. The residual concentrations of dye were established by a UV-Vis absorbance single beam spectrophotometer CamSpec M501 (UK) measuring in the wavelength region from 190 to 800 nm. The photocatalytic measurements determined that the apparent pseudo-first-order rate constants calculated by linear slopes approximating to first order kinetic equation, increase in following order: Sample 3 (1.1х10-3 min-1) < Sample 1 (2.2х10-3 min-1) < Sample 2 (3.3 х10-3 min-1) < Sample 4 (3.8х10-3 min-1) < Sample 6 (11х10-3 min-1) < Sample 5 (15.2х10-3 min-1). The mechanochemically activated manganese ferrite-type of photocatalyst samples show significantly higher degree of oxidative degradation of RB5 dye after 120 minutes of UV light illumination in comparison with co-precipitated ferrite-type samples: Sample 5 (92%) > Sample 6 (91%) > Sample 4 (63%) > Sample 2 (53%) > Sample 1 (42%) > Sample 3 (15%). Summarizing the obtained results we conclude that the mechanochemical activation leads to a significant enhancement of the degree of oxidative degradation of the RB5 dye and photocatalytic activity of tested manganese ferrite-type of catalyst samples under our experimental conditions. The mechanochemically activated Mn0.5Fe2.5O4 ferrite-type of material displays the highest photocatalytic activity (15.2х10-3 min-1) and degree of oxidative degradation of the RB5 dye (92%) compared to the other synthesized samples. Especially a significant improvement in the degree of oxidative degradation of RB5 dye (91%) has been determined for mechanochemically treated MnFe2O4 ferrite-type of sample with the highest extent of substitution of iron ions by manganese ions than in the case of the co-precipitated MnFe2O4 sample (15%). The mechanochemically activated manganese ferrite-type of samples show good photocatalytic properties in the reaction of oxidative degradation of RB5 azo dye in aqueous solutions and it could find potential application for dye removal from wastewaters originating from textile industry.

Keywords: nanostructured manganese ferrite-type materials, photocatalytic activity, Reactive Black 5, water treatment

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Swaleh Aboud, Mishal Gudka, David Obura

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Background: Coral reefs are becoming increasingly vulnerable due to several threats ranging from climate change to overfishing. This has resulted in increased management and conservation efforts to protect reefs from degradation and facilitate recovery. Recruitmentof new individuals are isimportant in the recovery process and critical for the persistence of coral reef ecosystems. Local coral community structure can be influenced by successful recruit settlement, survival, and growth Understanding coral recruitment patterns can help quantify reef resilience and connectivity, establish baselines and track changes and evaluate the effectiveness of reef restoration and conservation efforts. This study will examine the abundance and spatial pattern of coral recruits and how this relates to adult community structure, including the distribution of thermal resistance and sensitive genera and their distribution in different management regimes. Methods: Coral recruit and demography surveys were conducted from 2020 to 2022, covering 35 sites in 19coral reef locations along the Kenyan coast. These included marine parks, reserves, community conservation areas (CMAs), and open access areas from the north (Marereni) to the south (Kisite) coast of Kenya and across different reef habitats. The data was collected through the underwater visual census (UVC) technique. We counted adult corals (>10 cm diameter)of23 selected genera using belt transects (25 by 1 m) and sampling of 1 m2 quadrat (at an interval of 5m) for all coloniesless than 10 cm diameter. The benthic cover was collected using photo quadrats. The surveys were only done during the northeast monsoon season. The data wereanalyzed using the R program to see the distribution patterns and the Kruskal Wallis test to see whether there was a significant difference. Spearman correlation was also applied to assess the relationship between the distribution of coral genera in recruits and adults. Results: A total of 44 different coral genera were recorded for recruits, ranging from 3at Marereni to 30at Watamu Marine Reserve. Recruit densities ranged from 1.2±1.5recruit m-2 (mean±SD) at Likoni to 10.3± 8.4 recruit m-2 at Kisite Marine Park. The overall densityof recruitssignificantly differed between reef locations, with Kisite Marine Park and Reserve and Likonihaving significantly large differences from all the other locations, while Vuma, Watamu, Malindi, and Kilifi had significantly lower differences from all the other locations. The recruit generadensity along the Kenya coastwas divided into two clusters, one of which only included sites inKisite Marine Park. Adult colonies were dominated by Porites massive, Acropora, Platygyra, and Favites, whereas recruits were dominated by Porites branching, Porites massive, Galaxea, and Acropora. However, correlation analysis revealed a statistically significant positive correlation (r=0.81, p<0.05) between recruit and adult coral densities across the 23 coral genera. Marereni, which had the lowest densityof recruits, has only thermallyresistant coral genera, while Kisite Marine Park, with the highest recruit densities, has over 90% thermal sensitive coral genera. A weak positive correlation was found between recruit density and coralline algae, dead standing corals, and turf algae, whereas a weak negative correlation was found between recruit density and bare substrate and macroalgae. Between management regimes, marine reserves were found to have more recruits than no-take zones (marine parks and CMAs) and open access areas, although the difference was not significant. Conclusion: There was a statistically significant difference in the density of recruits between different reef locations along the Kenyan coast. Although the dominating genera of adults and recruits were different, there was a strong positive correlation between their coral communities, which could indicate self-recruitment processes or consistent distance seedings (of the same recruit genera). Sites such as Kisite Marine Park, with high recruit densities but dominated by thermally sensitive genera, will, on the other hand, be adversely affected by future thermal stress. This could imply that reducing the threats to coral reefs such as overfishingcould allow for their natural regeneration and recovery.

Keywords: coral recruits, coral adult size-class, cora demography, resilience

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Authors: Mariza Kaskara, Stella Girtsou, Maria Prodromou, Alexia Tsouni, Christodoulos Mettas, Stavroula Alatza, Kyriaki Fotiou, Marios Tzouvaras, Charalampos Kontoes, Diofantos Hadjimitsis

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Natural hazard assessment and monitoring are crucial in managing the risks associated with fires, floods, and geohazards, particularly in regions prone to these natural disasters, such as Greece and Cyprus. Recent advancements in technology, developed by the BEYOND Center of Excellence of the National Observatory of Athens, have been successfully applied in Greece and are now set to be transferred to Cyprus. The implementation of these advanced technologies in Greece has significantly improved the country's ability to respond to these natural hazards. For wildfire risk assessment, a scalar wildfire occurrence risk index is created based on the predictions of machine learning models. Predicting fire danger is crucial for the sustainable management of forest fires as it provides essential information for designing effective prevention measures and facilitating response planning for potential fire incidents. A reliable forecast of fire danger is a key component of integrated forest fire management and is heavily influenced by various factors that affect fire ignition and spread. The fire risk model is validated by the sensitivity and specificity metric. For flood risk assessment, a multi-faceted approach is employed, including the application of remote sensing techniques, the collection and processing of data from the most recent population and building census, technical studies and field visits, as well as hydrological and hydraulic simulations. All input data are used to create precise flood hazard maps according to various flooding scenarios, detailed flood vulnerability and flood exposure maps, which will finally produce the flood risk map. Critical points are identified, and mitigation measures are proposed for the worst-case scenario, namely, refuge areas are defined, and escape routes are designed. Flood risk maps can assist in raising awareness and save lives. Validation is carried out through historical flood events using remote sensing data and records from the civil protection authorities. For geohazards monitoring (e.g., landslides, subsidence), Synthetic Aperture Radar (SAR) and optical satellite imagery are combined with geomorphological and meteorological data and other landslide/ground deformation contributing factors. To monitor critical infrastructures, including dams, advanced InSAR methodologies are used for identifying surface movements through time. Monitoring these hazards provides valuable information for understanding processes and could lead to early warning systems to protect people and infrastructure. Validation is carried out through both geotechnical expert evaluations and visual inspections. The success of these systems in Greece has paved the way for their transfer to Cyprus to enhance Cyprus's capabilities in natural hazard assessment and monitoring. This transfer is being made through capacity building activities, fostering continuous collaboration between Greek and Cypriot experts. Apart from the knowledge transfer, small demonstration actions are implemented to showcase the effectiveness of these technologies in real-world scenarios. In conclusion, the transfer of advanced natural hazard assessment technologies from Greece to Cyprus represents a significant step forward in enhancing the region's resilience to disasters. EXCELSIOR project funds knowledge exchange, demonstration actions and capacity-building activities and is committed to empower Cyprus with the tools and expertise to effectively manage and mitigate the risks associated with these natural hazards. Acknowledgement:Authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project.

Keywords: earth observation, monitoring, natural hazards, remote sensing

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Authors: Jocelyn Doucet, Jean-Philippe Laviolette, Ali Eslami

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The end-of-life management and recycling of polymer wastes remains a key environment issue in on-going efforts to increase resource efficiency and attaining GHG emission reduction targets. Half of all the plastics ever produced were made in the last 13 years, and only about 16% of that plastic waste is collected for recycling, while 25% is incinerated, 40% is landfilled, and 19% is unmanaged and leaks in the environment and waterways. In addition to the plastic collection issue, the UN recently published a report on chemicals in plastics, which adds another layer of challenge when integrating recycled content containing toxic products into new products. To tackle these important issues, innovative solutions are required. Chemical recycling of plastics provides new complementary alternatives to the current recycled plastic market by converting waste material into a high value chemical commodity that can be reintegrated in a variety of applications, making the total market size of the output – virgin-like, high value products - larger than the market size of the input – plastic waste. Access to high-quality feedstock also remains a major obstacle, primarily due to material contamination issues. Pyrowave approaches this challenge with its innovative nano-recycling technology, which purifies polymers at the molecular level, removing undesirable contaminants and restoring the resin to its virgin state without having to depolymerise it. This breakthrough approach expands the range of plastics that can be effectively recycled, including mixed plastics with various contaminants such as lead, inorganic pigments, and flame retardants. The technology allows yields below 100ppm, and purity can be adjusted to an infinitesimal level depending on the customer's specifications. The separation of the polymer and contaminants in Pyrowave's nano-recycling process offers the unique ability to customize the solution on targeted additives and contaminants to be removed based on the difference in molecular size. This precise control enables the attainment of a final polymer purity equivalent to virgin resin. The patented process involves dissolving the contaminated material using a specially formulated solvent, purifying the mixture at the molecular level, and subsequently extracting the solvent to yield a purified polymer resin that can directly be reintegrated in new products without further treatment. Notably, this technology offers simplicity, effectiveness, and flexibility while minimizing environmental impact and preserving valuable resources in the manufacturing circuit. Pyrowave has successfully applied this nano-recycling technology to decontaminate polymers and supply purified, high-quality recycled plastics to critical industries, including food-contact compliance. The technology is low-carbon, electrified, and provides 100% traceable resins with properties identical to those of virgin resins. Additionally, the issue of low recycling rates and the limited market for traditionally hard-to-recycle plastic waste has fueled the need for new complementary alternatives. Chemical recycling, such as Pyrowave's microwave depolymerization, presents a sustainable and efficient solution by converting plastic waste into high-value commodities. By employing microwave catalytic depolymerization, Pyrowave enables a truly circular economy of plastics, particularly in treating polystyrene waste to produce virgin-like styrene monomers. This revolutionary approach boasts low energy consumption, high yields, and a reduced carbon footprint. Pyrowave offers a portfolio of sustainable, low-carbon, electric solutions to give plastic waste a second life and paves the way to the new circular economy of plastics. Here, particularly for polystyrene, we show that styrene monomer yields from Pyrowave’s polystyrene microwave depolymerization reactor is 2,2 to 1,5 times higher than that of the thermal conventional pyrolysis. In addition, we provide a detailed understanding of the microwave assisted depolymerization via analyzing the effects of microwave power, pyrolysis time, microwave receptor and temperature on the styrene product yields. Furthermore, we investigate life cycle environmental impact assessment of microwave assisted pyrolysis of polystyrene in commercial-scale production. Finally, it is worth pointing out that Pyrowave is able to treat several tons of polystyrene to produce virgin styrene monomers and manage waste/contaminated polymeric materials as well in a truly circular economy.

Keywords: nanorecycling, nanomaterials, plastic recycling, depolymerization

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Authors: Mina Naeini, Thomas A. Adams II

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Solid Oxide Fuel Cells (SOFCs) feature high electrical efficiency and generate substantial amounts of waste heat that make them suitable for integrated community energy systems (ICEs). By harvesting and distributing the waste heat through hot water pipelines, SOFCs can meet thermal demand of the communities. Therefore, they can replace traditional gas boilers and reduce greenhouse gas (GHG) emissions. Despite these advantages of SOFCs over competing power generation units, this technology has not been successfully commercialized in large-scale to replace traditional generators in ICEs. One reason is that SOFC performance deteriorates over long-term operation, which makes it difficult to find the proper sizing of the cells for a particular ICE system. In order to find the optimal sizing and operating conditions of SOFCs in a community, a proper knowledge of degradation mechanisms and effects of operating conditions on SOFCs long-time performance is required. The simplified SOFC models that exist in the current literature usually do not provide realistic results since they usually underestimate rate of performance drop by making too many assumptions or generalizations. In addition, some of these models have been obtained from experimental data by curve-fitting methods. Although these models are valid for the range of operating conditions in which experiments were conducted, they cannot be generalized to other conditions and so have limited use for most ICEs. In the present study, a general, detailed degradation-based model is proposed that predicts the performance of conventional SOFCs over a long period of time at different operating conditions. Conventional SOFCs are composed of Yttria Stabilized Zirconia (YSZ) as electrolyte, Ni-cermet anodes, and LaSr₁₋ₓMnₓO₃ (LSM) cathodes. The following degradation processes are considered in this model: oxidation and coarsening of nickel particles in the Ni-cermet anodes, changes in the pore radius in anode, electrolyte, and anode electrical conductivity degradation, and sulfur poisoning of the anode compartment. This model helps decision makers discover the optimal sizing and operation of the cells for a stable, efficient performance with the fewest assumptions. It is suitable for a wide variety of applications. Sulfur contamination of the anode compartment is an important cause of performance drop in cells supplied with hydrocarbon-based fuel sources. H₂S, which is often added to hydrocarbon fuels as an odorant, can diminish catalytic behavior of Ni-based anodes by lowering their electrochemical activity and hydrocarbon conversion properties. Therefore, the existing models in the literature for H₂-supplied SOFCs cannot be applied to hydrocarbon-fueled SOFCs as they only account for the electrochemical activity reduction. A regression model is developed in the current work for sulfur contamination of the SOFCs fed with hydrocarbon fuel sources. The model is developed as a function of current density and H₂S concentration in the fuel. To the best of authors' knowledge, it is the first model that accounts for impact of current density on sulfur poisoning of cells supplied with hydrocarbon-based fuels. Proposed model has wide validity over a range of parameters and is consistent across multiple studies by different independent groups. Simulations using the degradation-based model illustrated that SOFCs voltage drops significantly in the first 1500 hours of operation. After that, cells exhibit a slower degradation rate. The present analysis allowed us to discover the reason for various degradation rate values reported in literature for conventional SOFCs. In fact, the reason why literature reports very different degradation rates, is that literature is inconsistent in definition of how degradation rate is calculated. In the literature, the degradation rate has been calculated as the slope of voltage versus time plot with the unit of voltage drop percentage per 1000 hours operation. Due to the nonlinear profile of voltage over time, degradation rate magnitude depends on the magnitude of time steps selected to calculate the curve's slope. To avoid this issue, instantaneous rate of performance drop is used in the present work. According to a sensitivity analysis, the current density has the highest impact on degradation rate compared to other operating factors, while temperature and hydrogen partial pressure affect SOFCs performance less. The findings demonstrated that a cell running at lower current density performs better in long-term in terms of total average energy delivered per year, even though initially it generates less power than if it had a higher current density. This is because of the dominant and devastating impact of large current densities on the long-term performance of SOFCs, as explained by the model.

Keywords: degradation rate, long-term performance, optimal operation, solid oxide fuel cells, SOFCs

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