Energies, Volume 16, Issue 16 (August-2 2023) – 248 articles

The aviation industry is a significant contributor to global carbon dioxide emissions, with over 920 million tonnes per year, and there is a growing need to reduce its environmental impact. The production of biojet fuel from renewable biomass feedstocks presents a promising solution to address this challenge, with the potential to reduce greenhouse gas emissions and dependence on fossil fuels in the aviation sector. This review provides an in-depth discussion of current and emerging biojet fuel conversion technologies, their feasibility, and their sustainability, focusing on the promising conversion pathways: lipids-to-jet, sugar-to-jet, gas-to-jet, alcohol-to-jet, and whole biomass-to-jet. Each technology is discussed in terms of its associated feedstocks, important chemistries, and processing steps, with focus on recent innovations to improve yields of biojet product at the required specifications. In addition, the emerging power-to-liquid technology is briefly introduced. With the integrated biorefinery approach, consideration is given to biomass pretreatment to obtain specific feedstocks for the specific technology to obtain the final product, with the embedded environmental sustainability requirements. In addition, the review highlights the challenges associated with the biojet production technologies, with embedded suggestions of future research directions to advance the development of this important and fast-growing sustainable fuel industry. Full article

Based on the magnetic gear effect, the field-modulated permanent-magnet machine (FMPMM) can realize the unequal pole design of the rotor PM field and the stator armature magnetic field. With the advantages of high torque density and high efficiency, the FMPMM has been widely studied in low-speed direct-drive applications. As a kind of machine excited by PMs, the performance of the FMPMM was affected by the demagnetization state. However, the method for establishing the FMPMM demagnetization model based on a finite element analysis (FEA) presented some problems, including tedious repeated modeling work and long calculation time-consuming under fine subdivision. Therefore, in this paper, a six-phase surface-mounted FMPMM was taken as an example, and an equivalent magnetic network (EMN) model was proposed for evaluating the machine performance under demagnetization. In order to realize the rapid establishing EMN models under diverse demagnetization types, the variable coercivity of PM was introduced. Furthermore, for the purpose of improving the calculation accuracy and shortening the calculation time, the least square method was used in fitting and analyzing the discrete results. Then, in order to verify the validity of the proposed EMN model, a prototype was fabricated and a testing platform was built. The air-gap flux density and the no-load back EMF obtained by the FEA, the proposed EMN model, and the experimental testing were compared. The results showed that the proposed EMN model can realize the rapid modeling and accurate analysis of the six-phase surface-mounted FMPMM under diverse demagnetization types. Full article

This paper offers a set of comprehensive guidelines aimed at facilitating the widespread adoption of hydrogen in the industrial hard-to-abate sectors. The authors begin by conducting a detailed analysis of these sectors, providing an overview of their unique characteristics and challenges. This paper delves into specific elements related to hydrogen technologies, shedding light on their potential applications, and discussing feasible implementation strategies. By exploring the strengths and limitations of each technology, this paper offers valuable insights into its suitability for specific applications. Finally, through a specific analysis focused on the steel sector, the authors provide in-depth information on the potential benefits and challenges associated with hydrogen adoption in this context. By emphasizing the steel sector as a focal point, the authors contribute to a more nuanced understanding of hydrogen’s role in decarbonizing industrial processes and inspire further exploration of its applications in other challenging sectors. Full article

The production of sustainable, biomass-based synthetic natural gas (SNG) and Fischer–Tropsch (FT) diesel can contribute significantly to climate neutrality. This work aims to determine the commercial-scale production costs and CO₂ footprint of biomass-based SNG and FT diesel to find suitable integration scenarios for both products in the Austrian energy system. Based on the simulation results, either 65 MW SNG and 14.2 MW district heat, or 36.6 MW FT diesel, 17.6 MW FT naphtha, and 22.8 MW district heat can be produced from 100 MW biomass. The production costs with taxes for wood-based SNG are 70–91 EUR /MWh and for FT diesel they are 1.31–1.89 EUR /L, depending on whether pre-crisis or crisis times are considered, which are in the range of fossil market prices. The CO₂ footprint of both products is 90% lower than that of their fossil counterparts. Finally, suitable integration scenarios for SNG and FT diesel in the Austrian energy system were determined. For SNG, use within the energy sector for covering electricity peak loads or use in the industry sector for providing high-temperature heat were identified as the most promising scenarios. In the case of FT diesel, its use in the heavy-duty traffic sector seems most suitable. Full article

Due to their substantial energy density and economical pricing, switching-mode power supplies (SMPSs) often utilize electrolytic capacitors. However, their ability to function at low temperatures is essential for dependable operation in several sectors, including telecommunications, automotive, and aerospace. This study includes an experimental evaluation of how well standard SMPS electrolytic capacitors operate at low temperatures. This paper investigates the suitability of standard electrolytic capacitors used in switched-mode power supplies (SMPSs) for low-temperature applications. The experimental evaluation exposed the capacitors to temperatures ranging from −5 °C to −40 °C, assessing capacitance (Cp), impedance (Z), dissipation factor (DF), and equivalent series resistance (ESR) at each temperature. The capacitor’s time-domain electrical signals were analyzed using the Pearson correlation coefficient to extract discriminative features. These features were input into an artificial neural network (ANN) for training and testing. The results indicated a significant impact of low temperatures on capacitor performance. Capacitance decreased with lower temperatures, while the ESR and leakage current increased, affecting stability and efficiency. Impedance was a valuable diagnostic tool for identifying potential capacitor failure, showing a 98.44% accuracy drop at −5 °C and 88.75% at the peak temperature, indicating proximity to the manufacturer’s specified limit. The study suggests further research and development to improve the performance of electrolytic capacitors in SMPS systems under cold conditions, aiming to boost efficiency and reliability. Full article

This article proposes a Distributed-control Architecture (D-CA) and an operation sequence with start-up strategies for a Digital Signal Processor (DSP)-based Solid-State Transformer (SST). Although various control techniques for SSTs have been reported in earlier studies, there is still a lack of research covering comprehensive content, including hierarchical control architectures and operation sequences with start-ups considering the implementation of DSPs. Therefore, this article addresses the following factors of SST. First, the D-CA is described for the design of the hierarchy between control boards. With the D-CA, because sub-boards are in charge of their corresponding DC-link voltage balancing control individually, the computational burden on the master board can be reduced. Second, the operation sequence of the SST system is explained based on the SST with D-CA. The step of DC-link voltage balance is considered throughout the entire operation sequence for safe driving. Furthermore, the PWM start-up strategies for a Cascade H-bridge Multilevel (CHM) converter and Dual Active Bridge (DAB) converter are proposed to prevent switching pulse errors caused by DSP operating characteristics. These start-up strategies reduce the current surges. The validity of the proposed D-CA and operation sequence with start-up strategies are verified by experimental results. Full article

The economic downturn caused by the financial crisis of 2008–2009 and the intensifying global climate policy trends forced changes in the energy management of the European Union. The study examined how the most relevant energy economic variables affected the economic growth of the E.U. between 2010–2019. The study used the PSL-PM methodology to explore the relationship between G.D.P. (dependent variable) and energy consumption, greenhouse gas emissions, the average energy price, and renewable energy use (independent variables). The main findings are: G.D.P. growth is negatively correlated with CO₂ emissions, showing that the E.U. economy is still highly dependent on fossil fuels; the increase in the proportion of renewable energy consumption contributed to the growth of the E.U.’s G.D.P.; CO₂ emissions, energy consumption, and the average energy price are more critical in E.U. member states with a lower G.D.P.; renewable energy use and energy balance are essential in countries where more emphasis is placed on replacing traditional energy sources and reducing energy dependence; there is a strong positive correlation between G.D.P. and renewable energy use, indicating that this type of energy use effectively supports E.U. economic growth. The results of the multicollinearity test show that there is also a strong linear dependence between the independent energy economic variables. One of the significances of the study is that the presented and analyzed variables and the relationships between them can contribute to optimizing the E.U.’s currently critical energy management and economic growth. Full article

As the urgency for environmental sustainability escalates globally, the exploration of alternative fuels for diesel engines becomes a crucial endeavor. By combining chemical reaction kinetics and three-dimensional simulation software, the combustion and emission characteristics of a diesel engine fueled with two oxygenated fuels, hydrogenated biodiesel and ethanol, and adopting a multi-stage injection strategy were studied. The combustion mechanism of hydrogenated biodiesel ethanol diesel hybrid fuel was established, and the reaction activity of ester alcohol diesel with different mixing ratios was studied through reaction flow analysis at high and low OH temperatures. The established mechanism was coupled with CFD 2021 three-dimensional simulation software to compare the combustion and emission performance of diesel engines fueled with different ratios of ester alcohol diesel. The results show that as the proportion of ester alcohol mixture increases, at low temperatures, the OH generation rate decreases, the consumption rate increases, and the reaction activity decreases, which is not conducive to the promotion of combustion reaction; at high temperatures, the generation rate of OH increases, the consumption rate decreases, and the reaction activity increases, which is conducive to the promotion of combustion reactions. Compared to diesel, the reaction system activity of mixed fuel is enhanced, and the main peak values of cylinder pressure and instantaneous heat release rate are higher than that of diesel. The diffusion of oil and gas in the cylinder is improved. As the proportion of ester alcohol diesel mixture increases, the oxygen content increases, nitrogen oxides emissions increase compared to diesel, and soot emissions decrease compared to diesel. Soot emissions are mainly distributed in areas with a high equivalence ratio and high temperature, which is consistent with the distribution area of C₂H₂, the precursor of soot generation. Full article

The marine ranching industry in China is transitioning from traditional farming to a digital and intelligent model. The use of new technologies, algorithms, and models in the era of artificial intelligence (AI) is a key focus to enhance the efficiency, sustainability, and resilience of marine ranch operations, particularly in risk and disaster management. This study proposes a methodology for applying deep reinforcement learning to decision making in this domain. The approach involves creating an environmental model based on decision objects and scenarios, determining the number of decision makers, and selecting a single or multi-agent reinforcement learning algorithm to optimize decision making in response to randomly generated disasters. Three core innovations are presented: the development of a disaster simulator for marine ranching scenarios, the application of reinforcement learning algorithms to address risk and disaster management problems in marine ranching. Future research could focus on further refining the methodology by integrating different data sources and sensors and evaluating the social and economic impacts of AI-driven marine ranching. Overall, this study provides a foundation for further research in this area, which is expected to play an increasingly important role in global food production, environmental sustainability, and energy efficiency. Full article

The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources. Cleaning scheduling is one of the activities that can positively impact performance. This work proposes a methodological framework to define the optimal scheduling of the cleaning activities of photovoltaic systems. The framework integrates a forecast model of the performance ratio, including the environmental variables’ effect. In addition, an economic analysis involving the economic losses and maintenance costs of cleaning is used. This framework is applied to a case study of a photovoltaic system located in Yumbo, Colombia. Based on the historical data on irradiance, active energy, temperature, rainfall, and wind speed, the obtained forecast model of the photovoltaic system’s performance ratio in a 60-day horizon has a mean absolute percentage error lesser of than 11%. The next cleaning date is forecasted to be beyond the horizon in a 19-day range, which will decrease as time goes by. This framework was applied to historical data and compared to actual cleaning dates performed by the utility company. The results show a loss of USD 33.616 due to unnecessary, early, or late cleaning activities. Full article

Nowadays, integrating renewable energy sources, such as tidal power, into the existing power grids of turbines is crucial for sustainable energy generation. However, tidal turbine energy transforms the potential energy of moving water into electrical energy. When both nonlinear load and dynamic load harmonics are present, the tide speed variance causes serious power quality issues such as low power factor, unstable voltage, harmonic distortions, frequency fluctuations, and voltage sags. The integration of an LCL-filter-based connection scheme can address these challenges by improving power quality and the overall performance of the tidal current turbine grid system. This study shifts LCL filter research from its conventional wind energy emphasis to the emerging field of tidal stream generation systems. The LCL filter analysed in this paper is modelled to exhibit adequate mechanical, electrical, and hydrodynamic characteristics. This model accounts for tidal current variations, turbine speed control, and power extraction dynamics. The LCL filter is evaluated for its effectiveness in reducing harmonic distortions, voltage fluctuations, and reactive power fluctuations. This system is composed of a 1.5 MW/C, a 1.2 MW three-level inverter with a nominal voltage of 600 V, and an inductance (LCL) filter. The results show that the inverter produces a harmonic distortion of less than 0.5%, which demonstrates the effectiveness of the filter in improving total harmonic distortion, reactive power consumption, and voltage control. Full article

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio’s revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework. Full article

The constantly growing demand for energy, the need to ensure the security of its supply, and the progressing climate changes related to the emission of carbon dioxide and other pollutants have caused, in recent years, an increase in interest in offshore wind energy. This paper presents all the work that needs to be done to build a wind farm in the Baltic Sea. The work focuses on the description of the equipment and the necessary tests to perform in order to analyze the obtained data. The data will allow for unambiguous interpretation and the selection of a convenient location for the construction of a wind farm. The final product of the work is a cost estimate, in which the costs of undertaking such an undertaking are shown. Full article

Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO${}_2$, SO${}_2$, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO${}_2$ and SO${}_2$ concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution. Full article

One of the most effective technologies for recycling organic waste is its thermal destruction by pyrolysis methods to produce valuable products such as hydrogen and mixtures containing hydrogen. Increasing the thermal power of the flow helps to reduce the formation of secondary reactions, making the non-condensable hydrocarbon gas in the pyrolysis process cleaner, which simplifies further technology for the production of hydrogen and hydrogen-containing mixtures. In addition, the economic viability of pyrolysis depends on the energy costs required to decompose the organic feedstock. Using passive intensifiers in the form of discrete rough surfaces in heat exchanging channels is a widely used method of increasing heat transfer. This paper presents the results of numerical and experimental studies of heat transfer and hydraulic resistance in a channel with and without hemispherical protrusions applied to the heat transfer surface. The investigations were carried out for a reactor channel 150 mm long and 31 mm in diameter, with a constant pitch of the protrusions along the channels of 20 mm and protrusion heights h of 1 to 4 mm for 419 ≤ Re ≤ 2795. Compared to a smooth channel, a channel with protrusions increases heat transfer by an average of 2.23 times. By comparing the heat exchange parameters and the hydraulic resistance of the heat exchange channels, it was determined that h = 2 mm and 838 < Re < 1223 is the combination of parameters providing the best energetic mode of reactor operation. In general, an increase in h and coolant flow rate resulted in an uneven increase in heat transfer intensity. However, as h increases, the dead zone effect behind the protrusions increases and the rough channel working area decreases. Furthermore, increasing Re > 1223 is not advisable due to the increased cost of maintaining high coolant velocity and the reduced heat transfer capacity of the channel. Full article

Deep coalbed methane (CBM, commonly accepted as >1500 m) has enormous exploration and development potential, whereas the commercial development of deep CBM exploration areas wordwide has been quite limited. The Linxing area, with coals buried approximately 2000 m deep, shows great development potential. Based on a basic geological analysis of structural and hydrodynamic conditions, combining field tests of reservoir temperature and pressure and indoor measurements of maceral composition, proximate analysis, thermal maturity, porosity and permeability, the factors controlling deep CBM accumulations were discussed. The results show that the present burial depth of the No. 8 + 9 coal seam, mainly between 1698 and 2158 m, exhibits a high reservoir temperature (45.0–64.0 °C) and pressure (15.6–18.8 MPa), except for the uplift area caused by the Zijinshan magma event (with coal depth approximately 1000 m). The maximum vitrinite reflectance (R_o,max) of the coal varies from 1.06% to 1.47%, while the magma-influenced areas reach 3.58% with a relatively high ash content of 31.3% (air-dry basis). The gas content calculated by field desorption tests shows a wide range from 7.18 to 21.64 m³/t. The key factors controlling methane accumulation are concluded from regional geological condition variations. The north area is mainly controlled by structural conditions and the high gas content area located in the syncline zones. The center area is dominated by the Zijinshan magma, with relatively high thermal maturity and a high gas content of as much as 14.5 m³/t. The south area is developed with gentle structural variations, and the gas content is mainly influenced by the regional faults. Furthermore, the groundwater activity in the eastern section is stronger than that in the west, and the hydrodynamic stagnant areas in the western are more beneficial for gas accumulation. The coals vary from 3.35% to 6.50% in porosity and 0.08 to 5.70 mD in permeability; thus, hydrofracturing considering high temperature and pressure should be applied carefully in future reservoir engineering, and the co-production of gas from adjacent tight sandstones also should be evaluated. Full article

Complex oil and gas two-phase flow exists within an aero-engines bearing cavity scavenge pipe, prone to lubricated self-ignition and coking. Lubricant system designers must be able to accurately identify and understand the flow state of the scavenge pipe. The prediction accuracy of previous models is insufficient to meet the more demanding needs. This paper establishes a visualized flow pattern identification test system for the scavenge pipe, with a test temperature of up to 370 k, using a high-speed camera to photograph four flow patterns, decomposing the pressure signals obtained from high-frequency dynamic pressure sensors using the ensemble empirical mode decomposition (EEMD) method, and then performing Hilbert transform, using the Hilbert spectrum to quantify the changes of amplitude and frequency with time, and establishing the energy and flow pattern correspondence analysis. Then the energy percentage of IMFs is used as the input of feature values, and the random forest algorithm machine learning is used for predictive classification. The experimental results show that the flow pattern recognition rate established in this paper can reach 98%, which can identify the two-phase flow pattern in the scavenge pipe more objectively and accurately. Full article

Electromobility represents a strong option for reducing carbon emissions in the road transport sector. This study presents a methodology and a simulation tool that project the evolution of the market share of electric vehicles (EVs) in the new car market. The analysis adopts a stylized regional resolution, which accounts for attributes on the NUTS-2 level, such as the population density, GDP/capita, education levels, and current EV charger distribution, to simulate the uptake of BEVs in different regions. The methodology applies discrete choice modelling techniques, considering tangible and intangible factors, including purchasing and operation costs, an estimated cost for range anxiety and public charging, and a market maturity index. The analysis is based on four different scenarios, referring to the updated Greek National Energy Climate Plan. The results reveal that regions with a higher average income, GDP/capita, and population density show a higher uptake of EVs. Overall, the tool implements a method of simulating the market evolution of EVs up to 2030 in reference to regional parameters and, hence, highlights the regions that require the most attention in order to achieve national targets. The results can inform policymakers in developing tailored strategies and financial support to accelerate the adoption of BEVs, particularly in regions where their uptake prospects are lower. Full article

Over the last decade, Nitrogen Oxide (NOx) emissions have garnered significantly greater attention due to the worldwide emphasis on sustainable development strategies. In response to the issues of dynamic measurement delay and low measurement accuracy in the NOx sensors of heavy-duty diesel vehicles, a novel Multilayer Perceptron (MLP)–Random Forest Regression (RFR) fusion algorithm was proposed and explored in this research. The algorithm could help perform post-correction processing on the measurement results of diesel vehicle NOx sensors, thereby improving the reliability of the measurement results. The results show that the measurement errors of the On-board Nitrogen oxide Sensors (OBNS) were reduced significantly after the MLP-RFR fusion algorithm was corrected. Within the concentration range of 0–90 ppm, the absolute measurement error of the sensor was reduced to ±4 ppm, representing a decrease of 73.3%. Within the 91–1000 ppm concentration range, the relative measurement error was optimised from 35% to 17%, providing a reliable solution to improve the accuracy of the OBNS. The findings of this research make a substantial contribution towards enhancing the efficacy of the remote monitoring of emissions from heavy-duty diesel vehicles. Full article

In this study, the cold-start process of a polymer electrolyte fuel cell has been numerically investigated under various ambient temperatures and operating currents, ranging from subzero to 283 K. The water desorbed from the electrolyte, when the cell temperature is below the freezing point, is assumed to exist in a state of either supercooled water or ice. The evolution of cell voltage, temperature, membrane water content, and the averaged volume fraction of supercooled water or ice in the catalyst layer and gas diffusion layer are presented. The results indicate that the cold-start process may fail due to ice blocking of the cathode catalyst layer when the desorbed water is in the form of ice and the ambient temperature is sufficiently low. However, when the desorbed water is in a supercooled state, it can diffuse from the cathode catalyst layer to the cathode gas diffusion layer, avoiding water clogging and enabling a successful cold-start process. During the cold-start process, as the ice undergoes a melting process, the membrane water content inside the membrane would increase rapidly, and a larger operation current with anode gas humidification is helpful to the cold-start process. Full article

This study presents theoretical and practical contributions to the environmental dimension of enterprises’ corporate social responsibility (CSR) in sustainable development. Interest in the environment is related to CSR through environmental cost optimization and energy-efficiency management. The practical stage of the research, obtained using the computer-assisted telephone interviewing (CATI) method, allowed for presenting case studies of the best practices used by international enterprises operating in Poland. This study describes the practical tools and advice companies can use to improve efficiency and environmental responsibility. The article is an in-depth study of the growing role of enterprises in shaping sustainable and socially responsible businesses and aims to assess the extent to which these companies prioritize energy efficiency as a part of their CSR initiatives. The authors highlight the role of energy efficiency in achieving broader corporate environmental responsibility. This research aims to encourage businesses to adopt responsible environmental strategies for a greener and more sustainable future. The implementation of this goal helped develop and indicate conclusions regarding the development of environmental tools related to corporate responsibility in sustainable development, encouraging scientific debates and promoting responsible monitoring of the implementation of this concept. Full article

In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications. Full article

Within this research, we studied the structural–morphological and electrochemical characteristics of the PtNi/C catalysts synthesized via the two-stage sequential reduction of precursors. We also carried out a comparative study of the obtained bimetallic catalysts and their commercial Pt/C analog. The use of triethylamine as a surfactant as well as the acid treatment as an additional synthesis stage, were shown to have a positive effect on the functional parameters of the bimetallic electrocatalysts. The resulting PtNi/C electrocatalyst demonstrates a mass activity value of 389 A g_Pt⁻¹, which is 1.6 times higher than this parameter for a commercial analog. Full article

Drives in belt conveyors are critical components of the conveyor system, susceptible to various factors that can cause disruptions and energy losses. In underground mining conditions, the risk of drive fires is particularly hazardous. Therefore, it is necessary to develop highly effective fire suppression systems. However, there are no guidelines for designing such systems. This study presents a methodology for selecting and verifying the fire suppression systems for belt conveyor drives. The proposed AMIGA system for extinguishing fires on underground coal mine conveyor belts, incorporating spraying and water mist installations, is supported by a theoretical calculation methodology. This enables determining the number of required nozzles and flow rate for complete fire suppression. The development of a methodology for the selection and verification of the sprinkler system components utilized guidelines provided in the standard VdS 2109:2002-03 and the PN-EN 12845+A2 standard from 2010, while a novel approach is proposed for water mist parameters that has not been previously applied anywhere else, and is based on assessing the fire’s intensity and the persistent disruption of the energy balance of the combusted coal. The theoretical calculations for potential fire power facilitate the determination of the appropriate water flow rate for the spraying system to protect the upper belt drive. For the proposed AMIGA system, the potential fire power was calculated to be $10.33\mathrm{MJ}/\min$. Based on this, the water flow rate for the spraying installation to protect the upper drive belt of the conveyor was established to be a minimum $37.5{\mathrm{dm}}^3/\min$, and $21.4{\mathrm{dm}}^3/\min$ for the mist installation used to protect the space below the conveyor drive. In order to verify the developed methodology for parameter selection, on-site tests were conducted to verify the results. Tests were conducted on an AMIGA prototype suppression system integrated into a conveyor drive. The results demonstrate that the developed system is effective in extinguishing fires on the belt using the spraying installation, as well as under the conveyor belt drive using the water mist installation, within the entire supply pressure range ( $0.4\mathrm{MPa}$ to $1.6\mathrm{MPa}$ ). Full article

Organic solar cells offer benefits such as transparent characteristics, affordability in manufacturing, and the ability to tailor light absorption properties according to specific needs. This review discusses challenges and recent strategies to enhance the power conversion efficiency of organic solar cells, such as bandgap tuning, molecular orbital alignment, active layer morphology engineering, electron-donating and -withdrawing group incorporation, side chain length engineering, a third additive’s insertion, and control of the solubility of materials. The good transparency of organic solar cells makes them ideal for greenhouse-integrated photovoltaics applications. By efficiently absorbing sunlight for photosynthesis and clean energy production, transparent organic solar cells optimize light management, enhance energy efficiency, and minimize overheating risks, resulting in more sustainable and efficient greenhouse operations. This review also evaluates organic solar cell integration in the greenhouse. The implementation of the strategies explored in this review can significantly impact a wide range of performance parameters in organic solar cells. These parameters include the optoelectronic properties, absorption spectrum, open circuit voltage, exciton dissociation, charge transport, molecular packing, solubility, phase separation, crystallinity, nanoscale morphology, and device stability. An optimized organic solar cell design is particularly beneficial for greenhouse-integrated photovoltaics, as it ensures efficient energy conversion and energy management, which are crucial factors in maximizing the performance of the greenhouse. Full article

Society faces challenges in achieving a climate-neutral society due to deepening energy inequality. The pandemic led to reduced emissions but also caused an economic downturn. Geopolitical tensions since 2022 raised energy prices, affecting affordability. To address these issues, this research aims to conduct a systematic literature review to explore the content, conceptualization, and distinguishing factors of energy inequality compared to similar concepts as well as to identify energy inequality dimensions and its indicators and explore ways to reduce it. A systematic literature review explored recent publications on energy inequality from 2019 to 2023, encompassing both pre-pandemic and pandemic-affected periods. This review analyzed 203 articles, with 61 of them directly focusing on energy inequality indicators. This research is conducted in several stages. Firstly, this article clarifies the concept of energy inequality and highlights its differences from related terms. Secondly, this study investigates the effects of energy inequality taking into account its diverse dimensions, and it categorizes these dimensions and their respective indicators based on their specific contexts. Thirdly, recommendations are provided for potential approaches to reduce energy inequality. The methodology integrates an examination of macroeconomic energy inequality statistics. The resulting findings hold the potential to significantly contribute towards cultivating a more environmentally conscious trajectory. Moreover, these outcomes play a pivotal role in advancing energy justice and effectively tackling the multifaceted challenges posed by energy inequality. Full article

When attempting to quantify future harms caused by carbon emissions and to set appropriate energy policies, it has been argued that the most important metric is the number of human deaths caused by climate change. Several studies have attempted to overcome the uncertainties associated with such forecasting. In this article, approaches to estimating future human death tolls from climate change relevant at any scale or location are compared and synthesized, and implications for energy policy are considered. Several studies are consistent with the “1000-ton rule,” according to which a future person is killed every time 1000 tons of fossil carbon are burned (order-of-magnitude estimate). If warming reaches or exceeds 2 °C this century, mainly richer humans will be responsible for killing roughly 1 billion mainly poorer humans through anthropogenic global warming, which is comparable with involuntary or negligent manslaughter. On this basis, relatively aggressive energy policies are summarized that would enable immediate and substantive decreases in carbon emissions. The limitations to such calculations are outlined and future work is recommended to accelerate the decarbonization of the global economy while minimizing the number of sacrificed human lives. Full article

Considering the current limitations and restricted scope of existing experiments, as well as the absence of corresponding numerical simulation verifications and comparisons, and the lack of actual case studies of variable mass flow calculation and comparison, this study focuses on high production oilfields in the Mideast and South China Sea. The objective is to investigate single-phase and multiphase variable mass flow through numerical and experimental simulations. The study develops linear regression equations to establish the relationship between the mixture pressure drop caused by side flow and the velocities of the main flow, as well as the ratio between side and main flow velocities. Actual calculations using these equations are provided. The comprehensive analysis reveals that, for a fixed total flow rate, an increase in the side versus main injection velocity ratio leads to an increase in pressure loss before and after the injection hole. In single-phase flow, the friction factor for side hole flow is generally higher than that for only axial main flow, with the same total flow rate. In multiphase flow, when the gas-liquid ratio (GLR) is relatively large, the side flow has minimal impact on pressure drop, while at lower GLR values, the side flow significantly increases the pressure drops. When predicting the pressure drop for single-phase variable mass flow in horizontal wellbores, it is appropriate to consider only the mixture pressure drop caused by the closest hole to the calculation section, assuming the injection hole flow rates are approximately equal. In terms of predicting the productivity of single-phase variable mass flow, it is crucial to consider the mixture pressure drop. Neglecting the mixture pressure drop can lead to relatively larger productivity prediction results, with potential production rate errors exceeding 50%. The accuracy of the prediction is influenced by the ratio of mixture pressure drop to production pressure differential, and the pressure along the external zone of the screen pipe is higher when considering the mixture pressure drop compared to when it is neglected. Additionally, the flow rate along the external zone of the screen pipe becomes more non-uniform when the mixture pressure drop is considered. Furthermore, the findings from the single-phase and multiphase flow experiments suggest that significant deviations in production rates may occur in scenarios with low gas-liquid ratio (GLR), highlighting the need for further investigation in this area. Full article

Part I of this series of publications addressed the background and fundamentals of the lifetime assessment of prototype Francis turbines. Part II concentrated on the developed methods of numerical calculation and assessment procedures. The present contribution (Part III) deals with the instrumentation and the metrological range of the assessment procedure. The most important sensors, measurement tools, and data acquisition units are presented (background). The instrumentation of the prototype Francis turbine is used, on the one hand, for machine unit monitoring and plant operating and, on the other hand, for generating measurement data to validate and adjust/correct the numerical simulations. Measurement data form the basis for further evaluations at various levels. A wide variety of measured variables are required to carry out the remaining lifetime of a component using fatigue analysis. Those variables include pressure and acceleration signals, vibration monitoring, and strain gauge applications for mechanical stress analysis. The available measurement signals are divided into groups based on the developed method. Thus, already-available data from the control room are compared with machine monitoring and temporarily measured data. The correlation of all available data is essential today to determine an exact idea of the occurring flow phenomena and their effects on the mechanical stresses on the component. This interaction of the different data sources and, subsequently, the use of selected quantities for the numerical calculation are part of the newly developed concept for fatigue strength analysis of mechanical components of a turbine unit (methods). The results of this journal article are divided into the discussion of the necessary instrumentation and mounting of the sensors and into the evaluation, presentation, and interpretation of the measurement data. In addition, a fatigue strength assessment is made at the position of the strain gauges. These results serve as a basis for validating the numerical stress calculation. It is worth mentioning that the validation of the numerical results and the discussion of the deviations and error consideration is carried out in Part IV of this publication series (results). This journal article of the series on condition assessment of prototype Francis turbines ends with a discussion of the results and conclusions for further data processing (conclusion). Full article

The reliability of power converters in photovoltaic systems is critical to the overall system reliability. This paper proposes a novel active thermal-controlled algorithm that aims to reduce the rate of junction temperature increase, therefore, increasing the reliability of the device. The algorithm works alongside a normal perturb and observe maximum power point tracking algorithm, taking control when certain temperature criteria are met. In conjunction with a neural network, the algorithm is applied to long-term real mission profile data. This would grant a better understanding of the real-world trade-offs between energy generated and lifetime improvement when using the proposed algorithm, as well as shortening study cycle times. The neural network, when applied to 365 days of data, was 28 times faster than using standard electrothermal modeling, and the lifetime consumption was predicted with greater than 96.5% accuracy. Energy generated was predicted with greater than 99.5% accuracy. The proposed algorithm resulted in a 3.3% reduction in lifetime consumption with a 1.0% reduction in the total energy generated. There is a demonstrated trade-off between lifetime consumption reduction and energy-generated reduction. The results are also split by environmental conditions. Under very variable conditions, the algorithm resulted in a 4.4% reduction in lifetime consumption with a 1.4% reduction in the total energy generated. Full article