Efficient System Research Articles

Study on the influence of high permeability magnetic iron on energy consumption reduction in high gradient magnetic separator

The green processing of weakly magnetic iron ore is inseparable from high-intensity and high-gradient magnetic separator (HGMS), which is one of the most energy-intensive magnetic separation equipment. Iron (Fe) armor serves as the key primary structure for enhancing magnetic field conduction efficiency within the magnetic system. The order of magnetic properties of commonly used magnetic yoke materials is: ordinary carbon structural steel < Q235 steel < DT4 electrical pure iron. But the utilization cost of DT4 electrical pure iron is the highest, especially the magnetic properties of DT4C super electrical pure iron are unstable and the yield is low. On the basis of ordinary pure iron materials, this article has developed a new type of 411 pure iron (411) upon controlling the levels of harmful elements, while increasing the concentration of beneficial elements.intensity Furthermore, through the utilization of magnetic field simulation software, the impact of 411 on the background magnetic field intensity of HGMS was analyzed. The results indicated that the newly developed 411 pure Fe exhibited a coercivity of 24.07 A/m, a saturation magnetization intensity of ∼ 1.69 T, a maximum relative magnetic permeability of 24.38 × 10-3H/m, demonstrating superior magnetic properties in comparison with Q235 and DT4C. When using 411 pure iron as the magnetic yoke material, the background magnetic field intensity of the HGMS can be increased by 6.85 % − 14.64 % compared with using DT4C super pure iron under the same excitation current conditions. The magnetic yoke with better magnetic conductivity will enhance the magnetic field utilization efficiency of the magnetic system, a reduce the excitation energy consumption, and improve the sorting performance of HGMS.

Enhancing gas drainage and ventilation efficiency in underground coal mines: A hybrid expert decision approach for booster fan prioritization

Expanding mining operations in goaf zones heightens gas production potential, posing challenges in maintaining adequate ventilation within development panels, consequently impacting coal production. Various strategies have been explored to enhance mine ventilation and gas drainage effectiveness. However, deficiencies persist in the proposed ventilation system for the Okaba underground coal mine, prompting this study’s necessity. Addressing these concerns, the study evaluates the feasibility of employing booster fans to mitigate the identified drawbacks. Prioritizing booster fans for airflow distribution in underground mines is a complex decision-making process, requiring an advanced expert system approach. To address this, the study proposes an intuitionistic-based fuzzy TOPSIS (IFT) method for booster fan prioritization in the Okaba mine. Results indicate that booster fan 4 (BF4) ranks highest, followed by booster fan 3 (BF3), consistent with fuzzy TOPSIS findings. Sensitivity analysis supports the predicted importance order, affirming the efficacy of the hybrid expert decision method in selecting a booster fan capable of enhancing the overall efficiency of gas drainage and ventilation systems in underground mines. This study introduces a Hybrid Expert Decision Approach that integrates IFT and traditional fuzzy TOPSIS methodologies. This hybrid approach is particularly novel because it combines the strengths of both methods to prioritize booster fans in underground coal mines.

Development of agrivoltaic insect net house to enhance sustainable energy-food production: a techno-economic assessment

Agrivoltaics (AV) systems are the simultaneous land utilization for sustainable energy and food production. However, AV may not be suitable for some crops that require rigorous pest management. This study has applied an innovative approach, combining AV with an insect net house to cultivate capsicum. The 1.8 kW AV system has been installed at a 3-m elevation to facilitate unrestricted tractor manoeuvrability for various farming operations. The structure can withstand winds as high as 140.4 km/h. The system has a 3,612-kWh energy capacity and a 51.41 kg food production capacity. The system's energy and food conversion efficiency are 14.09% and 154.8%, respectively, compared to the traditional systems. Economic metrics like a 2.20 benefit-cost ratio and a 9.39-year payback period have been included in the findings. Further, the land equivalence ratio is 1.97, indicating a better system than traditional AV and agriculture approaches.

Performance and economic analysis of various geothermal power plant systems: Case study for the Chingshui geothermal field in Taiwan

Geothermal power plants are a source of sustainable energy. However, their performance is influenced by various factors, including production temperature and well locations, and few studies have investigated the relationships between these factors. To address this research gap, the present study developed integrated models of a geothermal reservoir and a variety of power plant systems. By using COMSOL and MATLAB, we conducted simulations for the Chingshui geothermal field in Taiwan to investigate the performance and economic feasibility of five common types of geothermal power plant systems over their operational lifetime under different well configurations. The key findings of this investigation are as follows. First, the locations of production wells are a primary factor influencing production temperature. Constructing wells in low-permeability zones results in considerable increases in the pressure differential in the injection pump; thus, wells should not be constructed in such zones. Second, as system flow rate and well depth decrease, production temperature remains relatively stable, net power output decreases, and system efficiency increases. This efficiency increase is caused by decreases in pumping power requirements at lower flow rates and shallower well depths. Finally, among the investigated systems, the organic Rankine cycle system had the lowest electricity production cost and shortest payback period within the enthalpy range of 500–1000 kJ/kg. Double-flash (DF) and DF–binary systems are also viable options when the mass flow rate is 10–20 kg/s and the enthalpy is 700–800 kJ/kg. Overall, the results of this study offer valuable insights for the design of geothermal power plants, thereby contributing to the optimization of renewable energy systems.

Optimizing urban flood management: enhancing urban drainage system efficiency under extreme rainfall events

ABSTRACT Extreme rainfall events, particularly those induced by tropical cyclones, pose a heightened risk to the urban drainage system (UDS). Existing UDSs, having been established long ago, often fail to account for the extreme rainfall caused by cyclones. To address this issue, this study designs a multi-objective intelligent scheduling model within a simulation -optimization framework, aiming to optimize the operation of urban drainage infrastructure and hydraulic structures. This is achieved by integrating the Storm Water Management Model (SWMM) with the multi-objective particle swarm optimization algorithm (MOPSO) and distinctly evaluating typhoons and torrential rains for their impact on extreme rainfall. The study results show that the multi-objective intelligent scheduling model can effectively devise operation strategies for pumping stations and weirs in the study area, thereby optimizing their use for urban drainage. The model was successful in reducing the total flood volume (TFV) and the water level fluctuation (WLF) by 3.11%–57.77% and 26.32%–65.48%, respectively. This not only mitigates urban flooding but also enhances the infrastructure stability of the UDS. The model outperformed the local adaptation strategy in most scenarios for the two selected objectives, suggesting that the efficiency can be significantly improved by optimizing UDSs without expansion of existing infrastructure or additional costs.

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Science and Engineering of Superaerophobic Surfaces for Electrochemical Gas—Evolving Reactions: A Review of Recent Advances and Perspective

AbstractIn energy conversion processes and various industries, gas evolution reactions (GERs) play an important role. To achieve a future without fossil fuels, the development of high‐efficiency electrocatalysts is necessary, as they directly affect the catalytic performance and overall efficiency of reactions. In addition to the discovery of highly active catalysts, the rapid removal of gaseous products on the electrode surface is equally important for GERs. The adherence of bubbles to the electrode surface introduces substantial resistance, significantly diminishing the system's efficiency. One promising solution to reduce the adhesion of bubbles is the development of electrocatalysts with superaerophobic levels. These surface structures, such as nanotubes, nanosheets, and nanowires, prevent gas bubbles from adhering and promote their rapid removal from the electrode. The aim of this review is first to obtain a deep understanding of mechanisms related to the creation of superaerophobic surfaces, including their characteristics, methods of creation, and bubble detachment behavior. Furthermore, recent advances in the application of these surfaces in various gas‐evolving reactions to enhance electrocatalytic properties are discussed. By taking this innovative approach, valuable insights can be gained into advancing the field of electrocatalysis and driving progress toward sustainable energy solutions.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Sustainable drag reduction in Taylor-Couette flow using riblet superhydrophobic surfaces

Superhydrophobic surfaces (SHSs) have been proven effective in reducing frictional drag force in various flow conditions. However, at high flow speeds, the air plastron on these surfaces collapses, leading to a decline in their effectiveness. In this study, we investigated the frictional drag forces of various SHSs and their combination with surface patterns across a wide range of flow conditions (5.00×102 < Re < 1.12×105) by using an facile coating method. Our experiments involved incorporating a superhydrophobic coating on the inner cylinder of a custom-made Taylor-Couette apparatus, integrated with a rheometer to measure torque applied on the inner rotor as a function of rotational speed. As part of our research, we calculate the effective slip length to assess the drag reduction performance of coatings, revealing an effective slip length of around 63 µm on a flat SHS. Furthermore, we explore the combined effect of superhydrophobic coatings and triangular-shaped riblets on drag reduction in Taylor-Couette flow, comparing the performance of these surfaces based on the riblet’s sharpness and the Reynolds number. Our experimental results show a reduction in measured torque of up to 24 % and 48 % on a V-grooved SHS in laminar and turbulent flow, respectively. Longevity tests confirm that the designed surfaces maintain their superhydrophobicity and drag reduction performance under turbulent flow conditions. Overall, this work introduces a passive drag reduction strategy through surface design, which substantially mitigates the frictional drag force and demonstrating considerable potential for enhanced performance and increased efficiency of Taylor-Couette systems.

Mitochondrial DNA Alterations in Glioblastoma and Current Therapeutic Targets.

Metabolic reprogramming within tumor cells involves a shift towards either glycolysis or mitochondrial respiration, depending on the stage of tumor progression. Consequently, irreversible dysfunction of the mitochondria is considered a crucial mechanism driving the progression mechanism. While numerous mutations in mitochondrial DNA (mtDNA) have been identified across various tumor types, including glioblastoma, many studies have been limited in the scope, focusing on small segments of mtDNA or utilizing sequencing methods with restricted sensitivity. As a result, several potentially significant mtDNA mutations may have been underestimated, along with their heteroplasmic states, which play a crucial role in determining the phenotypic impact of mtDNA mutation. Although both somatic and germline mtDNA mutations have been observed in different tumor types, research on the mtDNA mutations linked to glioblastoma remains scarce. The mitochondrial genome encodes thirteen protein-coding genes that are essential for the proper functioning of respiratory complex chains. Alterations in mitochondrial function manifest at various levels, including structural and functional changes, impacting mitogenic, hemodynamic, bioenergetic, and apoptotic signaling pathways. These alterations often signify a reduced efficiency of the oxidative phosphorylation system and energy production in tumor cells. As the crucial role of mitochondrial dysfunction in glioma development grows, mitochondria have emerged as promising targets for therapy aimed at overcoming chemoresistance and eliminating cancer cells. This brief review outlines the association between mtDNA alteration and glioblastoma, as well as the current advancements in therapeutic strategies targeting mtDNA alterations.

Optimizing Nitrogen Input Increased Yield and Efficiency in Maize-Soybean Strip Intercropping System

Optimizing nitrogen (N) fertilizer combination is a crucial measure to maximize yield and production efficiency in a maize-soybean strip intercropping system (MSSI). In this field experiment, six maize/soybean N input combinations (0 kg ha−1, F0; 255/30 kg ha−1, F1; 255/45 kg ha−1, F2; 255/60 kg ha−1, F3; 210/45 kg ha−1, F4; 300/45 kg ha−1, F5) were set in 2022 and 2023. The results indicated that optimizing N combination (maize/soybean, 255/45 kg ha−1) could synergistically increase yield and economic benefits. Path analysis results showed that the grain numbers in maize and soybean emerged as the most critical yield-affecting factors. Compared with F0, F5 showed the highest grain yield during the 2-year experiment, and the net return increased by 86.1% (F1), 133.3% (F2), 87.4% (F3), 104.7% (F4), and 128.3% (F5), respectively. Optimizing N input under F2 and F5 notably enhanced the leaf area index (LAI) of maize at the milk stage (R3) and soybean at the full pod stage (R4). Additionally, optimization of N distribution in maize stems at the tassel stage (VT) and soybean leaves at the initial flowering stage (R1) facilitated increased dry matter and N accumulation at the maturity stage, resulting in final land equivalent ratios (LER) of 1.44 and 1.55, respectively. Our results provide a more valuable field N combination for summer maize planting areas (sandy soil areas) in Huang-Huai-Hai and southern China, thus promoting the wider application of MSSI.

Current paths of tax for business entities: Financial, economic and legal aspects

The article aims to investigate the ways of optimizing taxation for business entities in Ukraine to enhance the efficiency of the domestic taxation system and analyze the economic and legal aspects of establishing a new model of relations between businesses and fiscal authorities in alignment with European tax standards. The authors examine the economic and legal dimensions of current trends in tax optimization for profits generated from business activities, value-added goods and services, transactions involving excisable goods, as well as taxation on various forms of income, property, and property rights of business entities. Legislative improvements related to taxation and financial mechanisms for determining and collecting specific taxes, such as income tax and a single tax, are discussed. This study addresses the pressing issues surrounding legislative provisions on business entity taxation and proposes methods for enhancing tax optimization strategies within each entity. The authors emphasize the significance of developing a tailored tax policy for businesses to manage financial stability, guide economic activities, and ensure compliance with tax obligations. The authors present applicable recommendations for optimizing taxation practices and enhancing financial mechanisms for tax assessment and collection within business entities. Their insights shed light on the importance of strategic tax policy development to navigate tax responsibilities and plan for future financial obligations effectively. This research provides practical recommendations for policymakers and business leaders to improve tax optimization strategies, ensuring that businesses can operate efficiently while fulfilling their tax obligations in a manner that aligns with European standards.

Transforming fire protection: AI's evolving role in proactive and reactive applications

Abstract. This paper comprehensively reviews the applications of artificial intelligence (AI) in the field of fire protection. It traces the historical development of AI, from its origins in the 1930s and 1940s to its current state, focusing on major milestones and algorithms that have shaped the field. The paper discusses the evolution of AI, particularly in terms of machine learning, deep learning, and the emergence of various models such as deep neural networks (DNNs), transformer models, and graph neural networks (GNNs). The application of AI in fire protection is explored in two primary categories: proactive and reactive applications. Proactive applications encompass AI's role in decision-making, construction, and operation, including the use of machine learning for environmental analysis, building information modelling (BIM) for design optimization, 3D printing and intelligent robots in construction, and intelligent fire facility management. Reactive applications focus on early fire detection and monitoring, especially in forests, utilizing terrestrial, aerial, and satellite systems equipped with advanced sensors and deep learning techniques. This paper highlights the significant contributions of AI in mitigating fire risks, improving fire detection and response, and enhancing the safety and efficiency of fire protection systems.

Solar-enhanced DCMD: dual-tank system for superior efficiency and viability

ABSTRACT This study investigates a novel solar-powered direct contact membrane distillation (DCMD) system designed to significantly enhance desalination efficiency and economic feasibility. The system features a dual-tank configuration integrated with a solar collector, which optimizes thermal energy storage and ensures consistent feedwater temperatures. Therefore, continuous operation is possible despite fluctuations in solar radiation. An assessment of the system's performance under various solar radiation conditions predicted an average daily permeate flux of 14.4 kg/m2 and a specific energy consumption of 1810 kJ/kg, indicating superior thermal efficiency. The economic analysis revealed a competitive levelized water cost of $25.75 per cubic meter, highlighting the system's potential as a viable alternative to conventional desalination technologies. These findings suggest that the proposed DCMD system is a leading technology in the pursuit of sustainable desalination. Considering its thermal management and cost-effectiveness, the system presents a transformative solution for sustainable freshwater production in regions with abundant solar resources.

Characterization of Enterobacter cloacae CAUCoF_BF_01: an autochthonous heterotrophic nitrifying strain from culture ponds for biofloc applications

The biofloc environment needs specific heterotrophic nitrifying bacteria for optimal system performance, hence screening perfect bacterial strains is crucial. Enterobacter cloacae sp. was identified as a novel heterotrophic bacterium isolated from freshwater pond sediment during July, 2022. Through high-throughput sequencing, the isolated bacterium was identified as E. cloacae CAUCoF_BF_01, which demonstrated potential nitrogen removal via heterotrophic nitrification and aerobic denitrification, as well as a preference for different C/N ratios (C/N10, 15, 20, and 25) for consuming nitrogen sources. The bacteria performed superior (p < 0.05) ammonia-N and nitrite-N removal in C/N20, with efficiencies of 91.70% and 91.0%, respectively. C/N15, on the other hand, had higher nitrate-N removal efficiency (69.20%). Furthermore, the optimum temperature for the chosen strain was determined to be 30 °C. This strain, on the other hand, can thrive in temperatures ranging from 20 to 37 °C and pH levels ranging from 6 to 10. Furthermore, the biofilm test reveals that the selected bacterial strain has the potential to form biofilm via extracellular polymeric compound secretion, besides passing ecological safety requirement. The current investigation will be helpful in the development of a successful biofloc-based probiotic for managing system generated ammonia to an optimal level, as well as floc production to maximize system efficiency.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Novel strategies for enhancing energy metabolism and wastewater treatment in algae-bacteria symbiotic system through carbon dots-induced photogenerated electrons: The definitive role of accelerated electron transport

The nitrogen removal efficiency of the algae-bacteria symbiotic system (ABSS) remains challenging due to competition for light capture and insufficient electron donors in aquaculture wastewater. To overcome this bottleneck issue, we first identified and reported the introduction of photogenerated electrons into ABSS by coupling them with carbon dots (CDs). The energy metabolism (21.52 %-56.25 %) and pollutant degradation efficiency (89.36 %-96.42 %) of ABSS/CDs surpassed the biochemical limit observed in traditional ABSS. Notably, the addtion of CDs facilitated an opening of the plastoquinone-pool (PQ-pool) valve and accelerated electron transfer rates, enabling photogenerated electrons to selectively target light-harvesting complexs as activation sites, thereby mobilizing photosynthesis and respiratory system activity to enhance nicotinamide adenine dinucleotide reduced (NADH) and adenosine triphosphate (ATP) production while promoting tricarboxylic acid (TCA) and Calvin cycle processes. In simulated swine wastewater treatment, ABSS/CDs achieved higher removal rates for the chemical oxygen demand (COD), total nitrogen (TN) and ammonium-nitrogen (NH4+-N) (100 %, 99.23 % and 98.62 % respectively) compared to pure culture systems alone. Network-based analysis revealed that ABSS/CDs improved microbial community structure stability within the photobioreactor while demonstrating efficient coupling between fungi, nitrifying bacteria, and denitrifying bacteria for enhanced performance in nitrogen removal processes. The upregulation of nitrogen metabolism and PQ-pool genes maintains efficient electron transport rates and contributes to nitrogen removal. This study expands our understanding of the physiological aptitudes of ABSS and provides valuable insights into applying CDs photoelectrons for enhanced wastewater treatment.

Advanced Venturini control for PMSM with modular multilevel matrix converter

This paper provides a comprehensive analysis of control strategies for modular multilevel matrix converters (MMMC) utilizing advanced second-order sliding mode control (ASOSMC) within the context of permanent magnet synchronous motor (PMSM) applications. The research introduces a novel control framework based on the Venturini method, which, when integrated with ASOSMC, achieves a unity input power factor while generating sinusoidal phase current waveforms with markedly reduced harmonic distortion. These advancements not only enhance overall system performance but also significantly improve power quality, addressing critical requirements in contemporary electrical engineering. Additionally, the study conducts a rigorous comparative analysis between classical Proportional Integral (PI) control and ASOSMC, elucidating the substantial limitations of PI control in nonlinear environments where dynamic adaptability is paramount. In contrast, ASOSMC demonstrates superior capability in managing disturbances and nonlinearities, thereby offering enhanced reliability and efficiency for MMMC systems. The findings illustrate that ASOSMC not only addresses the challenges posed by traditional control techniques but also contributes to the development of more robust control methodologies within power electronics. By establishing a clear superiority of ASOSMC over conventional methods, this research paves the way for future innovations in control strategies specifically tailored for high-performance applications that require resilience under varying operational conditions. Ultimately, this work underscores the necessity of integrating advanced control techniques to optimize the operational capabilities of modular multilevel converters, facilitating the creation of sophisticated solutions that meet the evolving demands of electrical systems and power quality enhancement in diverse applications. The implications of these findings are significant, offering foundational insights for both theoretical exploration and practical implementation in the field.

Uncovering pathway and mechanism of simultaneous thiocyanate detoxicity and nitrate removal through anammox and denitrification

Thiocyanate (SCN−) exists in various industries and is detrimental to the ecosystem, necessitating cost-effective and environmentally benign treatment. In response to alleviate the bacterial toxicity of SCN−, this study developed a two-stage coupled system by tandem of anammox in reactor 1 (R1) and SCN−-driven autotrophic denitrification in reactor 2 (R2), achieving simultaneous removal of SCN− and nitrogen. The total nitrogen removal efficiency of the coupled system was 92.42 ± 1.98%, with nearly 100% of SCN− elimination. Thiobacillus was responsible for SCN− degradation. The deduced degradation pathway of SCN− was via the cyanate pathway before coupling, followed by the co-action of cyanate pathway and carbonyl sulfide pathway after coupling. Although scaling-up study is needed to validate its applicability in real-world applications, this study contributes to the advancement of sustainable and cost-effective wastewater treatment technologies, being an attractive path for low-carbon nitrogen removal and greenhouse gas emission-free technology.

Robust Control of Electrical Machines in Renewable Energy Systems: Challenges and Solutions

The increasing global demand for energy driven by population growth necessitates a shift towards renewable energy sources to address sustainability and environmental concerns. Renewable energy sources and how they function in tandem with electrical equipment to generate power are covered extensively in this study. Renewable sources like solar-thermal, hydro, wind, wave, tidal, geothermal, and biomass-thermal utilize electric machines to convert various forms of stored energy into electricity. The paper classifies these machines based on their design and application in renewable systems, highlighting the most common types, including DC, induction, and synchronous machines. It also evaluates their performance according to efficiency, power density, and cost-effectiveness. A primary focus is on the challenges associated with controlling electrical machines in renewable energy systems, including issues related to intermittency, grid integration, nonlinear dynamics, fault tolerance, harmonics, efficiency optimization, thermal management, scalability, real-time control, and cost constraints. The paper concludes by discussing the need for advanced control strategies and solutions to address these challenges, aiming to enhance a reliability, efficiency, and performance of renewable energy systems.