Efficient System Research Articles

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.

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

La-Fe-Si magnetocaloric composite with anisotropic microstructure prepared by hot pressing

In this study, we have proposed an anisotropic microstructure in La-Fe-Si magnetocaloric composites for thermal management. This is vital to the rapid heat exchange and high working efficiency in magnetic refrigerator system. We demonstrate the anisotropic microstructure in composites can be fabricated via powder metallurgy in this work. By adjusting the particle size of the 1:13 phase, the introduced reinforcing phase with rheological property can effectively deform and anisotropic microstructure can be obviously observed. This can be attributed to the tailored stress distribution in cubic-anvil-type pressure apparatus. The element diffusion between the two phases was also discussed in this paper, taking into account the influence of particle size. Such anisotropic microstructure causes some other anisotropic physical properties in the composite, such as mechanical strength. The compressive measurements along the axial direction exhibit superior mechanical properties (~ 3.5% for strain and ~ 350 MPa for strength) compared to those along the radial direction. Attributed to the stress buffer-effect provided by the introduced ductile phase, magnetocaloric effect (magnetic entropy change ~ 7.8 J/kg K) can be maintained in the La-Fe-Si composite.

A data-driven regression model for predicting thermal plant performance under load fluctuations

The global energy demand is still primarily reliant on fossil-fueled thermal power plants. With the growing share of renewables, these plants must frequently adjust their loads. Maintaining, or ideally increasing operational efficiency under these conditions is crucial. Increasing the efficiency of such systems directly reduces associated greenhouse gas emissions, but it requires sophisticated models and monitoring systems. Data-driven models have proven their value here, as they can be used for monitoring, operational state estimation, and prediction. However, they are also sensitive to (1) the training approach, (2) the selected feature set, (3) and the algorithm used. Using operational data, we comprehensively investigate these model parameters for performance prediction in a thermal plant for process steam generation. Specifically, four regression algorithms are evaluated for the prediction of the highly fluctuating live steam flow with two training approaches and three feature subsets of the raw dataset. Furthermore, manual and automatic clustering methods are used to identify different states of operation regarding the fuel amounts used in the combustion chamber. Our results show that the live steam flow is predicted with excellent accuracy for a testing period of one month (R2=0.994 and NMAE=0.55%) when using a dynamic training approach and a comprehensive feature set comprised of 48 features representing the combustion process. It is also seen that the statically trained model predicts various load changes with strong accuracy and that the accuracy of the dynamically trained model can be approached by incorporating the cluster information into the static model. These models reflect the plant’s physical intricacies under varying loads, where deviations from the predicted live steam flow indicate unwanted long-term drifts. They can be directly implemented to help operators detect inefficiencies and optimize plant performance.

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

“Chacco” clay from the Peruvian highlands as a potential adsorbent of heavy metals in water

This research aimed to remove Cd (II), Cr (VI), Ni (II), Pb (II), and V (V) from aqueous solutions prepared in distilled water using “Chacco” clay from the Peruvian highlands as adsorbent. The adsorption process was carried out in Batch type systems for 120 min using solutions of each metal at a concentration of 5 mg/L in aqueous systems of a single metal or monometallic (MAS) and solutions of the five metals simultaneously or multimetallic (MMAS). For this purpose, the “Chacco” clay was first characterized by SEM-EDS analysis, finding a laminated clay structure with an elemental composition of C, Al, Fe, Na, Mg, Ca, K, As, Cu, Pd, O, and Ta. The results using 10 g/L of “Chacco” clay showed that the best adsorption efficiency in both MAS and MMAS aqueous systems is achieved at pH = 4 achieving in MAS aqueous systems the removal of 64.16 ± 0.98 % of Cd (II), 95.70 ± 0.81 % of Cr (VI), 97.20 ± 0.89 % of Ni (II), 92. 78 ± 0.79 % of Pb (II), and 95.80 ± 0.67 % of V (V), on the other hand, in aqueous MMAS systems a decrease in adsorption efficiency was observed, managing to remove 6.88 ± 0.53 % of Cd (II), 63.04 ± 0.94 of Cr (VI), 7.81 ± 0.43 % of Ni (II), 62.34 ± 0.77 % Pb (II), and 14.33 ± 0.56 % of V (V). The kinetic study showed that the adsorption mechanism would correspond to chemisorption since the process fitted best to the pseudo-second order model and Elovich. SEM-EDS analysis after adsorption confirmed the presence of the heavy metals under study in the “Chacco” clay. Metal adsorption is evidenced at 1418 cm−1 by -CH2-metal deformation vibrations according to FTIR analysis. In conclusion, the “Chacco” clay would be a promising adsorbent of heavy metals in polluted waters so that scaling up to real environments could be feasible. On the other hand, the “Chacco” clay is consumed by the population of Puno, Peru, therefore its potential impact on health should be evaluated due to its capacity to accumulate metals and the presence of Al in this clay.

Performance Evaluation of Hybrid Propulsion System on Fast Patrol Boat

This paper presents a performance evaluation of a hybrid propulsion system applied to a fast patrol boat. The study aims to analyze the efficiency, fuel consumption, and overall operational effectiveness of the hybrid system compared to conventional propulsion methods. A combination of electric motors and diesel engines is utilized in the hybrid setup, allowing for optimized energy use in varying operational conditions. The performance of the system was evaluated through simulation and sea trials, focusing on key metrics such as speed, endurance, maneuverability, and emission reduction. Results indicate that the hybrid system offers significant advantages in terms of fuel savings and reduced environmental impact, particularly during low-speed or patrol operations. This research provides valuable insights into the practical benefits and challenges of implementing hybrid propulsion systems in maritime security operations.

The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems

ABSTRACT Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Thermodynamic evaluation of a Ca-Cu looping post-combustion CO2 capture system integrated with thermochemical recuperation based on steam methane reforming

The calcium looping integrated with the chemical looping combustion (CaL-CLC) process is an efficient and cost-effective CO2 capture technology that avoids the energy-intensive air separation unit in the calcium looping (CaL) process. However, in these CaL-CLC and CaL system integration schemes, the carbonation heat is utilized for steam generation, resulting a significant temperature difference and considerable irreversible loss. To prevent temperature mismatch, this paper proposes a novel Ca-Cu looping post-combustion CO2 capture method with thermochemical recuperation based on steam methane reforming. Additionally, a novel system integrated with turbine exhaust heat recovery is introduced to effectively reduce carbon emissions from flue gas. Results show that the proposed system has superior performance compared to the reference system based on the Ca-Cu looping method. The specific primary energy consumption for CO2 avoidance decreased from 2.40 MJLHV/kg CO2 in the reference system to 2.02 MJLHV/kg CO2. Exergy analysis indicates that a total of 3.0 % reduction in exergy destruction can be achieved in chemical reaction processes and heat recovery processes, contributing to the superior performance of the proposed system. Furthermore, the effects of key operating parameters indicate that cascaded turbine exhaust recovery is essential for improving the thermodynamic efficiency of the proposed system. Overall, recovering the mid-temperature carbonation heat via thermochemical regeneration and integrating with exhaust heat recovery contribute to reducing SPECCA, thus providing a promising low-energy-consumption alternative for CO2 capture.

A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition

Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized collision sensors across various scenes. Recent progress in neuroscience has revealed the energetic advantages of dendritic arrangements presynaptic to the LGMDs, which receive contrast polarity-specific signals on separate dendritic fields. Specifically, feed-forward inhibitory inputs arise from parallel ON/OFF pathways interacting with excitation. However, none of the previous research has investigated the evolution of a computational LGMD model with feed-forward inhibition (FFI) separated by opposite polarity. This study fills this vacancy by presenting an optimized neuronal model where FFI is divided into ON/OFF channels, each with distinct synaptic connections. To align with the energy efficiency of biological systems, we introduce an activation function associated with neural computation of FFI and interactions between local excitation and lateral inhibition within ON/OFF channels, ignoring non-active signal processing. This approach significantly improves the time efficiency of the LGMD model, focusing only on substantial luminance changes in image streams. The proposed neuronal model not only accelerates visual processing in relatively stationary scenes but also maintains robust selectivity to ON/OFF-contrast looming stimuli. Additionally, it can suppress translational motion to a moderate extent. Comparative testing with state-of-the-art based on ON/OFF channels was conducted systematically using a range of visual stimuli, including indoor structured and complex outdoor scenes. The results demonstrated significant time savings in silico while retaining original collision selectivity. Furthermore, the optimized model was implemented in the embedded vision system of a micro-mobile robot, achieving the highest success ratio of collision avoidance at 97.51% while nearly halving the processing time compared with previous models. This highlights a robust and parsimonious collision-sensing mode that effectively addresses real-world challenges.

The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

Estimation of Hydraulic and Water Quality Parameters Using Long Short-Term Memory in Water Distribution Systems

Predicting essential water quality parameters, such as discharge, pressure, turbidity, temperature, conductivity, residual chlorine, and pH, is crucial for ensuring the safety and efficiency of water supply systems. This study employs long short-term memory (LSTM) networks to address the challenge of capturing temporal dependencies in these complex processes. Our approach, using a robust LSTM-based model, has demonstrated significant predictive accuracy, as evidenced by substantial R-squared values (e.g., 0.86 for discharge and 0.97 for conductivity). These models have proven particularly effective in handling non-linear patterns and time-series data, which are prevalent in water quality metrics. The results indicate the potential for LSTMs not only to enhance the real-time monitoring of water systems but also to aid in the strategic planning and management of water supply systems. This study’s findings can serve as a basis for further research into the integration of AI in environmental engineering, particularly for predictive tasks in complex, dynamic systems.

Design, development and test of single beamlet microwave ion source with spot-dense plasma system

In this paper, an efficient ion source with unique characteristics is introduced and proposed. It outlines the generation of a spot-dense plasma just below the plasma electrode aperture of the extractor system through the utilization of a 2.45 GHz microwave plasma source with a power of 1000 W, in the absence of a magnetic field. This source utilizes a 2.45 GHz microwave plasma generator operating at 1000 W, resulting in the generation of a spot-dense plasma just below the plasma electrode aperture of the extractor system, all achieved without the need for a magnetic field.The spot-dense plasma results in the extraction of an ion beam with high current density. Furthermore, the suggested ion source can be utilized in a smaller size using a higher frequency. Therefore, our findings indicate that the proposed method not only substantially enhances the extracted positive ion beam current but also significantly reduces the size of the ion beam source system. Based on the design and simulation results, an experimental model of the ion beam source is constructed. The diagnostic tools provided the beam current density and system efficiency values of 99mA/cm2 and 7×10−6A/W, respectively.

Numerical Investigation of Solar Collector Performance with Encapsulated PCM: A Transient, 3D Approach

This study presents a comprehensive numerical investigation into the thermal performance of solar collectors integrated with encapsulated phase change materials (PCMs) using a transient three-dimensional (3D) approach. The performance of two distinct PCMs—paraffin wax and RT60—was evaluated under varying operational conditions, including seasonal variations, inlet pipe velocities, and inlet temperatures. The results indicate that paraffin wax exhibits a higher peak temperature, reaching approximately 360 K, compared to RT60’s peak of 345 K, making paraffin wax more effective for consistent thermal energy storage. Paraffin wax also maintained higher fluid fractions, with a maximum of 0.9 in summer, indicating superior heat absorption and retention capabilities. In contrast, RT60 demonstrated a quicker phase transition, fully liquefying at a lower fluid fraction, which is advantageous for rapid heat release. Seasonal variations significantly impacted system efficiency, with the highest efficiency observed in June at 365 K and the lowest in December at 340 K. The study also found that lower inlet velocities (e.g., 0.25 L/s) significantly improved heat retention, resulting in higher outlet temperatures, while increasing the inlet temperature from 290 K to 310 K led to a marked increase in outlet temperatures throughout the day. These findings underscore the importance of optimizing PCM selection, inlet velocity, and temperature in enhancing the performance of solar thermal systems, offering quantitative insights that contribute to the development of more efficient and reliable renewable energy solutions.

The Role of Digital Health Under Taiwan’s National Health Insurance System: Progress and Challenges

ABSTRACT Digital health covers a wide spectrum of applications of digital technologies in the healthcare field. As a new set of tools to support the health system in achieving its goals—improving access to care, quality of care, and system efficiency—digital health has significantly transformed the landscape of modern medicine and health care. This paper examines the role of digital health under Taiwan National Health Insurance, considering the profound impacts of digital health during the COVID-19 pandemic. It focuses specifically on big data management and analytics (MediCloud and My Health Bank/NHI Mobile Easy Access) and innovative service provision models (telemedicine). We discuss two imminent challenges that any health system is likely to encounter: digital trust and digital divide. For the digital divide, we assessed the use of telemedicine and its determinants during the COVID-19 pandemic. Our study shows that high-income levels and the presence of chronic or severe illness were positively correlated with the use of telemedicine. This observation suggests that poor people who have poorer health status were most likely to suffer from unmet needs for telemedicine. Enhancing cybersecurity to safeguard confidentiality, and effective communications with the public are fundamental and essential steps to regaining public trust in the digital era. When calling for more investment in digital technology, policy makers should be mindful of the potential digital divide across the demographic and socioeconomic strata, and specific policies should be devised to provide support to target the socially disadvantaged group.

Research on Blockchain Interactive Zero Knowledge Proof Privacy Protection Scheme Based on Improved Paillier Homomorphic Encryption

In the context of the digital age, data privacy and security issues are increasingly prominent. Blockchain technology plays an important role in data sharing due to its transparency and immutability, but it also brings the risk of privacy leakage. Zero knowledge proof technology provides a solution for verifying data correctness without exposing data content, which is particularly important for blockchain as it can ensure the validity and compliance of transactions while protecting user privacy. Although zero knowledge proof is quite mature in theory, its application in blockchain systems still faces challenges such as computational efficiency, complexity of smart contracts, and system compatibility. This study aims to propose a privacy protection scheme that supports interactive zero knowledge proof by improving the homomorphic encryption Paillier algorithm, in order to enhance the privacy protection capability of blockchain systems and maintain system efficiency and security. The study will adopt an interdisciplinary approach, combining cryptography, computer science, and network security theory, to deeply analyze the application effect of zero knowledge proof technology in blockchain, explore its optimization space and applicability.

New hybrid system of PV/T, solar collectors, PEM electrolyzer, and HDH for hydrogen and freshwater production: Seasonal performance investigation

A new hybrid energy system of PV/T, flat plate collector, PEM electrolyzer, and humidification-dehumidification desalination is introduced here. A comprehensive mathematical model for the system seasonal performance is constructed and solved numerically using MATLAB software. A year-round comparison between the traditional PV/T performance and its concentrated counterpart indicates that the latter performs better compared to power, hydrogen, and freshwater production and system overall efficiency. The maximum monthly achieved PV/T efficiency, electrolyzer efficiency, desalination system Gain Output Ratio, and overall system efficiency are 52 %, 62 %, 4.8 %, and 44 %, respectively at a concentration ratio of 4 and maximum allowable PV temperature. The system produces 82,200 kWh of energy annually and provides 77,700 kWh and 1500 kWh to the electrolyzer and the electric heater, respectively. Moreover, the system produces an annual freshwater production of about 52,700 kg, out of which approximately 10,000 kg are consumed in the electrolysis process, and an annual hydrogen production of approximately 1120 kg.

Optimal Control of FSBB Converter with Aquila Optimizer-Based PID Controller

This paper presents a new methodology for determining the optimal coefficients of a PID controller for a four-switch buck–boost (FSBB) converter. The main objective of this research is to improve the performance of FSBB converters by fine-tuning the parameters of the PID controller using the newly developed Aquila Optimizer (AO). PID controllers are widely recognized for their simple yet effective control in FSBB converters. However, to further improve the efficiency and reliability of the control system, the PID control parameters must be optimized. In this context, the application of the AO algorithm proves to be a significant advance. By optimizing the PID coefficients, the dynamic responsiveness of the system can be improved, thus reducing the response time. In addition, the robustness of the control system is enhanced, which is essential to ensure stable and reliable operation under varying conditions. The use of AOs plays a key role in maintaining system stability and ensuring the proper operation of the control system even under challenging conditions. In order to demonstrate the effectiveness and potential of the proposed method, the performance of the AO-optimized PID controller was compared with that of PID controllers tuned by other optimization algorithms in the same test environment. The results show that the AO outperforms the other optimization algorithms in terms of dynamic response and robustness, thus validating the efficiency and correctness of the proposed method. This work highlights the advantages of using the Aquila Optimizer in the PID tuning of FSBB converters, providing a promising solution for improving system performance.

Molecular simulations of increased thermal energy storage in metal–organic heat carriers with R1234ze(E)/R32 mixtures combined with IRMOF-1

Metal-organic heat carrier (MOHC) nanofluids offer a promising solution to enhance the efficiency of Organic Rankine Cycle systems. In this study, we developed a computational model to assess the thermal energy storage capacity of MOHC nanofluids using a base fluid mixture of R1234ze(E) and R32 refrigerants. Through Grand Canonical Monte Carlo and molecular dynamics simulations, we examined the adsorption characteristics, desorption heat, and thermodynamic properties of MOHCs. Our findings reveal that R32 molecules are preferentially adsorbed in IRMOF-1 compared to R1234ze(E), with adsorption selectivity of R32 over R1234ze(E) increasing under higher adsorption pressures. When the temperature during the endothermic process surpasses the phase transition point of the base fluid, the latent heat of phase transition can be fully exploited to enhance energy storage. Moreover, the inclusion of IRMOF-1 nanoparticles significantly improves the energy storage properties of both R32 and R1234ze(E), as well as their mixtures. The energy storage enhancement provided by IRMOF-1 is particularly pronounced under high working pressures and low temperature differences. These insights offer valuable guidance for selecting appropriate base fluids in MOHC systems, thereby optimizing the utilization of low-grade energy sources.