Enhancement Of Efficiency Research Articles

Study of hybrid nanofluid flow in a porous medium over an exponentially stretching sheet under Joule heating and thermal radiation: Finite difference

The significant purpose of present investigation of the behavior of a nanofluid's in magneto-hydrodynamics (MHD), mass transfer, Joule heating, and boundary layer transfer characteristics over an exponentially stretching sheet in a porous medium and thermal radiation effects. The article's goal is to look at the fluid flow and heat transmission characteristics from a sheet of hybrid nanoparticles. The partial differential equations (PDEs) that were derived for the mathematical model were converted using the proper similarity transformation into ordinary differential equations (ODEs). The hybrid nanofluid composed of 97 % of ethyl glycol (EG) and the volume concentration of Magnetite (Fe3O4) and Copper(Cu) are ranging from 0.5 % to 2.5 % both respectively. The effects of thermal radiation, stretching rate, Joule heating, porous medium permeability, and nanoparticle volume fraction on the flow and heat transmission properties are investigated by numerical simulations using the finite difference method (FDM). The analysis reveals that the inclusion of nanofluids enhance the thermal conductivity and enhance the heat transfer rate. Additionally, the influence of variable viscosity on the flow behavior and thermal characteristics are examined graphically. The effects of variable viscosity and thermal conductivity are examined, as it has significance in optimizing system under various thermal and magnetic effects. This study offers a pathway to develop more efficient thermal management solutions, by contributing to technological advancement and energy saving. The key findings of present study reveal that the temperature profile rises significantly due to Joule heating effects. The Nusselt number reveal an improvement of about 12 % when the volume fraction of nano particles is increased by 1–4 % indicating the enhancement in heat transfer efficiency. Similarly, the velocity profile was influenced by porous medium permeability as 11 % increase in porosity result a 18 % decrease in velocity profile.By using parametric research, the role of physical parameters in determining the local skin-friction coefficient, temperature, nanoparticle volume percentage, and longitudinal velocity profiles, local Nusselt number, and local Sherwood number are thoroughly examined. A graphic representation of the velocity, temperature, and concentration distribution findings is presented.

Digitalization for ensuring innovative development of the enterprise

This paper examines the influence of digitalization on the innovative development of enterprises. The integration of digital technologies serves as a key driver for product, process, marketing, and organizational innovations, which collectively contribute to competitive advantages and the enhancement of business process efficiency. Core avenues of digitalization encompass automation, data analytics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and digital platforms. These technologies equip firms with the capacity to swiftly adapt to market fluctuations, optimize productivity, and foster resilience in an increasingly dynamic economic environment. The strategic implementation of digitalization extends beyond operational enhancements, enabling the reconfiguration of traditional business models. By leveraging digital tools, enterprises are able to deliver personalized customer experiences, enhance decision-making through real-time data analytics, and achieve more efficient coordination across value chains. Additionally, the adoption of digital platforms facilitates rapid scaling and the exploration of novel revenue channels through the provision of innovative digital services. In the context of a globalized economy, where adaptability and technological sophistication are critical, digital transformation emerges as a decisive factor in sustaining long-term competitiveness. Furthermore, this paper addresses the intersection of digitalization and sustainability. A growing number of enterprises are employing digital technologies to decrease resource consumption and mitigate environmental impact, aligning their practices with the increasing emphasis on corporate social responsibility and the principles of a circular economy. As businesses continue to pursue digital transformation, their capacity to innovate, maintain competitive positioning, and contribute to sustainable development will play a pivotal role in shaping the future trajectory of the business landscape.

Achieving high efficiency and bandwidth enhancement of DRA arrays: Synergic effects of LiMg4AlO6 microwave dielectric ceramic and 2D metamaterials

In this paper, LiMg4AlO6 ceramics with a sintering temperature of 1380 °C to 1460 °C are prepared, and the microwave dielectric properties of the ceramics and their potential application in dielectric resonator antenna (DRA) arrays are investigated. Analysis techniques such as XRD and Rietveld refinement reveal that the LiMg4AlO6 ceramics are rock salt structures and the space group is Fm-3 m (225). The ceramic obtained excellent microwave dielectric properties (εr = 10.17, Q×f = 172, 558 GHz, and τf = - 32.5 ppm/ °C) at 1440 °C. The εr of LiMg4AlO6 ceramics exhibits minimal variation across a wide temperature range (-100 °C to 400 °C) without phase transition. Furthermore, the 2D metamaterials (metasurfaces) are introduced in the DRA, which can significantly improve the radiation efficiency and bandwidth of the antenna. Measured results demonstrate that the antenna has a wide bandwidth ranging from 8.07 GHz to 8.57 GHz, with a maximum realized gain of 8.9 dBi and a peak radiation efficiency of 97%. The proposed ceramic antenna array combines the superior properties of 2D metamaterials and ceramic materials, which opens up a new avenue for ceramic antenna design and satellite communication applications.

Experimental Study on the Effects of Beeswax as Absorber for Solar Still

An experimental study on the effects of beeswax incorporated in the construction of the absorber for a solar still was conducted. As part of the study, two solar stills of the same geometry were constructed with the same water depth and volume. The one without a phase change material (PCM) was used as the control still, while the other with beeswax as a phase change material laid at the bottom of its basin to serve as an absorber was used as the experimental still. The experiments were conducted in the thermodynamic laboratory of JS Tarka University, Makurdi, Nigeria, between 10.00 am and 5.00 pm for two weeks. 16 litres of water were used for each still, corresponding to 100 mm depth. The stills were kept side by side and exposed to sunshine from 10.00 am to 5.00 pm for three days. Temperatures of the water at inlet and in the still were measured. HT-9815 digital thermocouple was used to determine the temperature of water in the basin and that of the inner glass cover. SM206 solar power meter was used to measure the amount of solar energy incident on the still’s collector. The results showed that the still with beeswax as phase change material (experimental still) has an improved thermal efficiency enhancement of 109.30% as compared to the still without a phase change material with a thermal efficiency enhancement of 86.1%. This implies that beeswax is a good phase change material for solar stills, and the use of beeswax should be encouraged as it is non-toxic and organic.

Machine Learning Algorithms for Prediction of Boiler Steam Production

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.

Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics

Environmental characteristics, particularly in mountainous regions with significant slopes, substantially impact vehicle power demand and fuel consumption, thereby influencing both fuel economy and vehicle lifespan. However, existing research lacks energy management strategies specifically designed for these challenging terrains. This study presents an innovative energy management strategy (EMS) for fuel cell hybrid electric buses (FCHEB) utilizing a deep reinforcement learning algorithm considering terrain characteristics. A comprehensive model that incorporates road fluctuations and steep slopes was developed to accurately represent the terrain features of mountainous regions. Driving cycle and road condition data specific to hydrogen fuel cell buses in these areas were collected and processed as for the training sets of EMSs. Subsequently, an EMS based on the Proximal Policy Optimization (PPO) algorithm was devised to address the unique characteristics of these mountainous regions. Simulation results indicate a 26.16 % improvement in fuel economy and a 44.98 % enhancement in convergence efficiency compared to traditional methods that do not consider road slopes. Further validation through new bus route trials and standard driving cycle tests confirms the strategy's robustness and adaptability.

High-Altitude-UAV-Relayed Satellite D2D Communications for 6G IoT Network

High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies.

A newly-constructed technology to remove and recover diethyl phthalate from wastewater by using the instant plasticization assembly ability of PVC

Diethyl phthalate (DEP) is a typical environmentally organic pollutant, widely used in the production process of polyvinyl chloride (PVC) to improve the flexibility of plastic materials. Its interaction with living organisms can inflict considerable harm to reproductive system functions. This research aims to utilize tetrahydrofuran (THF) to selectively break the chemical bonds in PVC molecules to provide more adsorption sites. Then incorporates the plasticizing assembly process of PVC to instantly remove and recover DEP from wastewater, achieving waste utilization, and sustainable environmental development. The research found that PTFR with a concentration of around 75 mg/L shows the best DEP removal efficiency. Sequencing batch processing removes more DEP compared to direct processing under the same material usage conditions. Furthermore, the recovery rate of DEP can reach over 90%. The technology demonstrates notable enhancements in removal efficiency and adsorption duration when compared to conventional adsorption techniques. This research has established an instant and efficient method for DEP removal, providing a new idea and technology for plasticizer treatment in practical wastewater.

Research on the jet active control mechanism of gas-liquid separation in helical-axial multiphase pumps

In the field of deep-water oil and gas extraction, the intermittent gas blocking phenomenon in helical-axial multiphase pumps leads to decreased pump performance and reliability, severely limiting their large-scale application and promotion in engineering projects. To address this challenge, this study is based on the principle of jet flow field control to flow separation, utilizing external energy for active intervention to suppress the separation phenomena and improve the hydraulic efficiency within the impeller flow channel. The research reveals that the jets achieved a reconstruction of the internal flow in the helical-axial gas-liquid mixed transport pumps. Using orthogonal experimental methods, a preliminary predictive analysis of the factors influencing the effectiveness of the jet system was conducted. The study explored the impact of jet parameters on enhancing the performance of helical-axial gas-liquid mixed transport pumps from the perspective of diminishing gas aggregation at the trailing edge of the rotor cascade and curtailing backflow conditions, and established a mathematical model for the head and efficiency of helical-axial multiphase pumps under jet active control. Research indicates that the jet velocity significantly impacts the efficiency enhancement of multiphase pumps, with an optimal jet speed coefficient, Cjet = 10, identified. Beyond this speed, the benefits brought by increased jet velocity do not compensate for the costs of introducing high-speed jets, and instead, may lead to a reduction in the efficiency of the pump. Jet flow field control methods alleviate the suction side gas aggregation downstream of the jet holes and in the flow domain traversed by the jets, effectively reducing the aggregation of gas phases in the rear channel of the rotor and suppressing backflow near the trailing edge of the rotor cascade. Jet control measures strengthen the working capacity of the blade in the flow direction beyond 0.35 times the length of the streamline, expanding the working range of the blade and enhancing the work efficiency of the multiphase mixed transport pump. Higher jet velocities result in more significant enhancements of blade working power, especially under conditions of high initial gas content, where the relative increase in blade load is more pronounced.

Optimization countermeasures for high-quality development of green economy driven by environmental regulations, carbon emission reductions, and efficiency enhancements

Optimization countermeasures for high-quality development of green economy driven by environmental regulations, carbon emission reductions, and efficiency enhancements

Tailoring Morphology of MgO with Mg-MOF for the Enhanced Adsorption of Congo Red.

The active site is a highly anticipated property of adsorbent in the field of separation, as it enables a concurrent enhancement of both adsorption efficiency and adsorption rate. Herein, employing morphology-oriented regulation, we successfully fabricated heterogeneous MgO adsorbent from a magnesium-based metal-organic framework (MOF) precursor, octahedral MgO-M and laminated sheets MgO-P, for the capture of Congo red (CR). Specifically, the octahedron MgO-M exhibits a greater abundant of moderately and strongly alkaline sites, which facilitate the adsorption of CR. Furthermore, the synergistic effect between alkaline site and lattice oxygen further enhances the adsorption process. The adsorption data align more closely with the Pseudo-second-order kinetic model and Langmuir models. Notably, the exceptional adsorption capacity (exceeding 1900 mg·g-1 for MgO-M) and the secondary regeneration efficiency (over 96% removal rate across six cycles) offer promising prospects for future industrial applications, effectively addressing challenges related to poor water stability and difficult storability. Additionally, characterizations reveal the positive roles of alkaline sites, lattice oxygen, and pore structure in capturing significant quantities of CR through mechanisms such as electrostatic interactions, hydrogen bonding, and pore filling.

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning

In the air-to-ground transmissions, the lifespan of the network is based on the “unmanned aerial vehicle's (UAV)” life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate’s minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the “Internet of Things (IoT)” systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.

Modeling, Design, and Application of Analog Pre-Distortion for the Linearity and Efficiency Enhancement of a K-Band Power Amplifier

Modeling, Design, and Application of Analog Pre-Distortion for the Linearity and Efficiency Enhancement of a K-Band Power Amplifier

Defects passivation in chloride-iodide perovskite solar cell with chlorobenzylammonium halides

Despite promising optoelectronic properties, it is a matter of fact that ion migration is unavoidable in chloride-iodide-based perovskite solar cells due to a radius mismatch between chlorine and iodine. Local defects like atomic vacancies or accumulation of atoms might occur due to ion migration in chloride-iodide-based perovskite thin film. Again, atomic vacancies are prevalent in solution-processed perovskite thin film. Here we have investigated the passivation of these defects with two organic halide passivators named 4-chlorobenzylammonium chloride and 4-chlorobenzylammonium bromide. They passivate both the surface and bulk of the perovskite thin film. The surface of the perovskite thin film is passivated with the bulky organic benzylammonium cations. The bulk of the perovskite thin film is passivated with the diffusion of chlorine or bromine. With different combinations of the two passivators, we vary the chlorine range from 50% to 100% and the bromine range from 25% to 50%. We have found the champion cell performance for 75% chlorine and 25% bromine combination. The enhancement of device efficiency was around 15% compared to the control device and the device stability was also considerably enhanced.

An iterative learning-based integrated motion planning and control method for autonomous patrolling of unmanned surface vehicles

Abstract The minimization of maritime patrol durations and the optimization of patrol trajectories are still a prevailing challenge for autonomous navigation of unmanned surface vehicles (USVs). In this paper, we propose an integrated trajectory planning and control method to achieve time-optimal autonomous patrol of USVs by a data-efficient iterative learning-based predictive control algorithm. We build the optimization problem of the algorithm by introducing a local cost and a local safe constraint set for closed-loop efficient data-driven learning. To guarantee recursive feasibility, stability and convergence properties of the optimization algorithm at each iteration, the local cost and the local constraint set are designed and update iteratively using the historical vehicle states at previous iterations as a dataset. Different from traditional optimization methods, the proposed method does not require a reference path. The cost function of the optimization method decreases monotonically and converge to obtain a time-optimal control law for trajectory planning and tracking of USVs only after several iterations. The proposed approach is validated on the robotic operation system in typical maritime patrol scenarios. The results show that the proposed method implement a 5% reduction of patrol time, fewer lateral track errors, and smoother trajectories compared to the traditional MPC algorithm under water flow conditions. In addition, Enhancements in patrol operation efficiency of USVs are achieved by the algorithm, even under the constraints of varied patrol paths, attesting to its versatility.

Performance improvement of red, green and blue InGaN micro-LEDs with distributed Bragg reflector

Abstract Red-green-blue (RGB) micro light-emitting diodes (micro-LEDs) without distributed Bragg reflector (DBR), with air-separating DBR, and with integrated DBR, were demonstrated. The effect of the DBRs as reflectors on the external quantum efficiency (EQE) and electroluminescence (EL) spectra enhancement of RGB micro-LEDs was systematically investigated for realizing higher-performance micro-LEDs for display applications. At 5 A/cm2, the EQEs of the RGB micro-LEDs with integrated DBR were improved by 38%, 33%, and 32%, respectively, with comparison to the RGB DBR free micro-LEDs. Further, the full width at half maximum (FWHM) of the red micro-LEDs was reduced by 4.3 nm at 50 A/cm2 with the integrated DBR due to the higher enhancement of the central wavelength spectrum. The green and blue micro-LEDs with integrated DBR had higher EQE and the red micro-LEDs with integrated DBR had narrower FWHM compared to those with air-separating DBR. However, the peak wavelength of the RGB micro-LEDs with integrated DBR shifted, resulting in a lower color gamut in CIE 1931. The above work provides guidance for future full-color micro-display applications based on RGB InGaN micro-LED technology.

A Comparative Analysis of Performance Simulation for PUI Detectors Based on Traditional Probability Model and the Vasyliunas and Siscoe Model.

In recent years, the enthusiasm for deep space missions has remained unabated, resulting in continuous advancements in the research field of space environment and particles. Many instruments carried on these missions have conducted detection of pickup ions (PUIs) in the solar system. For those instruments, simulation is an effective means and a crucial step for their performance optimization and future operation in-orbit. It holds great significance for the instrument's in-orbit performance assessment, science operation optimization, and detection efficiency enhancement. In this paper, the traditional probability model and the Vasyliunas and Siscoe (V-S) model are used to generate the PUIs, which are the input for the simulation of the PUI detector. For further analysis, the numerical results of the simulation are processed to calculate the instrument's geometric factor, mass resolution, and count rates. Then, two sets of experiments are carried out for the comparison of the traditional probability model and the V-S model. The results show that, for the simulation of the instrument in the design stage, the simulation results of the traditional probability model and the V-S model are not much different. However, for the simulation of the instrument performance in-orbit, the PUI data generated based on the V-S model gave a better result than those of the traditional probability model. This conclusion is of great significance for evaluating the detection ability of the PUI detector in future deep space explorations.

Exploring the efficiency enhancement of perovskite solar cells by chemical bath depositing SnO2 on mesoporous TiO2 electrode

This study presents a groundbreaking approach to enhance the efficiency of perovskite solar cells (PSCs) by employing Chemical Bath Deposited (CBD) SnO2 on mesoporous TiO2 (m-TiO2) as an electron transport layer (ETL) without a traditional compact TiO2 (c-TiO2) layer between FTO (fluorine-doped tin oxide) and m-TiO2 ETL. Our investigation reveals that this method significantly improves carrier dynamics, as evidenced by reduced time constants compared to traditional ETLs. The CBD SnO2 not only augments electron transport but also effectively reduces defect density in m-TiO2 films. These enhancements are reflected in the improved photovoltaic parameters of the cells, including higher open-circuit voltage (VOC), short-circuit current (JSC), fill factor (FF), and an impressive power conversion efficiency (PCE) exceeding 23.62 %. The study further underscores the scalability of this technology, highlighting advancements in screen printing and chemical bath deposition techniques vital for large-area PSC applications. Remarkably, the uniformity of PCE across large-area cells confirms the efficacy and consistency of this approach, which provides a new and general strategy for preparing high-efficiency PSCs.

Enhancing textile wastewater treatment for subsequent biological processes by integrating electrooxidation and electrocoagulation

ABSTRACT The presence of refractory contaminants in textile wastewater is one of the major concerns while handling them with the biological processes at common effluent treatment. Electro-oxidation (EO) as a standalone process is an insufficient treatment method for the abolition of inorganic contaminants (carbon and non-carbon). By incorporating electrocoagulation (EC) as an associated treatment method after EO, removal of such contaminants becomes easy, which not only makes the treated wastewater fit for biological remediation but also reduces load on biological units. The removal of non-carbonic impurities was assessed in terms of improvement in the chemical oxygen demand (COD) post EC. L25 orthogonal array of experiments was obtained using the Taguchi method. From the S/N ratio plot, the optimal process combination was obtained as, EO with current density = 25 mA/cm2, electrolysis time = 50 min followed by EC with current density = 18 mA/cm2, speed of rotation = 50 rpm and electrolysis time = 40 min. The enhancement in COD and total organic carbon removal efficiencies after EC were 65.11 and 63.57%, respectively, over EO. The biodegradability index also improved from an initial value of 0.098–0.737 post-hybrid treatment. Inorganic carbon reduced from a value of 36.37 mg/L after EO to 0.1 mg/L post EC.

Design of experiments with the support of machine learning for process parameter optimization of all‐small‐molecule organic solar cells

AbstractTraditionally, squaraine dyes have been studied and employed in biomedical research due to their excellent optical properties, and the molecules are being adopted in different research fields such as organic solar cells. In this study, we investigate correlations between solar cell performance and processing parameters of all‐small‐molecule bulk heterojunction solar cells comprising squaraine (SQ) as electron donor (D) and non‐fullerene small molecules (e.g., ITIC) as electron acceptor (A) with the help of machine learning (ML) and design of experiment (DoE) methods. Among the five predictive ML models tested with the selected parameters, the eXtreme gradient boosting model shows the satisfactory results with quite high coefficient of determination of 0.999 and 0.997 in training and testing sets, respectively. By measuring the contribution of each input variable to solar cell efficiency, four process parameters, that is, the total concentration, the ratio of D/A, the rotational speed of spin coating, and the annealing temperature, are found to be the key features strongly correlated to solar cell efficiency. From contour plots in DoE, the highest solar cell efficiency of approximately 5% can be predicted under the conditions of 15 mg mL−1 in solution concentration, a 1:2 mix ratio of D and A, rotational speeds ranging from 800 to 900 rpm, and annealing temperatures within 100–110°C. Using the suggested parameter conditions, we fabricated solar cells, achieving a quite high efficiency of approximately 4%. Besides the global optimization conditions, we also employ the solvent vapor annealing combination to the thermal annealing to facilitate further mobilization of molecules and more optimized microstructure of bulk heterojunction films, resulting in a further enhancement in solar cell efficiency of more than 20%.