Minimum Energy Consumption Research Articles

An overview of the constructions of conveyors for moving bulk materials, comparison and study of their parameters

The production of concrete mixes, along with their use in the production of building materials and structures, is one of the key processes in the construction industry during the construction, restoration and repair of buildings and structures. Because of this, the need to create modern concrete mixing plants that will meet the requirements of minimum energy consumption and maximum productivity of concrete mixture production is an urgent task. Not only the main operations, which include the dosing of the components of the mixture and their mixing, but also the maintenance operations, namely operations that ensure the timely movement of the components of the concrete mixture from warehouses to the main technological equipment, affect the set rhythm of the concrete mixture production. Conveyors of various types and designs are used to move bulk materials, such as crushed stone and sand. For the rational selection of such equipment in accordance with the characteristics of the cargo to be transported, knowledge of the types of conveyors, their structures and parameters, understanding of operation issues and methods of parameter calculation are required. In addition, it is worth paying attention to the following parameters: maximum cargo transportation productivity, low energy consumption per unit of moved products, low metal content of the structure. The work reviewed the most common designs of conveyors used to move bulk materials in concrete mixing plants, analyzed the disadvantages and advantages of conveyors, as well as technical parameters. As a result, the predominant directions for the use of belt and plate conveyors at construction enterprises were determined. The advantages of belt conveyors, which contribute to their widespread distribution, are high productivity, simplicity of design, reliability, quiet operation, low specific power consumption. When choosing a conveyor, it is recommended to choose the equipment with the highest productivity and the lowest power of the drive motors, however, the performance should be clearly related to other technological equipment.

Toward Methanol Production by CO2 Hydrogenation beyond Formic Acid Formation.

ConspectusThe Paradigm shift in considering CO2 as an alternative carbon feedstock as opposed to a waste product has recently prompted intense research activities. The implementation of CO2 utilization may be achieved by designing highly efficient catalysts, exploring processes that minimize energy consumption and simplifying product purification and separation. Among possible target products derived from CO2, methanol is highly valuable because it can be used in various chemical feedstocks and as a fuel. Although it is currently produced on a plant scale by heterogeneous catalysis using a Cu/ZnO-based catalyst, a limited theoretical conversion ratio at high reaction temperatures remains an issue. In addition, a catalytic system that can be adjusted to accommodate a variable renewable energy source for the synthesis of methanol is more desirable than current continuous-operation systems, which require a reliable energy supply. Recently, significant progress has been made in the field of homogeneous catalysis, which primarily relies on an indirect route to synthesize methanol via the hydrogenation of carbonate or formate derivatives in the presence of additives and solvents. However, homogeneous catalysis is inappropriate for industrial-scale methanol production because of the inefficient separation and purification processes involved.In this Account, we demonstrate a novel approach for methanol production under mild reaction conditions by CO2 hydrogenation catalyzed by multinuclear iridium complexes under heterogeneous gas-solid phase conditions without any additives and solvents. One of the aims of this Account provides insights for overcoming the barriers for efficient CO2 hydrogenation by focusing on catalyst design, specifically by incorporating varying functionalities into the ligand. The fundamental strategy entails activating hydrogen molecule and enhancing the hydricity of the resulting metal-hydride species, which is based on the following two concepts of catalyst design: (i) Activating a metal-hydride by electronic effects; and (ii) accelerating H2 heterolysis. We have elucidated the mechanism for accelerating H2 heterolysis using a state-of-the-art catalyst that contains an actor-ligand that responds to or participates in catalysis as opposed to a classical spectator-ligand.We have also demonstrated a novel heterogeneous catalysis using a molecular catalyst as a key step for the hydrogenation of CO2 to methanol beyond formic acid formation. The dehydrogenation of formic acid as a reverse reaction of formic acid hydrogenation is strongly favored in acidic aqueous solution. To circumvent the equilibrium limitation, we have envisioned an alternative route that both prevents the liberation of formic acid into the reaction medium, and develops a multinuclear complex to facilitate the transfer of multiple reactive hydrides. The unconventional gas-solid phase catalysis is capable of preventing the liberation of formate species and promoting further hydrogenation of formic acid through multihydride transfer.This novel catalytic system, which is the fusion of a molecular catalyst in heterogeneous catalysis, provides high performance for methanol synthesis through a sophisticated catalyst design and straightforward separation processes. A detailed mechanistic analysis of molecular catalysts in the gas phase would lead to significant progress in the field of Surface Organometallic Chemistry (SOMC).

Electrochemical Relithiation in Spent LiFePO4 Slurry for Regeneration of Lithium-Ion Battery Cathode.

Recycling spent lithium-ion batteries (LIBs) in a green and economical way is vital for maintaining the sustainability of the LIB industry. However, given the low content of high-value components in olivine-type lithium iron phosphate (LFP), traditional metallurgical processes are economically unfeasible for recycling due to high chemical/energy consumption and labor-intensive procedures. This study proposes a facile electrochemistry strategy to directly regenerate the spent LFP material by an electrically driven lithiation process as a spent LFP slurry (200 g/L) rather than as electrodes. Minimal energy and chemical consumption are achieved by enabling the healing of spent LFP without destroying the original olivine-type crystal structure. The proposed method utilizes mild healing conditions (25 °C for 2 h) and LiCl solution as the only reagent in the regeneration process, significantly lowering the expenses associated with producing cathode electrodes. The electrochemical performance of the regenerated LFP have been dramatically recovered after regeneration, exhibiting a capacity of 151.5 mA h g-1 at 0.1 C and 96.6% capacity retention over 400 cycles at 1 C. This approach demonstrates a high processing capability and offers considerable economic and environmental benefits, making it an eco-friendly option and supporting the sustainable development of the LFP industry.

Artificial Neural Network-Based Route Optimization of a Wind-Assisted Ship

The International Maritime Organization aims for net-zero carbon emissions in the maritime industry by 2050. Among various alternatives, route optimization holds an important place as it does not require any additional component-related costs. Especially for wind-assisted ships, the effectiveness of different sailing systems can be improved significantly through route optimization. However, finding the ship’s optimal route is computationally expensive when the totality of possible weather conditions is taken into consideration. To determine the optimal route that minimizes energy consumption, an energy model based on the environmental conditions, ship route and ship speed was built using artificial neural networks. The energy consumed for given input data was calculated using a ship dynamics model and a database was generated to train the artificial neural networks, which predict how much energy is consumed depending on the route followed in given environmental conditions. Then, such networks were exploited to derive the optimal routes for all the relevant operational conditions. It was found that route optimization can reduce the overall ship energy consumption depending on the weather conditions of the environment by up to 9.7% without any increase in voyage time and by up to 35% with a 10% delay in voyage time. The proposed methodology can be applied to any ship by training real weather conditions and provides a framework for reducing energy consumption and greenhouse gas emissions during the service life of ships.

Deformation-induced gas adsorption and self-desorption dynamics of a carbon nano-network: Molecular dynamics modeling focusing on CO2 capture

Gas adsorption and separation/desorption are two pivotal stages in gas capture, demanding additional energy for liberating gas molecules from the adsorbent. Hence, it’s essential to engineer an adsorbent with minimal energy consumption yet effective gas adsorption–desorption characteristics. Accordingly, this research introduces a carbon nano-network (CNN) material capable of inhaling and exhaling gases via self-deformation. Molecular dynamics simulations demonstrate that the gas adsorption–desorption capabilities of CNN can be efficiently regulated by its deformation. While the size of CNN minimally affects the gas sorption rate, a larger CNN necessitates a prolonged duration to achieve saturation under identical gas conditions. Incorporating elongated appendages in CNN enhances both its stability during contraction and its efficiencies in gas adsorption and desorption. We discerned that attaining a critical gas density within a confined space is imperative to initiate self-desorption of CNN. Specifically, if the gas density dips below this critical threshold–set at 5.2 times that of CO2 at room temperature (300 K) and atmospheric pressure (1 Bar)–CNN exhibits no self-desorption capability. Nevertheless, even a marginal elevation in gas density triggers and maintains self-desorption with a consistently surpassing desorption ratio of 98 %. This discovery offers valuable insights for designing automatic self-desorption materials or apparatuses.

Deep learning-based energy prediction and tangent search remora optimization-based secure multi-path data communication mechanism in WSN

ABSTRACT Wireless Sensor Network (WSN) has been exploited in numerous regions which can be hardly accessed by humans. However, it is essential to convey the information accumulated by the sensing devices or nodes to the Base Station (BS) for further processing. Multipath routing protocols are found to address these challenges and provide reliable communication. This paper aims to find an optimal path to the gateway with minimum energy consumption and reduced error rate while meeting the end-to-end delay requirements. In this research, an effective multipath routing based on energy prediction and hybrid optimization is developed. Here, a Deep Q-Network (DQN) is applied to predict the energy, and the process is augmented by the usage of a proposed Tangent Search Remora Optimization (TSRO) algorithm. Further, the multipath routing is executed using the TSRO algorithm, considering a fitness function formulated using various factors, like residual energy, distance, throughput, reliability, trust factors, predicted energy, Link Life Time (LLT), delay, and traffic intensity. The devised TSRO-routing is scrutinized for its competence based on trust, throughput, energy, distance, and delay and has achieved superior values of energy of 0.402 J, throughput at 25.056Mbps, trust at 84.975, and minimal distance of 29.964 m, and delay of 0.750 ms.

Insights into transition metal encapsulated inside carbon aerogel for accelerated electro-peroxone oxidation: Activity evaluation and reactive oxygen species generation

Transition metals have critical influences on the generation of reactive oxygen species in activating O3 and H2O2, as the binary oxidant sources in electro-peroxone (EP). To evaluate the catalytic activity of different metals and reveal the reactive oxygen species generation in heterogeneous EP, series of metals encapsulated inside carbon aerogel spheres (M@CS, M=Fe, Co, Ni, Zn) were fabricated for strengthening EP. Atrazine was used as model pollutant due to its low reactivity with O3, and the abatement trends followed Co@CS > Fe@CS > Ni@CS > Zn@CS. Co@CS exhibited the superior catalytic activity with maximum metal active site utilization (1.28 × 103 min-1 mol-1 per mole Co atom sites at pH 7) and minimum electric energy consumption (0.106 kWh/m3-log). The catalytic activity of M@CS was related to the adsorption energies for O3 and H2O2 (Fe@CS > Co@CS > Ni@CS > Zn@CS) and the graphitization degree (Co@CS > Fe@CS > Ni@CS > Zn@CS). Verified by theoretical calculation and in-situ Fourier transformed infrared, the metal effectively promoted the adsorption and decomposition of O3 and H2O2 into the surface oxygen intermediates, finally generating ·OH/·O2-/1O2. The carbon layer was conducive to the reduction of internal metal, ensuring catalyst durability. Electronic paramagnetic resonance and kinetic model revealed the occurrence, ratio and transient concentration of ·OH/·O2-/1O2 in M@CS/EP. M@CS/EP showed good performance for pollutant removal in coexistence of anions, humic acid and real water. This study provides guidance for selecting the metal–carbon catalyst in heterogeneous EP.

Truthful mechanism for joint resource allocation and task offloading in mobile edge computing

In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-time data processing and analysis by offloading to the MEC servers. The objective is maximizing social welfare in an auction-based model. However, the distances between mobile devices and access points lead to differences in energy consumption. Unfortunately, existing works have not considered both maximizing social welfare and minimizing energy consumption. Motivated by this, we address the problem of joint resource allocation and task offloading in MEC, with heterogeneous MEC servers providing multiple types of resources for mobile devices (MDs) to perform tasks remotely. We split the problem into two sub-problems: winner determination and offloading decision. The first sub-problem determines winners granted the ability to offload tasks to maximize social welfare. The second sub-problem determines how to offload tasks among the MEC servers to minimize energy consumption. In the winner determination problem, we propose a truthful algorithm that drives the system into equilibrium. We then show the approximate ratios for single and multiple MEC servers. In the offloading decision problem, we propose an approximation algorithm. We then show it is a polynomial-time approximation scheme for a single MEC server. Experiment results show that our proposed mechanism finds high-quality solutions in changing mobile environments.

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

A novel fractional rat hawk optimization–enabled routing with deep learning–based energy prediction in wireless sensor networks

SummaryWireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively.

The method for comparing different blade shapes of flat knives of a rotary cultivator

BACKGROUND: In the process of tillage, flat knives of rotary cultivators are to carry out the process of cutting soil and weeds with minimal energy consumption and not to become clogged with weeds, which is due to the compliance of the parameters of their blades with operating conditions. However, despite the abundance of works devoted to the study of the working process of flat knives of rotary cultivators, there is not any method of a comparative assessment of the various shapes of their blades. AIM: Development of the method for comparing different blade shapes of flat knives of rotary cultivators. METHODS: The research design included studying the following issues: clarifying the conditions for weeds to fall off the curved blade of a flat knife performing a plane-parallel movement; determining the equation of the curve of a knife blade that satisfies the condition of weeds to fall off the blade; development of methods for comparative analysis of the energy intensity of the process of cutting soil with flat knives with blades of various shapes; experimental verification of theoretical conceptions. The study objects were the processes of interaction of weeds and soil with the blades of flat knives of various shapes. The studies were conducted during 2022 and 2023. RESULTS: The condition for weeds to fall off the curved blade of a flat knife performing a plane-parallel movement has been clarified (a more informative form of notation), based on which an equation for the rational curve of the blade has been obtained. The comprehensive method has been developed for comparing different shapes of blades of flat knives of rotary cultivators, according to the criteria of compliance with the condition of weeds to fall off the blade and the energy intensity of soil cutting. Examples of the application of the method for the analysis of various shapes of blades of flat knives of rotary cultivators and the development of one of the effective shapes, as well as the results of experimental studies are given. CONCLUSION: The proposed method helps to give specific practical recommendations on the advisability of using knives of one shape or another in the operating conditions under consideration, taking into account the operating parameters of the machines, as well as to create effective combined blades.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry: A Review

Process optimization is acutely vital: mining and a metal industries research. Through applying modelling and optimization tools, businesses see tremendous improvement in process efficiencies, declining costs of production, and ensuring high quality. The valuable of simulation tools have also been reflected in the design of the virtual plants which make possible to conduct test runs of different process scenarios thereby providing the opportunity to optimize the performance of the plants. So, even if these issues are close to being resolved, some challenges must still be tackled to approach the full field of process optimization enjoyment. One of the main drawbacks is the integration of sustainable development and environmental evaluation into optimization phase. This can be achieved by developing models that are amended to an environmental impact of each process parameters and afterwards an optimization of processes will be based on environment factors. The second challenge is for the research community to come up with more advanced and sophisticated mathematical models that are capable of not only capturing but also depicting the diverse and complex interactions that take place between different processes variables. Such an approach indeed requires implementation of more precise data analysis tools and techniques, including neural networks and genetic algorithms. While there are many difficulties in it way, the process optimization of a bright future is foreseeable. The fast growth of the technological sphere, such as machine-2-machine and big data analytics, enables process optimization which was unavailable before. By implementation of a set of sensors and real-time data analysis plant operators are being provided with necessary information that enables this way to exercise real-time decisions for the sake of the increase of the plant performance. Minimizing energy consumption, increasing the product yield, and less damaging to the environment is a potential focus for the research. Through the application of mathematical models, optimization algorithms, and simulation-based approaches, one can compare the different methods of each process and the produced product yields.

Security-aware energy-efficient design for mobile edge computing network operating with finite blocklength codes

Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding.

Priority rules for handling containers to improve energy consumption and terminal efficiency

AbstractThis paper addresses the optimization of the yard crane handling processes in a container terminal to reduce energy consumption and improve overall system performance. More precisely, the paper presents and evaluates different sequencing rules, based on predefined priorities, to organize the rail yard to minimize moves during the rail loading operations. The minimization of overall energy consumption and maximum tardiness are considered, simultaneously assessing these two components of the objective function to better understand how they interact and how they can be optimized together. As a novel issue in optimization, a hill climbing algorithm is implemented, searching for the yard configuration that most improves the efficiency of container handling while being able to integrate different management rules of the terminal. The reference case study is the PSA Pra terminal in Genoa, Italy. A full rail yard with known delivery times, and crane operating along a single stack, is the operative scenario. Random due time sequences are generated during test instances, while technical data of crane are used. Moreover, crane movements involve both loading and unloading along multiple axes. From the results, the best priority rules improve energy consumption and lateness of the initial configuration of the yard by up to 55%, thus allowing the terminal management to reorganize the storage areas accordingly and improve their efficiency. The proposed priority rules bridge the gap between theoretical optimization procedures and container terminal practices.

Energy-Efficient Handover Algorithm for Sustainable Mobile Networks: Balancing Connectivity and Power Consumption

In the era of pervasive mobile and heterogeneous networks, maintaining seamless connectivity during handover events while minimizing energy consumption is paramount. Traditional handover mechanisms prioritize metrics such as signal strength, user mobility, and network load, often neglecting the critical aspect of energy consumption. This study presents a novel approach to handover decision-making in mobile networks by incorporating energy-related metrics, such as battery level, energy consumption rate, and environmental context, to make informed handover decisions that balance connectivity quality and energy efficiency. Unlike traditional methods that primarily focus on signal strength and network load, our approach addresses the critical need for energy efficiency, particularly in high-mobility scenarios. This innovative framework not only enhances connectivity but also significantly improves power consumption management, offering a more sustainable solution for modern mobile networks. Through extensive simulations, we demonstrate the effectiveness of our proposed solution in reducing energy usage without compromising network performance. The results reveal significant improvements in energy savings for mobile devices, especially under high-mobility scenarios and varying network conditions. By prioritizing energy-efficient handovers, our approach not only extends the battery life of mobile devices but also contributes to the overall sustainability of mobile networks. This paper underscores the importance of incorporating energy metrics into handover decisions and sets the stage for future research in energy-aware network management.

Beyond conventional models: integer and fractional order analysis of nonlinear Michaelis-Menten kinetics in immobilised enzyme reactors

PurposeThis study explores the immobilisation of enzymes within porous catalysts of various geometries, including spheres, cylinders and flat pellets. The objective is to understand the irreversible Michaelis-Menten kinetic process within immobilised enzymes through advanced mathematical modelling.Design/methodology/approachMathematical models were developed based on reaction-diffusion equations incorporating nonlinear variables associated with Michaelis-Menten kinetics. This research introduces fractional derivatives to investigate enzyme reaction kinetics, addressing a significant gap in the existing literature. A novel approximation method, based on the independent polynomials of the complete bipartite graph, is employed to explore solutions for substrate concentration and effectiveness factor across a spectrum of parameter values. The analytical solutions generated through the bipartite polynomial approximation method (BPAM) are rigorously tested against established methods, including the Bernoulli wavelet method (BWM), Taylor series method (TSM), Adomian decomposition method (ADM) and fourth-order Runge-Kutta method (RKM).FindingsThe study identifies two main findings. Firstly, the behaviour of dimensionless substrate concentration with distance is analysed for planar, cylindrical and spherical catalysts using both integer and fractional order Michaelis-Menten modelling. Secondly, the research investigates the variability of the dimensionless effectiveness factor with the Thiele modulus.Research limitations/implicationsThe study primarily focuses on mathematical modelling and theoretical analysis, with limited experimental validation. Future research should involve more extensive experimental verification to corroborate the findings. Additionally, the study assumes ideal conditions and uniform catalyst properties, which may not fully reflect real-world complexities. Incorporating factors such as mass transfer limitations, non-uniform catalyst structures and enzyme deactivation kinetics could enhance the model’s accuracy and broaden its applicability. Furthermore, extending the analysis to include multi-enzyme systems and complex reaction networks would provide a more comprehensive understanding of biocatalytic processes.Practical implicationsThe validated bipartite polynomial approximation method presents a practical tool for optimizing enzyme reactor design and operation in industrial settings. By accurately predicting substrate concentration and effectiveness factor, this approach enables efficient utilization of immobilised enzymes within porous catalysts. Implementation of these findings can lead to enhanced process efficiency, reduced operating costs and improved product yields in various biocatalytic applications such as pharmaceuticals, food processing and biofuel production. Additionally, this research fosters innovation in enzyme immobilisation techniques, offering practical insights for engineers and researchers striving to develop sustainable and economically viable bioprocesses.Social implicationsThe advancement of enzyme immobilisation techniques holds promise for addressing societal challenges such as sustainable production, environmental protection and healthcare. By enabling more efficient biocatalytic processes, this research contributes to reducing industrial waste, minimizing energy consumption and enhancing access to pharmaceuticals and bio-based products. Moreover, the development of eco-friendly manufacturing practices through biocatalysis aligns with global efforts towards sustainability and mitigating climate change. The widespread adoption of these technologies can foster a more environmentally conscious society while stimulating economic growth and innovation in biotechnology and related industries.Originality/valueThis study offers a pioneering approximation method using the independent polynomials of the complete bipartite graph to investigate enzyme reaction kinetics. The comprehensive validation of this method through comparison with established solution techniques ensures its reliability and accuracy. The findings hold promise for advancing the field of biocatalysts and provide valuable insights for designing efficient enzyme reactors.

A 3D self-floating evaporator loaded with phase change energy storage materials for all-weather desalination

Today, humanity is confronted with two interrelated crises: water and energy scarcity. The utilization of solar energy to evaporate seawater enables the acquisition of freshwater resources while minimizing energy consumption. However, the solar energy hitting the ground is associated with different weather patterns and the alternation of day and night. This work is based on chitosan (CS) aerogel, loaded with phase change material octadecane (ODE) and coated with epoxy resin glue and electrospinning fiber photothermal layer for continuous evaporator operation. Using the characteristics of phase change materials, the ODE undergoes a change from solidification to liquefaction, during which it absorbs and stores energy at elevated temperatures. Conversely, when the temperature decreases, the ODE reverts to solidification and releases stored energy. This enables the evaporator to be utilized in a broader range of scenarios, thereby enhancing the continuity of evaporation. The temperature of the evaporator remained higher than the initial temperature 2 h after the light source was switched off, achieving a light absorption of 94 % and an evaporation rate of 1.74 kg m−2 h−1. Furthermore, it exhibits purification capabilities for different kinds of solutions. The interfacial evaporator is capable of facilitating all-weather evaporation, and it has the potential to become a pivotal instrument in the domain of seawater desalination, with a vast array of applications.

Analysis of magneto-natural convection and second law of thermodynamics of Cu-Al2O3-hybrid nanofluid in a vertical wavy cabinet having a localized non-uniform heat source

Improvements in thermal transference rate and minimization of entropy generation are critical issues in many industrial and engineering applications where heat transfer coefficient is essential to achieve optimal performance and minimal energy consumption. In the present investigation, a computational analysis has been conducted to analyze the impact of magnetic field on magneto-free convection and entropy generation inside a vertical wavy cabinet filled by Cu-Al2O3/H2O hybrid nanosuspension. In the geometry of this model, vertical boundaries of the chamber are designed as wavy and kept as cooled temperature (Tc), whereas the horizontal boundaries are modeled as square and are designed to be thermally adiabatic, except the centrally heated portion on the bottom wall. The centered bottom heated section is supposed to be isothermal with linearly changing temperature, and it changes linearly from Th1 to Th2, whereas it is modelled as a non-isothermal condition. The main focus, in this investigation, is on the entire chamber filled with the combination of hybrid nanoparticles regarded the volumetric concentration of copper and alumina nanoparticles (Cu-50 %+Al2O3-50 %) along with base liquid (H2O). The transformed dimensionless partial differential equations were solved by using finite volume method (FVM) with power law differencing scheme. The simulated flow and temperature fields are scrutinized by streamlines, isotherms, entropy formations, total entropy generation and average convective Nusselt number associated with varying number of undulations (0 ≤ N≤3) volume fraction of hybrid nano liquid (0 ≤ φ ≤ 0.04) magnetic orientation angles (0° ≤ ς ≤ 135°), Hartmann number (Ha = 0 and 50), and dimensionless temperature drop (Ω = 0.001–0.1). From this study, it is found that the average Nusselt number and entropy generation increase as the solid volume fraction of hybrid nanoadditives increases, while opposite trend is observed when the values of Hartmann number rises. The findings show that growth of undulation number (N) causes the reduction of convective heat transfer rate as well as average entropy production intensity. The obtained outcomes concluded that hybrid nano liquid is more effective and finest choice for the improvement of cooling of heat transfer process and minimal energy loss than single nanoliquid. According to the new proposed criterion (TPC), the results show that square-shaped enclosure (N=0) manifests the better cooling performance among the considered governing parameters. The novelty of this study is to scrutinize the flat/ vertical wavy boundaries of the cabinet which can be represented as a model of heat transfer controller, in which it manages the thermal and flow field characteristics. Predominantly, the isothermal line source is used to boost the overall convective heat transfer which is applied in buildings and fluid-mounted heaters. Additionally, many investigators have focused only on the enhancement of heat transfer. As it is known, to increase the thermodynamic efficiency of a system, optimization of entropy generation is an excellent tool which reduces the loss of energy. Hence, it recently does a good engineering sense to highlight an entropy irreversibility.

Enhancing network lifetime in wireless sensor networks through coverage-aware optimized sensor activation

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures.

Minimizing Energy Consumption in Cell-Free Massive MIMO Networks

Minimizing Energy Consumption in Cell-Free Massive MIMO Networks