Minimum Energy Utilization Research Articles

Inexpensive advanced diatomite evaporator with optimal structure and density for efficient seawater desalination

The global water-scarcity crisis, exacerbated by population growth and pollution, necessitates the securing of freshwater resources. Recently, interfacial solar steam generation (ISSG) has emerged as a promising and cost-effective seawater-desalination technology with minimal secondary contamination. However, it employs high-end materials and complex structures, limiting its practical applicability. We develop an advanced diatomite evaporator (ADE) by incorporating inexpensive porous materials, such as diatomite, natural cement, and graphene nanoplatelets, which are readily available and replicable. To maximize evaporation efficiency while minimizing energy utilization, the structural density of the ADE is optimized during fabrication. Capillary-rise and geometric-modification experiments show that heat loss increases with temperature as the ADE diameter increases and the evaporation rate above a particular height saturates because of insufficient water supply. The optimal aspect ratio (height/diameter) of the ADE is approximately 4.8, with an evaporation rate of approximately 6.30 kg m−2h−1. Through the optimization of composition density and viscosity in the diatomite-cement dough, ADE achieved superior mechanical strength and durability, offering significant advantages for long-term and repeated use. The proposed ADE sustains evaporation performance for > 16 h and maintains stability after 10 repeated desalination cycles. This study will contribute to the development of a cost-effective and practical ISSG technology.

Clean Energy Solutions in Data Centers: Leveraging Advanced Materials and AI for Sustainable DevOps Operations

This paper examines how artificial intelligence (AI) and advanced engineering materials contribute to reduced energy consumption in data centers, a key consideration for sustainable DevOps. The topic of study is to use AI to introduce efficiency within data center operations such as maintenance, workload distribution, and cooling systems. Furthermore, the paper discusses the effectiveness of integrating different materials within buildings, including phase change materials and cutting-edge thermal management approaches to minimize energy utilization. Simulation findings reveal the potential to achieve higher efficiency levels in energy use when AI and Advanced Materials are incorporated. Such findings are supported by real-time examples that illustrate its applicability and usefulness in actual working environments. It also examines challenges regarding deployment and reproducibility, presenting suggestions intended to enhance the sustainability of the data centers via technological developments.

Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management

Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management

Automatic Migration-Enabled Dynamic Resource Management for Containerized Workload

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 1.18$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 2.35$</tex-math></inline-formula> compared to the baseline methods while maintaining similar performance.

The fragmented 3D-covalent organic framework in cellulose acetate membrane for efficient phenol removal

The membrane-based separation of molecular pollutants has several advantages such as minimum energy utilization, recyclability, and commercial viability. However, the membrane fabrications should be in line with the suitable porosity, functionality, mechanical strength, and economical for their practical applications. Herein, we report a novel hybrid membrane (TamDbta-CA) of low-cost cellulose acetate (CA) polymer and functionally diverse porous defective 3D-COF (TamDbta) for the removal of toxic phenol from industrially relevant synthetic oil-produced water. The low percentage (0.8%) use of TamDbta as pillars in CA-membranes improved the porosity, hydrophilicity, mechanical strength, and separation efficiency of phenol (∼80 to 90 %). The defective functional sites (–C=O and –Br) and nanoporosity of TamDbta and its uniform distribution throughout the mixed-matrix membrane contributed to the enhancement of membrane performance. The strategic membrane design and its efficient use for removing toxic pollutants may pave the way for using COFs as a pillar in commercially viable membranes.

Enhancement of R600a vapour compression refrigeration system with MWCNT/TiO2 hybrid nano lubricants for net zero emissions building

Net zero emissions building is widely investigated with great environmental care. In the case of refrigeration selection for net zero emissions building (NZEB), the ozone depletion potential is the primary criterion to choose the refrigerant. For achieving the NZEB, the R600a was preferred as it possesses the potential for global warming lower and zero potential for ozone depletion. This paper aims to amplify the coefficient of performance by utilizing MWCNT/TiO2 hybrid Nano lubricants in the R600a vapour compression refrigeration system. As numerous factors and equations are involved in the study and prediction of the Coefficient of Performance in vapour compression refrigeration systems which is comparatively complex and takes more time for promoting the development of precise prediction and results. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. By using the ANFIS technique enhanced cooling effect of 200 W with a 50 % increment was obtained with 0.4 g/L of MWCNT/TiO2 hybrid nano lubricants which is better in comparison with ANN and experimental results. The minimum energy utilization of 90 W was obtained with the ANFIS technique. This method also predicted the enhanced COP of 3.7 with a 32 % increase in comparison to the ANN prediction method. When compared to the ANN prediction model, the ANFIS model's estimated least training error value. The results indicate that when compared to ANN prediction the ANFIS predicted values produced results that were more accurate and were the proper approach for predicting COP parameters and consumed 35 % less energy.

Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS and energy efficiency in the Wireless Sensor Network (WSN). The MS collects the data from the Cluster Head (CH), and the RL incentive values select CH. The incentives value is computed by the QoS parameters such as minimum energy utilization, minimum bandwidth utilization, minimum hop count, and minimum time delay. The MS is used to collect the data from CH, thus minimizing the network delay. The sleep and awake scheduling is used for minimizing the CH dead in the WSN. This work is simulated, and the results show that the RLMQ scheme performs better than the baseline protocol. Results prove that RLMQ increased the residual energy, throughput and minimized the network delay in the WSN.

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated considerable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications. The difficulty stems from features other than mobile ad-hoc network (MANET), namely aerial mobility in three-dimensional space and often changing topology. In the UAV network, a single node serves as a forwarding, transmitting, and receiving node at the same time. Typically, the communication path is multi-hop, and routing significantly affects the network’s performance. A lot of effort should be invested in performance analysis for selecting the optimum routing system. With this motivation, this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication (COAER-UAVC) technique. The presented COAER-UAVC technique establishes effective routes for communication between the UAVs. It is primarily based on the coati characteristics in nature: if attacking and hunting iguanas and escaping from predators. Besides, the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay. A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system. The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

Reproducibility of Shade During Dyeing of Cotton/Polyester Fabric by Varying the Process Condition

ABSTRACT In every textile dyeing industry shade matching is the most frequent process and achieving the same shade every time is still a very challenging process. The fabric was dyed with the same recipe after some duration for the same shade but it showed more variation in the shade than the previously dyed fabric. This problem of reproducibility was tried to be minimized by working on some process parameters and the results found were appreciable. To overcome the shade variation numerous trials have been conducted with the dyeing process and recipes. The reproducibility of shades benefitted dyeing to some extent in saving chemicals reducing time minimizing energy utilization with reducing cost generating more accurate results and minimization of shade variations so a program must be developed that can dye the fabric every time without any variation. Temperature and time are the two main factors to be considered along with the selection of chemicals and auxiliaries during dyeing to get minimum shade variation. This paper describes the critical process parameters and their control measures to achieve shade reproducibility in the dyeing of cotton/polyester blend fabric.

RETRACTED: Medical data quality management using butterfly optimization with adaptive threshold sensitive energy‐efficient routing protocol and multidimensional chaotic blowfish encryption in wireless body networks

AbstractNowadays, medical applications require much medical information to analyze various diseases. To collect various medical data, wearable devices collect patient health and medical details using a wireless body area network. The collected details consist of several critical details: oxygen level, heart attack information, blood pressure, airflow and so forth. These details are broadcast to the healthcare centers using wireless technologies to make clinical decisions. During medical information transfer, the data quality and critical events are difficult to maintain because it reduces the packet delivery, transmission delay, and high energy. So, this article introduces bacterial optimization and adaptive threshold‐sensitive energy‐efficient routing protocol (BOATSEE) to transmit the medical data. The method aggregates the sensitive data by selecting the cluster head and effectively broadcasts the critical data. In addition, the optimized method manages the network lifetime and energy using an energy‐efficient method. Also, we have proposed a multidimensional chaotic blowfish encryption (MCBE) algorithm to enhance the system's security. Then the system's efficiency is computed based on metrics like energy consumption, packet delivery ratio, end‐to‐end delay, and QoS metric‐associated restraints. The results reveal that the proposed system is efficient in managing the medical data quality with minimum energy utilization, packet loss rate, delay, and maximum packet delivery ratio when compared with the conventional approaches. Our method also proved to be more secure than the traditional systems.

Design and Analysis of an Energy-Efficient Load Balancing and Bandwidth Aware Adaptive Multipath N-Channel Routing Approach in MANET

Mobile Ad Hoc Network (MANET) is a collection of spontaneous nodes that form a dynamic network without any centralized administration. Routing decisions for mobile communication is a challenging task because the continuous movement of nodes increases the routing overhead and energy consumption. Numerous studies have been conducted in the field of MANET to reduce the routing decision overhead and energy consumption. Their concepts are good for improving efficiency in terms of load balancing, congestion control, and energy consumption. This article provides new concepts that are able to give better outcomes. This paper, proposes an adaptive Multipath Multichannel (N-channel) Energy Efficient (MMEE) routing approach in which route selection strategies are dependent on predictive energy consumption per packet (calculated accustomed data delivery), available bandwidth, queue length, and channel utilization. Multipath provides multiple routes and balances network load, whereas multichannel reduces network collisions via a channel ideal assignment mechanism. In the multichannel mechanism, link bandwidth is divided into multiple sub-channels. Multiple source nodes access the channel bandwidth in a simultaneous manner that minimizes the network collision. The collaborative multipath multichannel mechanism provides more than one path between a single source or multiple sources to the destination without collision and congestion. The path selection is based on the MMEE routing approach. In the proposed MMEE, a load and bandwidth aware routing method selects the path, based on node energy and predicts their lifetime, which increases the network reliability. The result shows the comparative analysis between various multichannel medium access techniques such as MMAC, Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning based Multipath Routing (QMR), Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV), and the proposed MMEE method. The outcome concludes that the MMEE method performs better than other schemes. The proposed MMEE protocol provides better performance in terms of percentage of data received, throughput, and minimum energy utilization.

Lifetime Maximization of Heterogeneous WSN Using Fuzzy-based Clustering

Background: Wireless Sensor Network (WSN) is an arising field for research and development. It has various applications ranging from environmental monitoring to battlefield surveillance and more. WSN is a collection of multiple sensor nodes used for sensing the environment. But these sensing nodes are deployed in such areas where it is not that easy to reach, so battery used in these nodes becomes quite impossible to change, so we need to utilize this energy to get the maximum sensing for a long time. Objective: To use the Fuzzy approach in the clustering algorithm. Clustering is a key approach to prolong the network lifetime with minimum energy utilization. In this paper, our main concern is on the Cluster Head (CH) selection. So, we are proposing a clustering algorithm which is based on some of the attributes: Average Residual Energy of CHs, Average Distance from nodes to CHs, Standard Deviation of member nodes, and Average Distance from CH to Base Station(BS). Methods: Initially, some of the nodes are found having greater residual energy than the average network energy, and fifteen populations are made each having an optimum number of CHs. The final and best CHs set is chosen by determining the maximum fitness value using a fuzzy approach. Result: The result positively supports the energy-efficient utilization with lifetime maximization, which is compared with the Base algorithm [1] and LEACH [2] protocol based on residual energy and the number of nodes that die after performing some rounds. Conclusion: The proposed algorithm determines a fuzzy-based fitness value, provides load-balancing among all the networking nodes, and performs a selection of best Cluster Heads, resulting in prolonged network lifetime and maximized efficiency.

Dynamic Spectrum Allocation Access Using Cognitive Radio Networks in a Maritime

The maritime environment is unique due to radio wave propagation over water, surface reflection and wave obstruction. In dealing with the challenging maritime environment, a dynamic spectrum allocation access using cognitive radio network through optimization is proposed. Existing works in this area are limited in performance due to the long duration in achieving the probability of false alarm. Matched filtering technique which is known as the optimum method for detection of primary users (PUs) faces the challenge of large power consumption as various receiver’s algorithm are needed to be executed for detection. This work provides a platform that enables minimum energy utilization by secondary users (SUs) thereby, enhancing throughput. An algorithm for throughput maximum in spectrum allocation was developed and used based on demand based model. The implementation of the developed model was carried out using Java program and the spectrum analysis using long distance path loss model and adaptive modulation code to estimate the minimum bandwidth of the secondary users. A simulation of cognitive radio mesh network for the testing and validation of the demand based algorithm preference, and also the cognitive radio network traffic was carried out using Cisco packet tracer and results shown on MATLAB. Simulation results indicate that using the demand based algorithm, the throughput rose with time and almost stabilized. This increase and steady throughput indicates effectiveness in the algorithm which shows that the PUs and SUs activities increase as holes’ detection effort varies, unlike that of genetic algorithm where the throughput rose gradually, got to a peak value at certain time and then fell which indicates instability in the variation of the throughput. Also, the average throughput of the demand based algorithm is far greater than that of genetic algorithm which shows that demand based algorithm outperforms the genetic by a far greater percentage. The percentage of optimization is approximately 26%.

Energy Minimization Algorithm for Estimation of Clock Skew and Reception Window Selection in Wireless Networks.

The synchronization of time between devices is one of the more important and challenging problems in wireless networks. We discuss the problem of maximization of the probability of receiving a message from a device using a limited listening time window to minimize energy utilization. We propose a solution to two important problems in wireless networks of battery-powered devices: a method of establishing a connection with a device that has been disconnected from the system for a long time and developed unknown skew and also two approaches to follow-up clock synchronization using the confidence interval method. We start with the analysis of measurements of clock skew. The algorithms are evaluated using extensive simulations and we discuss the selection of parameters balancing between minimizing the energy utilization and maximizing the probability of reception of the message. We show that the selection of a time window of growing size requires less energy to receive a packet than using the same size of time window repeated multiple times. The shifting of reception windows can further decrease the energy cost if lower packet reception probability is acceptable. We also propose and evaluate an algorithm scaling the reception window size to the interval between the packet transmission.

A review on exergy analysis of ejector refrigeration system

Owing to the human comfort, the utilization of refrigeration system has increased nowadays. In today’s scenario, the air conditioning system has led to the innovative changes in the lifestyle of human beings. The Heat driven ejector refrigeration machine is a favorable alternative to a compression based refrigeration system for the point of view of minimizing energy utilization. As Exergy is directly related to the second law of thermodynamics and the system performance can be only evaluated on the basis of this thermodynamics law. On the basis of efficient performance, it has been concluded that good quality of ejector is more performance oriented rather than other components like condenser, generator and evaporator. This paper presents the idea of ejector refrigeration systems, its exergy analysis and working fluids used. It profoundly investigates the ejector technology and behaviour, exergy analysis, properties of refrigerant and its effect on the ejector refrigeration system.

Application of virtual machine consolidation in cloud computing systems

Cloud systems play a vital and significant role in our daily lives due to various internet services. For instance, email services, social networks, and others. Consequently, their energy consumption has also become an increasingly essential concern in Cloud Computing Systems (CCSs). Virtualization technologies are widely used to facilitate the management of CCSs and reduce their energy consumption. Virtualization enables live migration of Virtual Machines (VMs) where several VMs can be loaded on some Physical machines (PMs) called VM consolidation. A VM consolidation algorithm can be an effective technique for reducing energy consumption, operational cost, hardware cost, Service Level Agreements (SLAs) compliance/violation, CO2 emissions, and enhancing the hardware and service reliability, performance, and hardware lifetime, load balancing, and utilization in CCSs. Essentially, VM consolidation must minimize energy utilization and service quality in the cloud system. This study presents a taxonomy comprising resource assignment method, metrics, objective functions, migration methods, algorithmic methods, co-location criteria of VMs, architectures, workload dataset, and evaluation approaches in VM consolidation CCSs. Also, we reviewed related work regarding the resources of PMs, algorithm methods, metrics, architectures, and the objectives in static/dynamic VM consolidation.

Energy and Exergy Efficiencies of Fluidized and Fixed Bed Rice Drying

HighlightsFluidized bed drying of rough riceat 40°C with or without ambient air dehumidification worked best based on the energy and exergy utilization.The dryer lost exergy in the exit air, which was the primary cause of thermal inefficiency; recirculation of the exit air could improve the exergy efficiency.Ambient air dehumidification did not reduce the dryer’s energy utilization and exergy efficiency for rough rice.Abstract. Fluidized bed drying of rough rice in the U.S. has not been used to its full potential due to a lack of research to address rice quality impacts and energy consumption. Little research has been done to analyze the energy and exergy of fluidized bed drying of rough rice. Thermal analysis allows using the drying air’s energy better and improving the drying system’s thermal efficiency. In this study, energy utilization and energy utilization ratio were calculated using the first law of thermodynamics, while exergy loss and exergy efficiency were determined using the second law. Drying air temperature (40°C, 45°C, or 50°C), drying bed condition (fluidized or fixed), drying duration (30, 45, or 60 min), and ambient air dehumidification (yes or no) were the tested factors. A lab-scale drying system designed in a previous study was used. Three replicates were performed to minimize any bias or human errors. All factors significantly affected the energy and exergy of the drying process, except dehumidification and replication. The minimum and maximum energy utilization values were 0.01 and 0.55 kJ s-1 for fixed bed drying at 40°C for 30 min with dehumidification and fluidized bed drying at 50°C for 60 min without dehumidification, respectively. The minimum and maximum exergy efficiency values were 13.46% and 49.14% for fixed bed drying at 45°C for 45 min with dehumidification and fluidized bed drying at 40°C for 60 min with dehumidification, respectively. The primary cause of thermal inefficiency was attributed to the energy and exergy losses in the exit air, while the secondary source was the exergy and energy losses from the drying chamber and inlet air pipes. Costly solutions could be recirculation of the exit air and better insulation of the drying chamber and inlet pipes. However, using the optimal drying conditions for the energy and exergy utilization of the drying air is suggested. This study found that fluidized bed drying was better than fixed bed drying overall. At the primary drying stage, fluidized bed drying had a higher exergy efficiency, energy utilization, and energy utilization ratio than fixed bed drying. At 40°C, fluidized bed drying with or without ambient air dehumidification worked best based on the energy and exergy utilization of the drying system. Keywords: Dehumidification, Energy, Exergy, Fixed bed, Fluidized bed, Rice drying.

The Distribution of Solar Radiation and Solar Energy Intensity, and Top Locations for Constructing Solar Energy Stations in Iraq

The relationship between energy consumption and environmental pollution is evident from the exacerbation of widespread negative consequences such as climate change, including global warming, humidity, floods, earthquakes, destructive storms, and uncontrollable natural phenomena. Therefore, it is necessary to shift from fossil fuel consumption to alternative or renewable sources of energy such as solar and wind. Taking advantage of solar cell energy and investing in various fields such as electricity generation and agricultural uses such as water pumping, desalination and concentrating on their applications for the development of rural areas and their use in the development of the industrial sector. As well as encourage the private sector and capital owners to invest in solar cell energy. In our study, we analyze the best solar cell power stations in Iraq. Three cities selected across the country, providing data on distributions of solar energy and the density of electricity generated. Therefore, the research included a methodology suitable for the subject. This research aims to detect the importance of solar energy and how to exploit it through mathematical equations concerning the distributions and density to reach the possibility of the minimum solar energy utilization and then reach the preferred location for the construction of solar cell stations in the study area.

Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems

Due to the recent advances in intelligent transportation systems (ITS), Automatic Train System (ATS) gained significant attention among the research community. An effective ATS offers the whole railway network to operate in a safe, cost-effective and proficient manner against sudden disturbances like temporary platform blockages. Numerous Train Timetable Rescheduling (TTR) models have been presented for managing unforeseen events which might disturb the timetable. The main aim of an effective TTR model is to reduce power utilization by consuming the entire benefits of reproductive braking energy under a random situation. In this view, this paper presents a new TTR model to optimize the energy of metro systems by the incorporation of improved genetic algorithm (IGA) and long short term memory (LSTM) based recurrent neural network (RNN). The proposed method incorporates three different models, namely controller, timetable, and energy models. The proposed method requires minimum time to recompute a new schedule and offers effective solutions instantly after a random disturbance happens. The performance validation of the proposed IGSA-LSTM model is simulated using Chennai Metro Train Station. The proposed method significantly reduces the energy consumption of metro train and reaches to a minimum average energy utilization of 696 kWh.