Energy Efficient Techniques Research Articles

Life Cycle Assessment of Construction Components of Schools in Southern Brazil

Introducing energy efficiency techniques in schools can significantly reduce energy consumption, as Brazil has a large public education system. The main objective of this work was to select the set of construction components with the lowest environmental impact for use in schools in Florianópolis, southern Brazil, through life cycle assessment (LCA). Two types of walls, four types of roofs, and two types of window frames were studied. Ceramic bricks measuring 14x9x19cm and 9x19x19cm were considered for the walls. Wood and aluminium were used for the window frames, with single glass panes on all windows. For the roofs, fibre cement tiles with a PVC ceiling, a drywall ceiling, and a concrete slab were considered, as well as ceramic tiles with a PVC ceiling. Computer simulations were conducted using the EnergyPlus program in order to determine the building’s energy consumption. SimaPro was used to run the LCA. The construction of the building and one year of its energy consumption were analysed to select the combination of components with the lowest impact on the building’s life cycle. Finally, the set consisting of 9x19x19cm ceramic brick walls, wooden frames, and roof with fibre cement tiles and PVC ceiling presented the lowest environmental impact. Keywords: Life Cycle Assessment; public schools; computer simulation; buildings; EnergyPlus; SimaPro

A Comprehensive Review of Energy-Efficient Techniques for UAV-Assisted Industrial Wireless Networks

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation.

Pseudocapacitive Na ion storage in binder-less, carbon additive-free Nb2O5-x electrode synthesised via solvothermal-assisted electro-coating with enhanced areal capacitance and lowered impedance parameters

Negatrodes with wide negative operating voltage, high electrochemical storage capacity, and intrinsic metal ion intercalation abilities are vital to the continuous development of storage devices with simultaneous energy and power density improvement. Herein, we report binder-less coating of non-stoichiometric vacancy-implanted Nb2O5-x as carbon additive-free negatrode on FTO substrate for asymmetric supercapacitor and sodium ion capacitor applications. The negatrode was fabricated through vacuum-less and low-temperature solvothermal-assisted electro-coating technique and yielded several orders of enhancement in its areal capacitance and retained ca. 90 % of its capacity after 5000 cycles of charge-discharge in aqueous Na+ electrolyte. The solvothermal treated electro-coated electrode (STT_Nb2O5-x) achieved an areal capacitance of 22.58 mF/cm2, which was far higher than those of hydrothermal-treated electro-coated HTT_Nb2O5 and Nb2O5 electrodes. The solvothermal treatment simultaneously enhanced the electro-coated samples' impedance properties and mass load through oxygen vacancy implantation and re-crystallization of the electro-coated Nb2O5 layer, respectively. This study presented a facile and energy-efficient technique of direct coating of defect-enhanced pseudocapacitive nanomaterials for the fabrication of electrochemical storage devices.

Design and analysis of low power sense amplifier for static random access memory

<p>Today’s era is a digital world where each and every section of the society is experiencing and encountering with semiconductor chips. In very large-scale integration (VLSI) circuits the design of static random-access memory (SRAM) plays a crucial role in ensuring both low-power consumption and high-speed performance. The sense amplifiers (SA) are integral parts for information accessing storage in SRAM IC design. This paper introduces a dual voltage latch sense amplifier (DVLSA) for SRAM integrated circuits (IC). The comparative analyses of various SA are studied and then design a low-power SA through the implementation of energy-efficient technique. Further, we have elucidated the causes of delay and power dissipation in different SA with useful solutions and performance evaluation is conducted by comparing the proposed design with existing SA reported in the literature. The performance parameters such as power 1.604 uw, energy 470.50 fJ, delay 80.04 ps, and current 5.406 are scrutinized to assess the efficiency of the designs. The cell outcomes have been validated with cadence tool on 180 nm technology and operate at 1.8 V. The proposed design, namely, DVLSA demonstrates minimal energy consumption and low power dissipation, making it a promising advancement in SRAM IC technology.</p>

Highly selective alginate/CuO mixed matrix membranes for efficient dehydration pervaporation of various organic solvents

Pervaporation is an energy-efficient technique for dehydrating organic solvents from aqueous mixtures. To develop a highly selective pervaporation membrane with broad usage, we incorporated three types of CuO particles (solid CuO-S, sea urchin-like CuO-U, and porous CuO-P) into sodium alginate (Alg) for fabricating the Alg/CuO mixed matrix membranes (MMMs). The CuO syntheses and membrane preparation are simple and eco-friendly. The synthesized particles and membranes were systematically characterized. The improved hydrophilic feature of Alg/CuO MMMs was validated from the reduced water contact angle and higher water swelling ratio with increasing CuO wt%. When the Alg/CuO MMMs were applied to the pervaporation of 30 wt% water/isopropanol (IPA) at 25 ℃, both the permeation flux and separation factor were enhanced significantly compared to the pure Alg membrane. The separation efficiency followed the order of Alg/CuO-S MMMs < Alg/CuO-U MMMs < Alg/CuO-P MMMs, in good agreement with their water swelling tendency. The incorporation of 3 wt% porous CuO-P particles in MMM achieved the optimal dehydration efficacy (normalized total flux of 3.5 kg m-2h−1, separation factor of ca. 5000, and water purity of 99.96 wt%, with a stable performance over 168 h). A further larger loading wt% led to a decreased separation selectivity due to more void formation from particle aggregation. With an increase in feed temperature, both the permeation flux and separation factor were improved; however, the increase in feed water concentration deteriorated the separation selectivity while enhancing the flux. Furthermore, the high selectivity and broad applicability of Alg/3% CuO-P MMM were fruitfully demonstrated by exhibiting superior separation efficiencies in dehydrating various polar aprotic solvents compared with several membranes discussed in the literature.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

An advanced hybrid deep learning model for accurate energy load prediction in smart building

In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph neural network (GNN), Transformer and Fusion Layer architectures for precise energy load forecasting. Better feature extraction results from the Improved-CNN's dilated convolution and residual block accommodation of wide receptive fields reduced the vanishing gradient problem. By capturing temporal links in both directions, Bi-LSTM networks help to better grasp complicated energy use patterns. Graph neural networks improve predictive capacities across linked systems by characterizing the spatial relationships between energy-consuming units in smart cities. Emphasizing critical trends to guarantee reliable forecasts, transformer models use attention methods to manage long-term dependencies in energy consumption data. Combining CNN, Bi-LSTM, Transformer and GNN component predictions in a Fusion Layer synthesizes numerous data representations to increase accuracy. With Root Mean Square Error of 5.7532 Wh, Mean Absolute Percentage Error of 3.5001%, Mean Absolute Error of 6.7532 Wh and R2 of 0.9701, the hybrid model fared better than other models on the ‘Electric Power Consumption’ Kaggle dataset. This work develops a realistic model that helps informed decision-making and enhances energy efficiency techniques, promoting energy load forecasting in smart cities.

Numerical study on the mechanism of microplastic separation from water by cyclonic air flotation

Microplastics have attracted considerable attention as emerging contaminants that threaten water bodies. The removal of microplastics from a mini-hydrocyclone, enhanced by air flotation, was studied numerically. The three-phase flow was modeled using the Eulerian-Eulerian model coupled with interphase interactions. The characteristics of the flow field and distribution of microplastics and microbubbles were discussed, and the mechanism of cyclonic air flotation separation was analyzed. It was found that injecting microbubbles accelerated the axial migration of microplastics and moved the enriched area upward toward the overflow. The coalescence rate of the bubbles near the axis was higher than their breakage rate, which led to the formation of an air core. The length and diameter of the air core increased with the inlet gas holdup. When the air core size closely matched the overflow, the constrained flow channel prevented the discharge of microplastics. The optimal air holdup must be determined to ensure the efficiency of the cyclonic air flotation process. The sizes of the microbubbles used for cyclonic air flotation should be comparable to those of the separated microplastics. The upper cone angle significantly promoted the migration of microplastics to the axis. This study was conducted to purify microplastic-containing wastewater using an environmentally friendly and energy-efficient technique and to provide a theoretical basis and practical reference for applying microplastic separation technology in water.

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

Experimental Determination of Mechanical Properties and Characterization of Selected Crop Residues

The management of huge waste generated from crop harvesting and processing has continued to create challenges and constitute an environmental nuisance. Inappropriate disposal and open-air burning of crop residues exacerbate environmental pollution, escalate bush burning and deforestation, and impact human health. Mixing, processing, and conversion of crop residues to form useful composites for various applications remain one of the economical, eco-friendly, and sustainable strategies for its management. The study constructed composites by mixing different ratios of unripe plantain peel (UPP) and coconut fibre (CCF) with an appropriate binder and hardener. The fabricated composites were subjected to mechanical, compositional, and morphological analyses. The outcomes of the tests show that the hardness, tensile strength, and impact strength only UPP is 97.8 RHN, 411 MPa, and 9 818 J/m2, respectively while the CCF/UPP composite is 98.5 RHN, 538 MPa, and 12 273 J/m2, respectively. The wear rate of UPP is 0.56 cm3/m while that of the CFF/UPP composite is as high as 0.73 cm3/m and increases with increased load. Silicon, oxygen, and aluminium are the major constituents of the composite samples as revealed by the compositional analysis. The tensile strength, hardness, impact, and wear rate of UPP can be boosted by the blending of CFF to form homogenous composites. The outcome of this study will deepen the literature and escalate research into the conversion and utilization of crop residues for diverse applications. The usage of innovative technologies and energy-efficient techniques should be adopted for the processing, modification, and conversion of crop residues.

Surfactant-Mediated Microalgal Flocculation: Process Efficiency and Kinetic Modelling.

Microalgae are a valuable source of lipids, proteins, and pigments, but there are challenges in large-scale production, especially in harvesting. Existing methods lack proven efficacy and cost-effectiveness. However, flocculation, an energy-efficient technique, is emerging as a promising solution. Integrating surfactants enhances microalgal harvesting and disruption simultaneously, reducing processing costs. This study investigated cetyltrimethylammonium bromide (CTAB), dodecyltrimethylammonium bromide (DTAB), and sodium dodecyl sulphate (SDS) for harvesting Tetraselmis sp. strains (75LG and 46NLG). CTAB exhibits superior results, with 88% harvesting efficiency at 1500 and 2000 mg L-1 for 75LG and 46NLG, respectively, for 60 min of sedimentation-thus being able to reduce the operating time. Beyond evaluating harvesting efficiency, our study explored the kinetics of the process; the modified Gompertz model led to the best fit. Furthermore, the largest kinetic constants were observed with CTAB, thus highlighting its efficacy in optimising the microalgal harvesting process. With the incorporation of the suggested enhancements, which should be addressed in future work, CTAB could hold the potential to optimise microalgal harvesting for cost-effective and sustainable large-scale production, eventually unlocking the commercial potential of microalgae for biodiesel production.

Energy Efficient AODV routing protocols for FANETs

All system units in flying UAV like transmitter, receiver equipment, control unit, information processing unit and payloads are powered by in build power sources. UAVs equipped with the limited on-board energy capability restrict the flying time, which will significantly affect the performance of a FANETs. Optimizing the energy consumption among nodes is one of the important research challenges. Optimization techniques for energy usage can be implemented at different OSI layer level. In our study we focused on OSI networks layers solution for performance improvements based on energy efficient routing techniques in flying ad hoc network environments. Routing algorithm that is used in MANETs applications can be optimized and used in FANETS as in both networks majority of the operational characteristics similar. But Few characteristics in FANETs like distance coverage, node mobility velocity, capacity and types of power supply used differentiate from other mobiles networks (VANETs,MANETs). During first phase of our work we evaluated the performance of classical routing protocols AODV, DSR, DSDV in FANETs using energy metrics. Result in first phase revel that AODV performance is superior to other protocols in FANETs. In second phase we designed EEAODV protocols which use energy metrics in addition to hop count for path optimization. Finally details performance comparison study between EEAODV and AODV performed, the result shows that EEAODV performance is much better that AODV. For our performance evaluation we used NS-3 simulator and random waypoint mobility models.

A review on energy efficiency techniques used in machining for combined generation units

A review on energy efficiency techniques used in machining for combined generation units

Extraction of metal values from iron-rich mine tailings via chloridized roasting and water leaching

The surge in mining and mineral processing to meet metal demand has led to accumulation of mining by-products like tailings, waste rocks, and acid mine drainage, containing high iron, copper, nickel, and zinc concentrations. These residues pose environmental threats. The concept of technospheric mining aims to repurpose these wastes for beneficial uses, reducing disposal and ecological impact while benefiting stakeholders. Within this context, the present investigation focuses on the discriminative retrieval of copper and nickel from mine tailings characterized by elevated iron content. This is achieved through formulation of an energy-efficient technique designed to surmount the challenge of iron co-extraction during hydrometallurgical processes aimed at extracting and recuperating metal values from intricate phases. The proposed method entails the amalgamation of mine tailings and solid NH4Cl in a stoichiometric ratio of 1:2, followed by roasting at 300 °C for a span of 1 h. Application of chloridized roasting orchestrates the transformation of copper and nickel into soluble metal chlorides, while concurrently inducing conversion of iron into a stable iron oxide state. Subsequent stages encompass the separation of copper and nickel from the water leach liquor, achieved through solvent extraction techniques utilizing LIX84I and Cyanex 272 as extractants. Rigorous characterization efforts, employing techniques such as X-ray Diffraction (XRD) and Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS), are undertaken to elucidate the attributes of the raw material and solid residues. Remarkably, the comprehensive isolation of copper, nickel, and iron is realized without generation of any effluents, ensuring preservation of environmental equilibrium.

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

The reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

Generalized index and mode modulated OFDM with improved spectral and energy efficiency

AbstractRecently developed trends in wireless communication encounter extremely high surge in data traffic, which makes it inevitable to employ energy efficient techniques to reduce detrimental consequence of carbon emission over society. This concern has motivated us to upgrade our previous work on index and mode modulated orthogonal frequency division multiplexing (IMM‐OFDM) for achieving dual benefits in terms of spectral and energy efficiency. In this paper, we have proposed a generalized index and mode modulated OFDM scheme with variable number of subcarrier activation. Novelty of this generalized scheme is its capability to act as a unified model for classic OFDM and three other benchmark index modulated OFDM schemes along with our previously proposed IMM‐OFDM scheme. This new scheme outperforms those five descendent schemes in terms of energy efficiency and error performance as indicated by simulation results. Spectral efficiency improvement in this scheme is achieved through optimum sets of active subcarrier number, which are determined to gain most optimized trade‐off between spectral and energy efficiency with least detector complexity. This generalized parent scheme can replace individual models of its five descendent schemes and consequently can be considered to be one of the most promising candidates for next generation mobile communication system.

Energy Efficient Business Management System for Improving QoS in Network Model

Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.

Photothermal assisted space-confined chlorine activation for removal of pharmaceuticals and personal care products coupled with disinfection by-products control

The solar/chlorine process provides an energy-efficient technique for the abatement of some pharmaceuticals and personal care products (PPCPs) but it is inefficient in degrading PPCPs lacking electron-donating group and suffers high formation of disinfection byproducts (DBPs). In this study, a photothermal approach is developed to promote chlorine activation (photothermal/chlorine) under solar radiation to effectively remove a wide range of structurally diverse PPCPs and to control DBPs formation. To this end, a carbon black (CB) coated 3D porous melamine sponge (MS), namely CB@MS, is developed for the photothermal/chlorine application, achieving significantly enhanced PPCPs degradation with an ibuprofen (IBU) removal rate constant (0.22 min−1) 3.76 times that of the solar/chlorine process (0.058 min−1). Mechanistic investigations revealed that CB@MS confines the thermal energy within the 3D porous structure to achieve high localized heating for boosting PPCPs degradation in the confined space, which is equivalent to the thermal effect achieved by the solar/chlorine process at constant solution temperature of around 70 °C. Moreover, the photothermal/chlorine process reduces TOCl and DBPs formation by 77.6 % and 67.5 %, ascribed to adsorption by CB@MS, less chlorine exposure, and altered PPCPs degradation pathways. The broad applicability in various water matrices, and good reusability and stability of the photothermal/chlorine system also ensure its excellent practicality. This study offers in-depth mechanisms and practical insights into the development of a novel photothermal/chlorine process for PPCPs abatement and DBPs control.

Energy-Efficient Fog Computing: A Review and Future Directions

Fog computing has emerged as a promising paradigm to meet the increasing demands of latency-sensitive and bandwidth-intensive applications in the era of the Internet of Things (IoT) and edge computing. However, the proliferation of fog nodes introduces significant energy consumption challenges, particularly in resource-constrained environments. This paper presents a comprehensive review of energy-efficient techniques in fog computing, focusing on optimization strategies to minimize energy consumption while meeting application requirements. We categorize existing research efforts based on their approaches, including task scheduling, resource allocation, communication protocols, and energy harvesting techniques. Additionally, we identify key open challenges and propose future research directions to further enhance energy efficiency in fog computing environments.

Green Building: Energy Efficiency Optimisation Strategies using MATLAB

Abstract: This research paper explores the integration of optimization techniques in the realm of green building construction, aiming to minimize environmental impact while maximizing resource efficiency. With the pressing need to mitigate climate change and reduce the carbon footprint of the construction industry, the study investigates various methodologies for optimizing the design, materials, and processes involved in sustainable building practices. The paper delves into the core principles of green building, emphasizing the importance of energy efficiency, renewable resources, and innovative construction techniques. It analyzes the application of optimization models, such as life cycle assessment (LCA), Building Information Modeling (BIM), and parametric design, in streamlining decision- making processes throughout the building life cycle. Moreover, the research assesses case studies and real-world implementations of optimization strategies in green building projects across different geographical locations and scales. It examines the economic feasibility, environmental benefits, and social impacts of these strategies, emphasizing the need for a holistic approach to sustainable construction. By synthesizing existing knowledge and exploring novel approaches, this paper aims to provide insights into the role of optimization in advancing the goals of green building, ultimately contributing to the creation of more ecofriendly and resilient built environments