Energy Efficiency Research Articles

Energy factors affecting environmental pollution for sustainable development goals: The case of India

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic growth, and environmental pollution in India. The primary objective is to understand how these factors interrelate and influence each other, focusing on their implications for sustainable development. The study used data from the World Bank from 2000 to 2023; the methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, and variance decomposition. These econometric techniques were selected due to their ability to capture dynamic relationships, determine causality, and identify long-term equilibrium among the variables. The findings reveal that economic growth significantly increases electricity consumption and fossil fuel usage, leading to higher carbon dioxide emissions. On the other hand, renewable energy adoption reduces environmental pollution. The study also highlights the complex interplay between population growth, urbanization, and trade activities in shaping India's energy demand and environmental outcomes. The implications of these findings are critical for India's sustainable development. The results suggest that while economic growth is essential, it must be balanced with sustainable energy practices to mitigate environmental pollution. The findings emphasize the need for policy interventions that promote renewable energy, enhance energy efficiency, and enforce environmental regulations. Recommendations include accelerating renewable energy adoption, implementing stringent energy efficiency standards, and developing integrated policies that simultaneously address the economic, energy, and environmental dimensions. These actions will help India achieve a sustainable balance between economic growth and environmental protection, ensuring a healthier future for its population.

Edukasi Penerapan Prinsip Akuntansi Lingkungan Melalui Efisiensi Energi dan Pengelolaan Limbah Pada UMKM Tambakrejo Semarang

Management understanding and knowledge of environmental accounting, especially related to waste management and energy efficiency, is very important to achieve business sustainability, including for Micro, Small, and Medium Enterprises (MSMEs). The Tambakrejo MSME group often lacks understanding of milkfish waste management and faces increasing energy operational costs, especially for refrigerators. Lack of environmental awareness causes milkfish waste to pollute the environment and hinder water drains. Environmental accounting integrates environmental aspects in accounting practices to measure, report, and manage environmental impacts. This Community Service educates about waste management strategies and energy efficiency based on environmental accounting principles. The results of Community Service have a positive impact, the enthusiasm of MSME actors is very high, this is evidenced through pre and post testing measuring tools for MSMEs with the results of the increasing level of public understanding of environmental awareness and energy saving in MSMEs, as well as teaching environmental cost management methods through identification, recognition, measurement, reporting, and integration of environmental aspects in the accounting system.

Multi-pronged strategies for enhancing building envelopes toward nearly-zero energy in hot climatic regions

Abstract Growing concerns about climate change and rising energy demands necessitate advancements in building energy efficiency. This study investigates the effectiveness of radiative coatings and thermal insulation, both individually and combined, in reducing energy consumption and carbon footprint for buildings in hot and humid climates. This research contributes to a growing body of knowledge by comprehensively evaluating the combined effects of these strategies. A comparative analysis was conducted using data on energy usage and carbon emissions.
The research highlights the effectiveness of envelope-enhancing techniques in reducing energy consumption and carbon emissions. The application of radiative coating led to a significant 13.1% decrease in energy usage, totaling 681.95 MWh, and corresponding emissions of 482.14 tons of CO2. Radiative coating offers the most cost-effective solution with an LCOS of $0.045/kWh. When integrating thermal insulation with radiative coating, there was a substantial 12.0% reduction in energy consumption, amounting to 690.39 MWh, and emissions of 488.11 tons of CO2. The integrated model provides significant energy savings at a slightly higher LCOS of $0.052/kWh, making it a balanced choice between efficiency and cost-effectiveness compared to using thermal insulation alone. Moreover, the study emphasizes that the combination of Glazing Integrated Photovoltaic (GIPV) with radiative coating can lead to the creation of nearly zero-energy buildings, resulting in a significant energy savings of 34.9%. These results underscore the efficacy of these technologies in achieving significant energy savings and environmental benefits.
This study demonstrates that radiative coatings significantly reduce energy consumption and carbon footprints. The combined method with thermal insulation reduces energy, suggesting further optimization strategies in hot and humid conditions. The results of this investigation recommend utilizing Glazing Integrated Photovoltaic (GIPV) to achieve nearly zero-energy buildings. Such integrated solutions not only improve energy efficiency but also make a substantial contribution to environmental sustainability in the building sector.

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.

Advanced spatial query processing in IoT through mobile agent integration

In IoT environments, various applications and protocols rely on spatial query processing to obtain, analyze, and interpret information from sensor nodes based on their location within a specific area. It enables the efficient monitoring and management of spatial data, which is crucial for applications such as environmental monitoring, smart cities, and disaster response. By querying the location and status of sensor nodes, spatial query processing facilitates real-time decision-making, optimizes resource allocation, and enhances the overall performance of the IoT system.This paper focuses on Window queries, a key spatial query type that retrieves data from sensors within a defined two-dimensional zone. The research aims to improve spatial query processing in IoT environments by proposing a Window query processing method that balances various performance factors such as energy efficiency, latency, accuracy, and query success rate. We introduce a novel approach using mobile agents to enhance spatial query processing by addressing the constraints of sensor networks. Simulation results from the NS-2 simulator demonstrate the effectiveness of our method across different parameters and environments.

Optical circuit switched three-stage twisted-folded Clos-network design model guaranteeing admissible blocking probability

Some data center networks have already started to use optical circuit switching (OCS) with potential performance benefits, including high capacity, low latency, and energy efficiency. This paper addresses a switching network design to maximize the network radix, i.e., the number of terminals connected to the network under the condition that a specified number of identical switches with the size N×N and the maximum admissible blocking probability are given. Previous work presented a two-stage twisted and folded Clos network (TF-Clos) with a blocking probability guarantee for OCS, which has a larger network radix than TF-Clos with a strict-sense non-blocking condition. Expanding the number of stages allows for enhancing the network radix. This paper proposes a model designing an OCS three-stage TF-Clos structure with a blocking probability guarantee to increase the network radix compared to the two-stage TF-Clos. We formulate the problem of obtaining the network configuration that maximizes the network radix as an optimization problem. We conduct an algorithm based on an exhaustive search to obtain a feasible solution satisfying the constraints of the optimization problem. This algorithm identifies the structure with the largest network radix in non-increasing order to avoid unnecessary searches. Numerical results show that the proposed model achieves a larger network radix than the two-stage model.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Ultra-fine nano-crystalline optimize electrostatic energy storage

Grain size pays a crucial role in the properties of ferroelectrics and dielectrics. Reducing the grain size is considered to be an effective mean for enhancing dielectric energy storage. In this work, high recovered energy storage density and efficiency were achieved in three-layered Aurivillius thin films by ultra-fine grain nano-crystalline engineering. The ultra-low remanent polarization can be attributed to the emergence of polar nano-regions due to the disruption of macroscopic continuity of ferroelectric domains by ultra-fine nano-grains. At the same time, all thin films have high dielectric breakdown strength due to the presence of extremely high grain boundary density. Thus, a high recovered energy storage density of 71 J/cm3 and an efficiency of 76% were achieved, and the thin film capacitors show good fatigue endurance and temperature stability. The results suggest that ultra-fine nano-crystalline engineering can expand the application of traditional ferroelectric thin films in energy storage devices.

Modelling a low-head seawater-pumped hydro storage system's impeller for energy production within a small island developing state

ABSTRACT The proposed seawater pumped hydro storage (SPHS) is one option for providing a buffered energy storage system that will surely be required in the future. Given the fact that most small island developing states (SIDS) are isolated and surrounded by large bodies of water, the medium of seawater becomes an infinite supply. However, SIDS are also low lying and therefore only low heads will be available at high flows to maximize the energy efficiency of the system. The computational fluid dynamics (CFD) model, ANSYS Fluent, was used to simulate this SPHS pump turbine impeller at low heads and high flows. This pump turbine impeller was simulated to function as both a pump and as a turbine at various revolutions per minute (rpm). The performance of the system was evaluated based on the pressure and velocity distributions of the pump turbine impeller at varying rpm. It was found that there were less energy losses within the turbine mode compared to the pump mode. Furthermore, this study showcases that the CFD model ANSYS Fluent is an economical, efficient, and easily diagnosable technique for analysing an impeller in both pump and turbine modes.

A Janus Spectrally Selective Glazing Toward All-Season Energy-Efficient Windows.

Windows offer the most promising avenue for mitigating energy consumption and reducing greenhouse gas emissions. However, the balance between comfortable natural lighting and all-season energy savings is often neglected in extensive explorations of energy-efficient windows. Herein, a Janus glazing is proposed that enables the switch of passive radiative cooling and heating under the precondition of conveying sufficient natural light. Measurement results indicate that the Janus window maintains a visible transmittance of 0.47, while possesses a near-infrared (NIR) reflectivity/absorptivity of 0.75/0.71 and a mid-infrared (MIR) emissivity of 0.94/0.13 for the cooling and heating modes, respectively. As demonstrated by the outdoor test, the Janus window realizes a reduction of 7.1°C for room cooling and an increase of 0.4°C for room heating compared with commercial low-e window, potentially conserving 13%-53% of the total building energy consumption across China. Meanwhile, attributed to the photothermal effect, the Janus window can elevate the surface temperature by 6.1°C compared with the low-e window, which can effectively reduce fogging occurrences on the window surface for ensuring sunlight entrance in the cold-weather conditions. This strategy offers novel prospects for enhancing energy efficiency in diverse applications, including architectural windows, greenhouse cultivation, photovoltaic generation, etc.

Advanced Exergy-Based Optimization of a Polygeneration System with CO2 as Working Fluid.

Using polygeneration systems is one of the most cost-effective ways for energy efficiency improvement, which secures sustainable energy development and reduces environmental impacts. This paper investigates a polygeneration system powered by low- to medium-grade waste heat and using CO2 as a working fluid to simultaneously produce electric power, refrigeration, and heating capacities. The system is simulated in Aspen HYSYS® and evaluated by applying advanced exergy-based methods. With the split of exergy destruction and investment cost into avoidable and unavoidable parts, the avoidable part reveals the real improvement potential and priority of each component. Subsequently, an exergoeconomic graphical optimization is implemented at the component level to improve the system performance further. Optimization results and an engineering solution considering technical limitations are proposed. Compared to the base case, the system exergetic efficiency was improved by 15.4% and the average product cost was reduced by 7.1%; while the engineering solution shows an increase of 11.3% in system exergetic efficiency and a decrease of 8.5% in the average product cost.

Pumping Electrons from Oxygen-Bridged Cobalt for Low-Charging-Voltage Zn-Air Batteries.

Reducing the charging voltage is a prerequisite for improving the chargeability and energy efficiency of Zn-air batteries (ZABs). Herein, Fe3+ pumps electrons from oxygen-bridged cobalt (Fe-O-Co) and induces the accelerated charging kinetics. For the liquid ZABs, a charging voltage of around 1.94 V at 10 mA cm-2 was displayed, which slightly increased 2% after continuous cycles for 180 h. A steady charging voltage of around 1.87 V at 10 mA cm-2 was also exhibited for quasi-solid-state ZABs. Control experiments and characterization show that the interactions between the O2- and Fe3+ sites are relatively weaker than those between the O2- and Co3+ sites. Compared with Mn3+, Zn2+, and Cu2+, Fe3+ effectively pumps electrons from Co sites to generate the active species for the oxygen evolution reaction. Thus, the deprotonation behavior and *OH conversion were improved. This work demonstrates the oxygen electron bridge modulated electron transfer between dual metal sites, contributing to the improvement of low-charging-voltage ZABs.

Unveiling the Resistive Switching Mechanism and Low Current Dynamics of Ru‐based Hybrid Synaptic Memristors

AbstractMobile Ru ions in oxide media have been reported as a novel species that offer extremely low switching currents for memristors. However, their bi‐stable resistive‐switching (RS) and low‐switching currents dynamics have not been quantitatively unveiled. Here, the bi‐stable RS mechanism via in‐depth field‐induced atomic migration and chemical bonding state studies is elucidated, showing that the RS of the Ru‐based hybrid memristor (RHM) is possible via the simultaneously controlled hybrid Ru cation and oxygen anion. Additionally, the Ru ion mobility is quantitatively obtained via atomic moving distance and switching time measurements, demonstrating that the lower Ru ion mobility, compared to other conventional mobile species in oxide media, can be the origin of the low‐switching currents. It is found that the current conduction mechanism of the low‐resistance‐state in RHMs has temperature‐range‐dependencies. The direct tunneling conduction mechanism is dominant in relatively low temperatures; however, the ionic transport and thermally activated hopping conduction mechanism govern the current flow in high temperatures. Owing to the low Ru ion mobility, the RHM exhibits highly linear synaptic plasticity with a low‐conductance regime, showing outstanding energy efficiency compared to other memristors in image recognition tasks. These findings can contribute to improving the feasibility of hyper‐scale synaptic cores consisting of RHMs.

Optimal Propagation Model for DVB-T2 System in Urban Area

The large-scale implementation of analog switch-off for television broadcasting in Indonesia has led to blank spots in several regions. To address this issue, an optimal propagation model is needed. Proper selection and analysis of the channel model can enhance transmitter coverage, increase coverage percentage, improve energy efficiency, and boost field strength due to optimal transmit power. Previous studies have explored various DVB-T2 propagation models, notably the ITU-R P.1812-4 and Longley-Rice models, which are sophisticated and consider various environmental parameters, making them suitable for diverse broadcasting conditions. This research introduces a novel approach by specifically focusing on the urban context of Semarang City, Indonesia, to reduce blank spots by applying the ITU-R P.1812-4 and Longley-Rice propagation models. This study uniquely compares the two models to determine the most effective one for this urban area. Results indicate that the ITU-R P.1812-4 model provides a higher field strength value than the Longley-Rice model, with average field strengths of 108.3425 dBμV/m and 108.2325 dBμV/m, respectively. The difference in average field strength of 0.11 dBμV/m, despite having the same free space loss value of 100.9025 dB, indicates that one model has a slightly stronger signal. This stronger signal can improve coverage by reaching further distances and penetrating obstacles better. Additionally, a stronger signal means less power is needed to maintain the same coverage area, thus improving energy efficiency. This research not only offers empirical data specific to Semarang City but also provides insights that can guide future digital broadcasting optimizations in similar urban environments.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.

Crystal structure of the 4-hydroxybutyryl-CoA synthetase (ADP-forming) from nitrosopumilus maritimus

The 3-hydroxypropionate/4-hydroxybutyrate (3HP/4HB) cycle from ammonia-oxidizing Thaumarchaeota is currently considered the most energy-efficient aerobic carbon fixation pathway. The Nitrosopumilus maritimus 4-hydroxybutyryl-CoA synthetase (ADP-forming; Nmar_0206) represents one of several enzymes from this cycle that exhibit increased efficiency over crenarchaeal counterparts. This enzyme reduces energy requirements on the cell, reflecting thaumarchaeal success in adapting to low-nutrient environments. Here we show the structure of Nmar_0206 from Nitrosopumilus maritimus SCM1, which reveals a highly conserved interdomain linker loop between the CoA-binding and ATP-grasp domains. Phylogenetic analysis suggests the widespread prevalence of this loop and highlights both its underrepresentation within the PDB and structural importance within the (ATP-forming) acyl-CoA synthetase (ACD) superfamily. This linker is shown to have a possible influence on conserved interface interactions between domains, thereby influencing homodimer stability. These results provide a structural basis for the energy efficiency of this key enzyme in the modified 3HP/4HB cycle of Thaumarchaeota.

Investigation of Engine Exhaust Heat Recovery Systems Utilizing Thermal Battery Technology

Over 50% of an engine’s energy dissipates via the exhaust and cooling systems, leading to considerable energy loss. Effectively harnessing the waste heat generated by the engine is a critical avenue for enhancing energy efficiency. Traditional exhaust heat recovery systems are limited to real-time recovery of exhaust heat primarily for engine warm-up and fail to fully optimize exhaust heat utilization. This paper introduces a novel exhaust heat recovery system leveraging thermal battery technology, which utilizes phase change materials for both heat storage and reutilization. This innovation significantly minimizes the engine’s cold start duration and provides necessary heating for the cabin during start-up. Dynamic models and thermal management system models were constructed. Parameter optimization and calculations for essential components were conducted, and the fidelity of the simulation model was confirmed through experiments conducted under idle warm-up conditions. Four distinct operational modes for engine warm-up are proposed, and strategies for transitioning between these heating modes are established. A simulation analysis was performed across four varying operational scenarios: WLTC, NEDC, 40 km/h, and 80 km/h. The results indicated that the thermal battery-based exhaust heat recovery system notably reduces warm-up time and fuel consumption. In comparison to the cold start mode, the constant speed condition at 40 km/h showcased the most significant reduction in warm-up time, achieving an impressive 22.52% saving; the highest cumulative fuel consumption reduction was observed at a constant speed of 80 km/h, totaling 24.7%. This study offers theoretical foundations for further exploration of thermal management systems in new energy vehicles that incorporate heat storage and reutilization strategies utilizing thermal batteries.

Numerical investigation of heat and mass transfer of SWCNT/MWCNT‐water suspension over a porous stretching sheet using Sisko fluid model

AbstractThis study presents a comprehensive numerical computation of heat‐mass transfer characteristics of single‐walled carbon nanotube (SWCNT)/multi‐walled carbon nanotube (MWCNT)‐water suspension flow over a porous stretching sheet with an inclined magnetic field. The governing equations for fluid flow characteristics are formulated using the Sisko fluid model to capture the Newtonian and non‐Newtonian behavior of the nanotube‐water mixture. The nonlinear coupled partial differential equations are converted into nonlinear dimensionless coupled ordinary differential equations using suitable similarity transformations. These equations are solved using MATLAB by implementing the four‐stage Lobatto IIIa formula. The comprehensive set of computations is performed to explore the influence of pertinent parameters, including Sisko fluid parameters, concentration of nanotubes, stretching sheet velocity, and porous medium characteristics on the flow, heat, and mass transfer profiles. From the graphs and statistical analysis, it is clear that the volume fraction of SWCNT and MWCNTs are strongly correlated. The investigation reveals that increasing the inclination angle affects the fluid velocity. The variation in all flow features is negligible for volume fractions of CNTs between 0% and 10% but a significant effect is observed only beyond 10%. Higher volume fractions of CNTs result in enhanced local heat transfer coefficient. This can be attributed due to the outstanding heat transfer capabilities of CNTs owing to their high thermal conductivity. However, Shear thickening fluids exhibit high heat transfer phenomena when compared to shear‐thinning and Newtonian fluids. This research provides valuable insights into the optimization of CNT‐based nanofluids for efficient heat and mass transfer applications in electronics cooling, heat exchangers, and solar energy systems, offering opportunities to enhance energy efficiency and device performance.

Multidisciplinary Review of Induction Stove Technology: Technological Advances, Societal Impacts, and Challenges for Its Widespread Use

Induction stoves are increasingly recognized as the future of cooking technology due to their numerous benefits, including enhanced energy efficiency, improved safety, and precise cooking control. This paper provides a comprehensive review of the key technological advancements in induction stoves, while also examining the societal and health impacts that need to be addressed to support their widespread adoption. Induction stoves operate based on the principle of eddy currents induced in metal cookware, which generate heat directly within the pot, reducing cooking times and increasing energy efficiency compared with conventional gas and electric stoves. Moreover, induction stoves are considered an environmentally sustainable option, as they contribute to improvements in indoor air quality by reducing emissions associated with fuel combustion during cooking. However, ongoing research is essential to ensure the safe and effective use of this technology on a broader scale.