Energy-efficient Solution Research Articles

An energy-efficient solution to sludge drying and combustion process through Camellia oleifera shells amended foaming

Foaming pretreatment has been proven effective in promoting sludge drying, however, the variation in sludge properties significantly influences the foaming efficiency. Inspired by foam stabilizer of solid particles, Camellia oleifera shells (COS) was screened out from various biomasses as an additive incorporated with the CaO for promoting the sludge foaming. For the introduction of COS, this study analyzed the drying behaviors of foamed sludge, quantified the surface cracks information, characterized the combustion performance, and evaluated the energy consumption. The results indicated that 46.72–50.10% of time could be saved in foaming the sludge to 0.70 g/mL by addition of 3.0 wt% COS. Compared with the original sludge (OS), the 0.70 g/mL foamed sludge saved 47.43% of time for sludge drying at 80 °C, and this value further increased to 53.14% with 3.0 wt% COS addition. Combining the multifractal spectra and drying kinetics analysis, the foaming promoted the formation of complex surface cracks in the warm-up period, while COS further improved the complexity of cracks in the constant rate period, and the shrinkage of isolated sludge blocks in the falling rate period, thus enhanced the moisture diffusion and heat transfer. Furthermore, the appropriate porous structure and additional volatile matters promoted the combustion performance. The 0.90 g/mL foamed sludge with COS presented the lowest activation energy of 180.362 kJ/mol in combustion. Overall, compared with OS, the 0.70 g/mL foamed sludge with COS saved 40.65% energy consumption during the foaming, drying and combustion processes, providing an energy-efficient solution for the sludge treatment and disposal.

Improving the Energy Performance of Public Buildings in the Mediterranean Climate via a Decision Support Tool

Addressing Europe’s decarbonisation challenge involves widespread deployment of nearly zero-energy buildings, deep energy renovations and renewable energy integration in the building sector. Enhancing energy efficiency in public buildings necessitates tailored solutions and strategic planning involving Local Public Administration. This work focuses on advancing insights into the application of the PrioritEE Decision Support Tool in Portuguese public buildings, highlighting the energy and financial savings and carbon dioxide emission reduction potential. Using detailed building characterisation data from energy performance certificates, we applied the tool across 22 public buildings of diverse typologies in three distinct regions of Portugal, representing various public entities. Results demonstrate the tool’s adaptability, enabling a comprehensive assessment of energy performance and facilitating the exploration of customised energy efficiency and renewable energy solutions. The research emphasises the critical role of user-friendly tools in aiding policymakers and local administration technicians in meeting national renovation targets and contributing to the broader energy transition objectives.

Study on Oxidation Activity of Hydrogenated Biodiesel–Ethanol–Diesel Blends

In the pursuit of understanding the oxidation mechanisms of hydrogenated biodiesel fuels and elucidating the combustion behavior of biomass fuels when blended with diesel, this study presents a comprehensive investigation into the reaction mechanism of hydrogenated biodiesel–ethanol–diesel mixtures. We develop a comprehensive reaction mechanism encompassing 187 components and 735 reactions for hydrogenated biodiesel–ethanol–diesel mixtures. Through kinetics analysis under varied conditions, including 1.0 MPa pressure, an equivalence ratio of 1.0, and temperatures of 900 K and 1400 K, we explore the impact of cross-reactions and changing fuel blend ratios on low- and high-temperature oxidation. Our findings indicate that oleic and stearic acid methyl esters serve as better substitutes for representing hydrogenated biodiesel kinetics than methyl decanoate. At lower temperatures, increased hydrogenated biodiesel and ethanol content leads to reduced OH generation, impacting reactivity. Conversely, higher temperatures result in enhanced OH production with increased hydrogenated biodiesel and ethanol concentrations, promoting reactivity. A cross-reaction analysis reveals CH2O as a prominent product, with the CH2O→HCO→CO pathway playing a pivotal role. In summary, our research unveils the intricate oxidation mechanisms of hydrogenated biodiesel–ethanol–diesel mixtures, providing insights into their combustion characteristics and offering implications for optimizing fuel blends for cleaner and more efficient energy solutions.

Electrochemical performance of symmetric solid oxide cells employing a Sc-doped SrFeO3-δ-based electrode

Symmetrical solid oxide cells (SSOCs) integrate the capabilities of solid oxide fuel cells and solid oxide electrolysis cells within a symmetric device, which have the advantage of simple fabrication and lower cost compared to conventional devices. Herein, to develop more advanced electrode materials to meet the catalytic activity for different electrode reactions, this study explores Sc-doped Sr0.9Fe0.8Sc0.1Co0.1O3−δ (SFSC) as an SSOC electrode material. Experiments show that Sc doping enhances the crystal stability of Sr0.9Fe0.9Co0.1O3−δ (SFC) in H2 and provides more oxygen vacancies for CO2 adsorption. Electrochemical tests reveal that the SFSC-Gd0.1Ce0.9O1.95 (GDC) electrode offers high power output, high CO2 electrolysis rates, and stability. Specifically, when operated as a fuel cell, the thus fabricated SSOC exhibits a peak power density of 781 mW·cm−2 and a polarization resistance of 0.117 Ω·cm2 at 850 °C, with operational stability over 300 h. Additionally, it reaches a peak current density of 1.96 A·cm−2 at 850 °C for CO2 electrolysis and is stable for 200 h at 0.45 A·cm−2 and 800 °C. Ab initio simulations further reveal a higher probability for oxygen vacancies in SFSC and an improved catalytic dissociation ability relative to Sr0.9Fe0.9Co0.1O3−δ for hydrogen and CO2. The stable and multi-functional SFSC-GDC electrode thus represents a compelling choice for SSOCs, advancing the prospects for efficient and cost-effective energy solutions.

Enhancement of thermal power plant performance through solar-assisted feed water heaters: An innovative repowering approach

Increasing the efficiency of a power plant by optimization or repowering has always been a crucial challenge for energy analysts. At the same time as natural energy resources are limited, the integration of renewable energy sources such as solar energy has been prioritized for the past few decades. This study presents a way of enhancing a thermal power plant with a regenerative reheating Rankine cycle, evaluating the impact of solar-powered heat exchangers on feed water heating at 200 MW single unit of the Shahid Montazeri Power Plant operating at 13 MPa and 540 °C. Increases in energy-exergy efficiencies, power output and fuel save due to this modification have been studied in 14 different cases which have been analyzed in Engineering Equation Solver (EES). The case studies cover replacing single, double, and triple Feed Water Heaters (FWH). Parametric analysis of the four most promising cases has been performed in varying operating conditions such as main steam and reheat steam temperature and pressure and condensation pressure. In the most favorable configuration (case 12), the net thermal power output increases to 225 MW while energy and exergy efficiency increases to 39.73 % and 43.99 % respectively. Exergy and energy loss across different major components have been presented as well which indicates room for further improvement and contributes valuable insights to thermal power plant optimization for sustainable and efficient energy solutions in the future.

BEANet: An Energy-efficient BLE Solution for High-capacity Equipment Area Network

The digital transformation of factories has greatly increased the number of peripherals that need to connect to a network for sensing or control, resulting in a growing demand for a new network category known as the Equipment Area Network (EAN). The EAN is characterized by its cable-free, high-capacity, low-latency, and low-power features. To meet these expectations, we present BEANet , a novel solution designed specifically for EAN that combines a two-stage synchronization mechanism with a time division protocol. We implemented the system using commercially available Bluetooth Low Energy (BLE) modules and evaluated its performance. Our results show that the network can support up to 150 peripherals with a packet reception rate of 95.4%, which is only 0.9% lower than collision-free BLE transmission. When the cycle time is set to 2 s, the average transmission latency for all peripherals is 0.1 s, while the power consumption is 18.9 μW, which is only half that of systems using LLDN or TSCH. Simulation results also demonstrate that BEANet has the potential to accommodate over 30,000 peripherals under certain configurations.

Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks.

The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.

A Comparative Analysis of Energy Consumption in Various Wireless Sensor Network Techniques

The objective of this study is to analyze the energy consumption associated with modern methodologies utilized in wireless sensor networks and to conduct a comparative assessment with Reed Solomon (RS) codes. This paper presents three discrete techniques for wireless sensor networks. The strategies mentioned include the Self-Evolving Sensor System (SESS), the Secure and Adaptive Key Management utilizing Multipath Routing Protocol (SAKM-MRP), and the National Instruments Secure Reference-based Data Aggregation (NI-SRDA). A distinct algorithm was developed for each method to examine the energy use. Based on the experimental results, it has been shown that the RS-codes approach consumes a considerably greater quantity of energy compared to the SESS methods, which, in contrast, exhibit a significantly lower energy consumption. When comparing the efficiency of RS-codes and SESS methods, it is observed that the SAKN-MRP technique exhibits a more significant decrease in energy consumption. Compared to the RS-Codes system, the SESS scheme stands out with a significant 45.5% reduction in energy usage at the maximum delivery node. Similarly, the SAKM-MRP scheme showcases an average decrease of 35.7% in energy consumption. Notably, the NI-SRDA scheme achieves an impressive 60% reduction in energy consumption, underscoring its remarkable impact on energy efficiency. In a broader sense, it can be inferred that the NI-SDRA technique holds promise as an energy-efficient solution for wireless sensor networks in comparison to alternative strategies suggested in the current study.

Machine learning in high-entropy alloys: phase formation predictions with artificial neural networks

Due to their complex compositions, high entropy alloys (HEAs) offer a diverse range of material properties, making them highly adaptable for various applications, including those crucial for future sustainability. Phase engineering in HEAs presents a unique opportunity to tailor materials for environmentally friendly technologies and energy-efficient solutions. However, the challenge of predicting phase selection, a key aspect in harnessing the full potential of HEAs for sustainable applications, is compounded by the limited availability of HEA data. This study presents a distinctive approach by using a precisely produced and selected dataset to train an artificial neural network (ANN) model. This dataset, unlike prior studies, is uniquely constructed to contain an equal amount of training data for each phase in HEAs, which includes single-phase solid solutions (SS), amorphous (AM), and intermetallic compounds (IM). This methodology is relatively unexplored in the field and addresses the imbalanced data issue common in HEA research. To accurately assess the model's performance, rigorous cross-validation was employed to systematically adapt the model's hyperparameters for phase formation prediction. The assessment includes metrics such as phase-wise accuracy (AM 86.67% SS 81.25% & IM 82.35%), confusion matrix, and Micro-F1 score (0.83), all of which collectively demonstrate the effectiveness of this approach. The study highlights the importance of feature parameters in phase prediction for HEAs, shedding light on the factors influencing phase selection. Its balanced dataset and training method notably advance machine learning in HEA phase prediction, providing valuable insights for material design amidst challenges and data scarcity in the field.

Navigating Success in Carbon Offset Projects: A Deep Dive into the Determinants Using Topic Modeling

Carbon offset projects play a crucial role in tackling the global challenge of climate change. However, there is limited understanding of the factors contributing to the success of a carbon offset project. In this study, we utilize the latent Dirichlet allocation method to extract topics from the descriptions of carbon offset projects sourced from the Gold Standard Foundation. Our findings reveal that projects encompassing both safety and efficient energy solutions for households command higher prices. These results imply that an effective carbon offset project should mitigate individual household emissions while enhancing safety. Our research carries significant implications for stakeholders involved in carbon offset projects and can serve as a foundation for policy formulation and standard regulations.

Theoretical studies of the expedient radius of thin pipe insulation

Decentralized ventilation systems with exhaust heat recovery are an energy-efficient solution for organizing and maintaining comfortable microclimate parameters in apartments and individual residential buildings. Such supply and exhaust systems do not require additional space for the distribution network of air ducts and significant intervention in the interior of the premises. One such device is the Vents TwinFresh regenerative installation. Twin Fresh install in the structure of the outer wall. The task of follow-up is to increase the efficiency of its work. Studies of heat exchange processes in the thin channels of the heat regenerator carried out. Channels with different thermophysical properties are studied. Experimental uncertainty decreases with better thermal insulation of the investigational stand. It is confirmed by the decrease in overheating and the heat transfer coefficient of the external surface thermal insulation when its thickness increases. Overcoming the critical diameter of the insulation of the experimental installation allows to minimize the measurement error, which takes into account the influence of heat exchange processes of the outer surface of the insulation with the environment. It is necessary to determine not the critical but the expedient diameter of the insulation. When using the appropriate thickness of thermal insulation, the temperatures on the inner and outer surfaces of the pipe have practically the same value. Thus, it is possible to take temperature readings of thin tubes from the outside, not from the inside, with the same accuracy. Accordingly, the design of the test stands for experimental research is simplified. The scope of application of the appropriate diameter of thermal insulation is not limited only to studies of heat transfer. This concept can used in any systems, equipment, and techniques that require insulating materials.

A radical polymer membrane for simultaneous degradation of organic pollutants and water filtration

Integrating reactive radicals into membranes that resemble biological membranes has always been a pursuit for simultaneous organics degradation and water filtration. In this research, we discovered that a radical polymer (RP) that can directly trigger the oxidative degradation of sulfamethozaxole (SMX). Mechanistic studies by experiment and density functional theory simulations revealed that peroxyl radicals are the reactive species, and the radicals could be regenerated in the presence of O2. Furthermore, an interpenetrating RP network membrane consisting of polyvinyl alcohol and the RP was fabricated to demonstrate the simultaneous filtration of large molecules in the model wastewater stream and the degradation of ~ 85% of SMX with a steady permeation flux. This study offers valuable insights into the mechanism of RP-triggered advanced oxidation processes and provides an energy-efficient solution for the degradation of organic compounds and water filtration in wastewater treatment.

Recent Development and Future Frontiers of Oxygen Reduction Reaction in Neutral Media and Seawater

AbstractThe oxygen reduction reaction (ORR) in neutral media is of great importance due to its potential to optimize energy generation and storage application. With the increasing urgency for sustainable and efficient energy solutions, comprehensively understanding and enhancing ORR performance in such media have become crucial. This review aims to shed light on this critical yet challenging area. This review encapsulates the fundamental principle of ORR and the latest breakthrough in the field of electrocatalysts, with a distinct focus on innovative synthesis strategies for creating novel, efficient, and robust electrocatalysts. A succinct evaluation of the strengths, limitations, performance, and reaction mechanism is presented. The essential findings are analyzed and their implications for future research directions. Finally, an outlook on current advances, challenges, and future research recommendations is provided. This review serves as a stepping‐stone toward harnessing the underutilizing energy potential within the extensive aquatic environment.

An optimized approach for solar concentrating parabolic dish based on particle swarm optimization-genetic algorithm

Parabolic dish concentrators have demonstrated the highest thermal and optical efficiencies among the available concentrator options. This paper proposes a novel design approach for fabricating large parabolic dish concentrators by employing compliant petals optimized through Particle Swarm Optimization-Genetic Algorithm (PSO-GA). The design concept involves using cables to pull the outer corners of the petals towards the center, resulting in the creation of finely formed dish mirrors. These mirrors are constructed from thin, optimal-shaped metal petals with highly reflective surfaces. In addition, an analytical model is presented to optimize the bending stiffness of the petals by strategically arranging punched holes using PSO-GA. The proposed design concept is validated through the application of Finite Element Analysis and ray tracing software, specifically LightTools, as well as laboratory experiments. Based on the demonstration with a 1m-diameter parabolic dish, it was observed that a receiver surface with a radius of 3.5 cm could achieve an impressive sunlight collection efficiency of up to 98%. This innovative design approach offers several advantages, including simplified fabrication and transportation of flat mirror elements to field sites, which can potentially lead to cost reductions and highly efficient solar energy solutions.

Energy Harvesting in Electric Power Systems for Data Communication Purposes

Aiming to illuminate the path towards integrating energy harvesting (EH) technologies into modern electric power systems, thereby enabling more efficient and sustainable energy solutions, this paper explores energy harvesting within electric power systems and points out its potential to power data communication purposes. In this sense, we examine the overarching principles of EH in these systems, spotlighting both the merits and limitations associated with energy extraction across varying voltage levels and electric signal frequency bands. Central components of EH devices, alongside a spectrum of harvesting methodologies, are thoroughly reviewed. Also, we showcase a case study that provides a quantitative assessment of EH potential in electric power systems. The manuscript culminates in a forward-looking analysis of EH, considering emerging technologies that promise to redefine the landscape. These include integrating hybrid power line and wireless systems, strategies for optimal power allocation, enhancements in physical layer security, the innovative concept of negawatts, advancements in harvesting circuit designs and applying non-orthogonal multiple access techniques

Assessing the efficacy of green building design strategies in minimizing energy consumption in commercial buildings of Mumbai: A building performance analysis

This research paper investigates the effectiveness of green building design strategies in minimizing energy consumption in commercial buildings in the bustling metropolis of Mumbai, India. With a rapidly urbanizing and densely populated environment, Mumbai presents a unique setting for assessing green building practices. The research objectives focus on understanding the impact of various green building design features, such as green roofs, solar panels, and floor area optimization, on energy efficiency in commercial structures. The research employs a robust methodology, incorporating regression analysis to analyze data obtained from the Mumbai Municipal Corporation's building energy consumption records. Through this analysis, the study investigates the relationships between green building design parameters, building characteristics, and energy consumption patterns. Key findings indicate that specific green building features, including intensive and biodiverse green roofs and solar panel installations, significantly reduce energy consumption. Additionally, older buildings tend to exhibit lower energy consumption, emphasizing the need for retrofitting existing structures with energy-efficient solutions. The study also underscores the role of occupant density in influencing energy efficiency, with higher occupant rates leading to more efficient space utilization and energy use. The implications of this research are far-reaching, offering insights into tailoring green building strategies to the specific challenges posed by densely populated urban areas. These findings hold significance for sustainable urban development, policy formulation, architectural design, and the global effort to mitigate climate change.

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

A Review Study on Energy Consumption in Cloud Computing

Cloud computing has become a fundamental technology for a wide range of computing services, yet its increasing energy demands present substantial environmental and economic challenges. With the rapid growth of Cloud services and applications, increasing number of researches have been focused on energy saving. The need to reduce energy costs is a constant challenge of cloud providers and data centers. This paper offers an extensive review of the issues surrounding energy consumption in cloud computing, with a focus on algorithms associated with the situational awareness, consolidation, allocation, placement/migration, and scheduling of virtual machines and containers. We conduct a critical analysis of studies from 2018 to 2023, comparing various methodologies aimed at achieving energy efficiency without sacrificing performance. This review delineates current trends, identifies gaps in existing research, and proposes directions for future investigations. Our study emphasizes the necessity of cultivating sustainable practices in cloud computing and provides valuable insights into the practical implementation of energy-efficient solutions in cloud environments.

Numerical Study of Thermal Efficiency in Light-Gauge Steel Panels Designed with Varying Insulation Ratios

In the construction domain, there is a growing emphasis on sustainability, resource efficiency, and energy optimisation. Light-gauge steel panels (LGSPs) stand out for their inherent advantages including lightweight construction and energy efficiency. However, the effective management of thermal efficiency, particularly addressing thermal bridges, is crucial. This paper conducts a detailed numerical investigation into the thermal performance of LGSPs, examining varied insulation ratios. Thermal finite element (FE) models were initially developed using the THERM software and validated against code predictions and results available in the literature. A comprehensive parametric study explored different insulation ratios, insulation materials, and wall thicknesses, discovering their impact on thermal transmittance (U-value). Key findings revealed that U-value correlated with insulation material conductivity, with E-PLA insulation exhibiting the lowest values, and increasing wall thickness resulted in decreased U-values. It was found that a strategic use of insulation yielded a U-value reduction of over 65%. New simplified design approaches were developed, featuring insulation ratios linked to accurate U-value predictions for LGSP configurations. The new design approaches were found to provide more accurate and consistent U-value predictions. Moreover, optimum insulation ratios for new builds and existing building extensions were found to be around 0.9 and 0.7 for 275 mm and 325 mm thick walls, respectively. These proposed energy-efficient solutions, facilitated through advanced design, are well-aligned with net-zero construction objectives.

Geographically constrained resource potential of integrating floating photovoltaics in global existing offshore wind farms

Marine renewable energy is gaining prominence as a crucial component of the energy supply in coastal cities due to proximity and minimal land requirements. The synergistic potential of integrating floating photovoltaics with offshore wind turbines presents an encouraging avenue for boosting power production, amplifying spatial energy generation density, and mitigating seasonal output fluctuations. While the global promise of offshore wind-photovoltaic hybrid systems is evident, a definitive understanding of their potential remains elusive. Here, we evaluate the resource potential of the hybrid systems under geographical constraints, offering insights into sustainable and efficient offshore energy solutions. We compile a database with 11,198 offshore wind turbine locations from Sentinel-1 imagery and technical parameters from commercial project details. Our analysis reveals an underutilization of spatial resources within existing offshore wind farms, yielding a modest 26 kWh per square meter. Furthermore, employing realistic climate-driven system simulations, we find an impressive potential photovoltaic generation of 1372 ± 18 TWh annually, over seven times higher than the current offshore wind capacity. Notably, floating photovoltaics demonstrated remarkable efficiency, matching wind turbine output with a mere 17 % of the wind farm area and achieving an average 76 % increase in power generation at equivalent investment costs. Additionally, the hybrid wind and photovoltaic systems exhibit monthly-scale complementarity, reflected by a Pearson correlation coefficient of -0.78, providing a consistent and reliable power supply. These findings support the notion that hybrid offshore renewable energy could revolutionize the renewable energy industry, optimize energy structures, and contribute to a sustainable future for coastal cities.