Energy-saving Effect Research Articles

High near-infrared reflectance Zn1-xMnxO yellow pigment prepared and properties by co-precipitation method

A successful synthesis of yellow pigments, namely Zn1-xMnxO (x = 0.001–0.009), was achieved using the co-precipitation method. To characterize the prepared pigments, various analytical techniques were employed, including powder X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FT-IR), and thermogravimetric-differential scanning calorimetry (TG-DSC). The results showed that the Zn1-xMnxO series pigments prepared by co-precipitation method retained the ZnO fibrous zincite structure and all had a good yellow color (b*=30.14–40.56) and high NIR solar reflectance (80.66 %-87.80 %). The results of the simulated heat shielding experiments confirmed that the Zn0.997Mn0.003O (abbreviated as ZM3) pigment synthesized in the study was 5°C cooler than the commercial iron yellow pigment, and the thermal imaging experiments showed that the surface temperature of the sample coatings was reduced by 8°C, which further confirmed that the ZM3 pigment had good heat shielding properties. Furthermore, the pigment demonstrates excellent chemical and thermal stability. and can be used as a cold pigment in architectural coatings to obtain energy-saving effects, which has great potential for development.

Numerical Analysis on Thermal Behavior of Composite Wall Combined Autoclaved Aerated Concrete With Thermal Insulting Material in Summer Day

Abstract Thermal insulation material (TIM) is commonly employed to enhance the thermal behavior of the lightweight walls. To further obtain the energy-saving effect of the walls, the composite wall (ATIM) combined autoclaved aerated concrete (AAC) block with TIM was present in this study. A testing rig with an ATIM wall was constructed and tested in summer design days, while the numerical modeling was developed and validated using the experimental data. Furthermore, reference walls of the brick and AAC with the same dimension as the ATIM wall were established to evaluate its thermal behavior. The thermal behavior and economic evaluation of the ATIM wall were then investigated by varying the thickness and position of the TIM using the numerical method. And the research results indicated that: (1) the average inner surface temperature of the ATIM wall is approximately 1.1 °C lower than that of the AAC wall, 1.3 °C lower than that of brick wall, the thickness of TIM is positively correlated with decreasing wall surface temperature, while TIM positions have minimal impact on reducing surface temperature; (2) heat gain reduction ratio for ATIM wall (δ = 20 mm) is approximately 52.7%, which is 8.1% higher than that of AAC wall, and variation in TIM position can decrease the heat gain, with inside or outside placement being more effective than positioning it in the middle part; (3) CO2 emission saving (CO2ES) and electricity cost saving (ECS) for room C (ATIM wall) is approximately 7.05 kg/100 m3/day and 5.23 RMB/100 m3/day, respectively, outperforming room A (brick wall) and room B (AAC wall) in terms of energy efficiency and economic benefits.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Analysis of the Applicability of a Phase-Change Energy Storage Wall for Public Buildings in Hot Summer and Cold Winter Climate Zones

The effects of applying a phase-change energy storage wall in office buildings in hot summer and cold winter climate zones were analyzed by comparing several factors based on numerical calculations, specifically focusing on the internal and external wall temperature, delay time, attenuation multiple, and building load. It was found that the most effective phase-change temperature was 30 °C and the optimal thickness for the phase-change layer was 30 mm. The adoption of the outer phase-change wall layout improved the heat insulation performance during the summer. This effectively enhanced the energy-saving effect of the phase-change layer thickness and layout, which aligns with the influence law of phase-change wall heat transfer.

Optimized Rear Drive Torque Allocation Strategy for Dual-Motor Mining Dump Trucks

This paper takes the dual-motor pure electric mining dump truck as the research object and designs a dual-motor rear-drive torque optimization allocation strategy in view of the problems such as the large load variation of the dump truck and the facts that the motor output torque cannot accurately express the driver’s dynamic intention and that the overall output efficiency of the dual motor is low. The strategy first divides the demand torque of the whole vehicle into two parts, the base torque and the compensation torque, which are determined by the accelerator pedal opening and the motor speed, and the compensation torque is fuzzy-controlled by taking the vehicle speed, the rate of change of the accelerator pedal opening, and the state of the battery charge as inputs. Subsequently, the dual-motor drive torque allocation is optimized using a particle swarm algorithm, with the objective of minimizing power loss in the dual motors. Furthermore, the energy-saving effect of the torque optimization allocation strategy proposed in this paper is compared with that of the traditional torque average allocation strategy under three working conditions: the driving conditions of Chinese dump trucks, the unloaded uphill movement of mining dump trucks, and the fully loaded downhill movement of mining dump trucks. The results show that the average efficiency of the dual-motor drive using the torque optimization allocation strategy is improved by 2.32%, 4.23%, and 2.24%, respectively, and battery energy savings are improved by 0.5%, 0.47%, and 0.24%, respectively.

Building energy consumption analysis and measures: a case study from an administration building in Chengdu, China

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

Digital economy spillover on energy saving and emission reduction: Evidence from China

Digital economy spillover (DES) measures the real impact of the digital economy. This study analyzes the theoretical mechanism of DES affecting energy saving and emission reduction. Selecting balanced panel data of 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2003 to 2022, we constructed a comprehensive DES index and empirically analyzed its impact on energy saving and emission reduction and the mechanism of this impact by fixed effect model. Our results suggest that DES has a significant emission reduction and energy-saving effect. The results of mechanism analysis show that DES can result in energy saving and emission reduction by promoting industrial structure optimization and enhancing technological innovation capacity. In addition, the impact of DES on energy saving and emission reduction is influenced by various factors, such as geographical conditions, the level of economic development, and performance assessment, exhibiting evident heterogeneity.

Research on Energy-Saving Efficiency and Influencing Factors of Green and Low-Carbon Enterprises Based on Three-Stage DEA and Tobit Models

Implementing the “dual-carbon” objective has profoundly affected China’s businesses, prompting a continuous process of transformation and innovation. Green, low-carbon transformation, upgrading, and sustainable development have emerged as the means to achieve high-quality company growth, with firms playing a crucial role in achieving the “dual-carbon” target. By prioritizing firms’ green, low-carbon, and sustainable growth, not only can their economic efficiency be improved, but it also serves as a vital measure to advance high-quality development. To achieve this objective, it is crucial to analyze the elements that impact the energy-saving effectiveness of businesses to develop optimization tactics that can enhance their competitiveness. This study combines the three-stage Data Envelopment Analysis (DEA) and the Tobit model to assess the energy-saving efficiency of green and low-carbon firms from 2018 to 2022. The analysis focuses on selected samples from various areas and industries. The study investigates the relationship between energy-saving effectiveness and different regional and industrial parameters, considering the distinct attributes of each firm and the key elements that influence its energy-saving performance. The findings indicate that green, low-carbon firms typically demonstrate suboptimal energy-saving efficiency but have considerable room for improvement. Upon considering environmental concerns, it becomes clear that the primary limitation on energy-saving performance is the lower efficiency of the enterprise scale. The energy-saving efficiency of green, low-carbon firms varies significantly across different regions and industries. On average, northern firms in the energy-saving and carbon reduction industry, as well as the resource recycling industry, have higher energy-saving efficiency compared to southern enterprises. In contrast, the environmental protection industry in southern firms demonstrates a better average energy-saving efficiency compared to their northern counterparts. Employee quality, policy support, and automation are key factors that greatly enhance the energy-saving efficiency of firms in both regions. Enterprise size has a beneficial impact on the energy-saving efficiency of southern firms but a negative impact on northern enterprises. Moreover, the industry and financial structures exert a detrimental influence on enhancing the energy-saving efficiency of green and low-carbon firms. Thus, in order to improve the energy-saving effectiveness of businesses, it is essential to utilize the elements that have a positive influence on energy-saving performance and reduce the impact of those that have negative impacts. This strategy will enhance the energy-saving efficiency of businesses and promote the development of an environmentally friendly and low-carbon society.

Research on Energy Saving Effect of Parallel and Perpendicular Yard Layouts under Different Proportions of Transshipment at the Automated Container Terminal

The proportions of container transshipment is the key factor in determining the proportion of automated guided vehicle (AGV) and external container truck operations. In terms of parallel and perpendicular layouts of automated container terminals (ACTs), varying proportions of container transshipment result in different proportions of AGVs and external container truck operations, subsequently leading to distinct impacts on energy consumption (EC) for each ACT layout. This paper deemed EC as the primary evaluation criterion, established an EC model encompassing yard cranes (YCs) and container trucks, and investigated the EC of parallel and perpendicular layouts at different proportions of container transshipment. The results indicate that when the proportions of container transshipment were less than 17%, the parallel layout had lower EC; when it was between 17% and 21%, there was not much difference between the two layouts; when it was greater than 21%, the perpendicular layout had lower EC. This conclusion was based on an ideal environment established in this paper. When making decisions, decision makers should use this model as a starting point and adapt it flexibly to the actual situation of the port, in order to arrive at a reasonable and feasible plan.

Study on energy-saving applications of vacuum ejectors across various operational demands

Vacuum suction technology is a pivotal innovation within the Industry 4.0 revolution. Ejectors serve as the core of vacuum systems with distinct advantages such as simple structure, low cost, and ease of operation. However, traditional fixed ejectors suffer from low efficiency, energy waste, and poor adaptability. This study aims to optimize the design of vacuum ejectors to meet various requirements, achieving energy efficiency and high performance in automated production. On this basis, an energy-saving vacuum system based on dual ejectors is proposed, which can switch between single and dual ejectors depending on different working stages. When the vacuum reaches 90 kPa, the required motive pressure and the entrainment ratio are selected as the screening indicators for the vacuum ejector. A two-dimensional axisymmetric model is established and verified through experimentation. The key parameters are identified through a single-factor analysis. A multi-factor analysis is employed to determine the recommended values for each parameter and their respective applications. The energy-saving effects of the optimized ejectors are evaluated in conjunction with the automated processes. The findings indicate that the vacuum system based on dual ejectors can achieve rapid response and low air consumption. This paper contributes to advancing the research on energy-saving applications of ejectors.

The promotion effect of human activity intensity on displacement ventilation efficiency by the enhancement indoor thermal stratification

In a displacement ventilation system, the human body heat source plays a significant role and should not be overlooked. The intensity of human activity is a crucial factor in determining the amount of heat generated by the human body. In this work, the impact of thermal plumes generated by different levels of human activity intensity on the temperature stratification and ventilation efficiency in displacement ventilation system have been explored with experimental and numerical methods. The airflow patterns in the displacement air supply system under various levels of human activity intensity were simulated. The results show that under the same air supply speed, as the human activity intensity increasing from 125 W to 402 W, the phenomenon of temperature stratification becomes more pronounced. With the promotion of thermal plumes, the air flow velocity on the human body surface increases, leading to an enhancement in ventilation efficiency. Therefore, to elimination of the indoor thermal load, the air supply speed can be reasonably reduced according to the human activity intensity in the room, further enhancing the energy-saving effect of displacement ventilation.

Study on the daily thermal radiation iso-disturbance on a building by trees in summer

Many studies have demonstrated that the cooling and energy-saving effects of planting trees around buildings vary depending on their location. However, most research has focused on a limited number of representative planting sites. This study evaluates the reduction in thermal radiation absorbed by walls over a single day due to trees, defined as daily thermal radiation disturbance (TRDD). Using a combination of experiments and ENVI-met software simulations, we established a coordinate system to map the TRDD distribution of trees at various locations around a building. The findings reveal that trees at different locations exhibit isoTRDD lines, which are symmetrical about the building’s centerline. Trees planted along these isoTRDD lines exert equal effects on TRDD. The isoTRDD lines form a series of ellipses that decay in two stages with increasing distance between the tree crowns and the walls (DW-T): rapid decay when DW-T is less than 3 m and slower decay beyond 3 m. Additionally, we found a linear positive correlation between the change in the sky view factor (ΔSVF) on the wall surface caused by trees and the TRDD. Trees near the north and south walls, as well as those along the building's diagonal, primarily block sky-scattered radiation (R²>0.97). Trees around the east and west walls block both sky-scattered and direct solar short-wave radiation (R²>0.85). This indicates a significant relationship between isoTRDD and SVF of the building. The methodology proposed in this study allows for a comprehensive assessment of the spatial evolution of TRDD for trees around the building, providing a scientific basis for the spatial design of trees to optimize cooling and energy savings. Moreover, the relationship between ΔSVF and TRDD enables rapid calculations and comparisons of the cooling effects of trees at different locations.

Photothermal synergistic modulation of patterned VO2-Based composite films for smart windows

Vanadium dioxide (VO2) emerges as promising material for smart windows due to its ability to dynamically modulate the characteristics of near-infrared light during phase transition, accompanied by a relatively high luminous transmittance (Tlum). However, traditional VO2-based smart windows have predominantly emphasized achieving high solar modulation ability (ΔTsol), often overlooking the modulation of emissivity (ε) of long-wave infrared (LWIR). This oversight makes it challenging to achieve optimal energy-saving effects. To address this, patterned smart window composite films are designed and optimized by using Fabry-Pérot cavity to achieve dual-band coordinated regulation of sunlight and thermal radiation, which can greatly improve the energy-saving efficiency. The obtained composite film has a great low-temperature luminous transmittance (Tlum-L=50.4 %) and a high solar modulation ability (ΔTsol = 13.9 %). Meanwhile, the composite film can adaptively adjust its emissivity, with emissivity values of 0.67 and 0.18 at high and low temperatures, respectively, endowing it with excellent thermal radiation regulation capabilities. The development of patterned composite films is of great significance in the field of thermal control smart windows, which has great potential in improving the energy efficiency of buildings and enhancing resident comfort.

Investigation of the Optimal Operation Method of the Heat Recovery Ground Source Heat Pump System Installed in an Actual Building and Evaluation of Energy Saving Effect

Investigation of the Optimal Operation Method of the Heat Recovery Ground Source Heat Pump System Installed in an Actual Building and Evaluation of Energy Saving Effect

Research on Coal Flow Visual Detection and the Energy-Saving Control Method Based on Deep Learning

In this paper, machine vision technology is used to recognize the coal flow on a conveyor belt and control the running speed of a motor according to the coal flow on the conveyor belt to achieve an energy-saving effect and provide technical support for the sustainable development of energy. In order to improve the accuracy of coal flow recognition, this paper proposes the color gain-enhanced multi-scale retina algorithm (AMSRCR) for image preprocessing. Based on the YOLOv8s-cls improved deep learning algorithm YOLO-CFS, the C2f-FasterNet module is designed to realize a lightweight network structure, and the three-dimensional weighted attention module, SimAm, is added to further improve the accuracy of the network without introducing additional parameters. The experimental results show that the recognition accuracy of the improved algorithm YOLO-CFS reaches 93.1%, which is 4.8% higher, and the detection frame rate reaches 32.68 frame/s, which is 5.9% higher. The number of parameters is reduced by 28.4%, and the number of floating-point operations is reduced by 33.3%. These data show that the YOLO-CFS algorithm has significantly improved the accuracy, lightness, and reasoning speed in the coal mine environment. Furthermore, it can satisfy the requirements of coal flow recognition, realize the energy-saving control of coal mine conveyor belts, and achieve the purpose of sustainable development of the coal mining industry.

Universal artificial intelligence workflow for factory energy saving: Ten case studies

Numerous studies have affirmed that artificial intelligence (AI) can effectively enable energy savings in factories. However, there is currently a lack of explicit research that identifies the energy-saving effects of AI methods as compared to the conventional practices employed in factories, which involve the replacement of equipment with high energy efficiency (non-AI method), with respect to variance in the effectiveness. Thus, this ignited the motivation for this study, wherein the research team developed a “Universal AI workflow for energy saving” which adopted a standard workflow of “learning, Modeling, and Prediction.” Additionally, a three-year research project was launched to conduct empirical tests in ten factories, comparing the efficacy differences between AI and Non-AI methods. In 2021, research team established the energy consumption baselines for each factory through data collection; the entirety of 2022 was dedicated to AI integrations; and energy-saving benefits were evaluated in 2023. During the research period, each factory also implemented other non-AI energy-saving methods which were juxtaposed with AI approaches for comparison. Our study results showed that average AI-enabled energy savings across the ten factories amounted to 106,124 kWh/yr, while average energy savings from non-AI methods amounted to 1,231,625 kWh/yr. Despite the inferior energy-saving results, the AI methods had an average return on investment (ROI) of 0.46 years while non-AI methods had an average ROI of 6.22 years, indicating that AI methods could recover the investment costs through energy savings in less than one year. It is also worth noting that the ten factories produced a diverse range of products encompassing wafers, steel, and foods, but the AI methods used for energy saving employed the same workflow. By contrast, the non-AI methods necessitated distinct workflows and detailed engineering adjustments. It can be inferred from these empirical findings that AI techniques for achieving energy-saving control in factories can be integrated using standardized processes. In summary, AI methods can facilitate standardized workflows in factories and offer substantial energy-saving benefits that can typically cover investments within a year. AI represents a highly viable energy conservation option for factories seeking to maintain uniformity in product processes and realize efficient ROIs.

Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics

The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.

Calculation Method of Energy Saving effect for Dimming-control according to Day-lighting in ECO2

Calculation Method of Energy Saving effect for Dimming-control according to Day-lighting in ECO2

Theoretical investigation of VO2 smart window with large-scale dynamic infrared emittance adjustment for adaptive thermal management

Thermochromic windows based on vanadium dioxide (VO2) are widely used in architectural windows due to its reversible phase change process. However, traditional VO2 windows only manage solar radiative transmittance and their emittance variation trend in the mid-infrared contradicts realistic requirements, which seriously hinders the further development of thermochromic windows. To address this issue, a VO2 full-spectrum smart window based on theoretical calculations is proposed for adaptive adjustment of solar spectral transmittance and thermal emittance. We cleverly utilize a thin Ag layer to construct a Fabry-Perot (FP) resonant cavity with the VO2 layer to achieve forward modulation of the emittance in the atmospheric window. Notably, the smart window has 83.8 % emittance modulated ability, which enables effective control of radiative cooling and has greatly exceeded the reported performance of the smart windows while maintaining 72.8 % high visible transparency. Energy consumption analysis indicates that the smart window has high energy-saving potential worldwide, achieving over 60 MJ/m2 energy-saving effect. This simple and easy-to-manufacture smart window expands the research scope of windows and broadens application prospects in thermal management, infrared camouflage, and building energy conservation.

Comprehensive analysis on building performance enhancement based on selective split-band modulated adaptive thermochromic windows

Thermochromic technology has exciting potential for building applications as it passively modulates its solar transmittance. Existing smart window technologies lead to unnecessary increases in lighting energy consumption during spectral modulation and limited application scenarios. Therefore, spectrally selective modulation is a key direction to improve the performance of smart windows. In this paper, a three-story office building is established in EnergyPlus, and how the characteristic parameters and spectral modulation properties affect the effectiveness of thermochromic windows are thoroughly investigated through actual spectra and hypothetical split-band modulation spectra. A comprehensive human-centered evaluation system is proposed, multiple light zones are divided to investigate in detail the light and thermal environment and comfort performance of office spaces with different orientations of near-window areas and verify the energy-saving effect. The impact of thermochromic adaptive windows on building part loads and equipment efficiency is discussed and the matching of this passive technology with renewable photovoltaics is briefly mentioned. Based on the two-way synergistic perspective of materials and buildings, the results inform the application of smart windows in specific scenarios, throwing light on more cross-cutting research.