Energy Consumption Of Equipment Research Articles

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

Insights into the thermal and electric field distribution and the structural optimization in the graphitization furnace

The Acheson graphitization furnace (AGF), as a most energy-consuming equipment for graphite production, results in substantial thermoelectric losses during heating process. To improve the thermal efficiency, a method is proposed to optimize the structure of furnace core based on the thermoelectric field distribution. In this study, a numerical simulation using FLUENT software was adopted to explore the characteristics and coupling rules in relation to thermoelectric field distribution. Further optimize furnace core structure to achieve more suitable performance indicators, which include electrical heating efficiency and thermal efficiency. The results suggested that the changes of electric (power density) and thermal (temperature) fields with the layout of coke materials exhibited a consistence: the lower power density and temperature were observed at the position of graphitized coke, while the higher values were for calcined coke. The heat producer during the graphitization process was twofold: the electric heat and the transferred heat, and their proportion was confirmed as a range of 7:3–9:1. Under a fixed square side section, which is an optimal scenario, both thermal efficiency and electric heating efficiency with the furnace core length presented a parabolic variation. These findings may provide some guidelines and insights for a better design and operation of AGFs.

EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems

Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models.

Carbon Emission Accounting Model for Comprehensive Medical Facilities Based on Population Flow

China is striving to reach a peak in its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. The accurate accounting of carbon emissions is important for achieving these dual carbon goals. An extensive literature review and field measurements were conducted to investigate the specific impact of population density on carbon emissions in large integrated healthcare organizations. This research uses VOSviewer to visualize the literature analysis. We determined that the flow of people is a key factor affecting carbon emissions during the operational phase of large-scale comprehensive medical institutions. Through field measurements, the relationship between the density of pedestrian flow and indoor environment measurements was derived, and the incremental equipment operating loads caused by changes in the indoor environment were analyzed. Using the carbon emission factor method advocated by the IPCC, a carbon emission accounting model based on different flow intervals was constructed, and the energy consumption of different equipment was fully considered according to its proportion. The validation results showed that the error between the calculated value and the actual values of the model was 3.07% (less than 5%), which has good validity. The model calculates the direct and indirect carbon emissions in the operational phase based on the population flow perspective, which can provide a reference for the energy-saving design and green operation of large-scale comprehensive medical institutions. The research will continue to focus on the population flow, and the accounting model will be further optimized through machine learning algorithms.

Study on flexible job shop scheduling problem considering energy saving

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation.

Study of water-side free cooling technology for power lithium battery plant

Power lithium battery production process needs to consume a lot of energy. In this paper, an energy consumption analysis of a representative company was conducted, the energy consumption of the facilities that provide support for the process is higher than the energy consumption of the process equipment itself, which greatly affects the cost of battery manufacturing. Air-conditioning system as the most important energy-consuming system of the facilities, accounting for 25% to 45% of the total electricity consumption. The energy consumption of the cooling source system dominates, even in winter its energy consumption can reach 14% of the total energy consumption. On this basis, this paper analyzes the chilled-water-use characteristics of the air-conditioning terminal in winter through theoretical analysis, the chilled water temperature is therefore optimized according to the real-time outdoor humidity. The chilled water temperature is linearly increased after the humidity ratio fall below 7.5g/kg(a), reaching a maximum value of 12.1°C at 6g/kg(a). In addition, the cooling tower free cooling technology based on the optimized chilled water temperature is innovatively proposed to fully explore the energy-saving potential of the technology to achieve the energy saving of the cooling source system. TRNSYS simulation results show that after optimizing the chilled water temperature, the time period for cooling tower individual cooling under winter operating condition is extended by 60%, and cooling source system energy saving rate can reach 49.0% and the cost saving rate 48.0%, both of which are higher than 15% compared to the un-optimized chilled water temperature.

Energy-Saving Control Strategy for Multisleep States of CNC Machine Tool Considering Components Priority

As the representative energy-consuming equipment in the manufacturing industry, machine tools consume lots of energy during the non-machining period, which leads to low energy efficiency. The existing strategies do not consider the startup sequence among components, which causes the control parameters to fail to run during the practical startup process, and this paper conducts energy-saving research to address the issues. The relationship of priority among components was used as the constraint to obtain an energy-saving control strategy for multi-sleep states of machine tools considering components priority. Furthermore, the improved whale algorithm was applied to solve the energy-saving control strategy of the machine tool, and the search and convergence ability of the algorithm was improved by introducing the chaotic convergence factor and adaptive weight coefficient. Finally, the energy-saving strategy was utilized with a vertical CNC machine tool as an experimental example to mill the end face of the randomly arriving workpieces. The results show that this changed algorithm has a better solution speed and effect than the ordinary whale algorithm and some other improved whale algorithms. Compared with the practical control process and other control strategies, the control strategy considering components priority can obtain more stable productivity and better energy-saving effect.

Morocco’s path to a climate-resilient energy transition: identifying emission drivers, proposing solutions, and addressing barriers

Morocco is currently at a critical juncture, facing a pivotal decision regarding its future energy transition and standing at the crossroads of its energy trajectory. The dilemma lies in whether to prioritize energy efficiency (reducing energy consumption and promoting the adoption of electric vehicles) and energy sobriety (limiting the frequency of using energy-consuming equipment) or to pursue the decarbonization of the grid through enhancements in fossil and nuclear production, gradually transitioning to a 100% renewable mix. In an effort to foster a broader contemplation, this study illuminates these concepts, encompassing an analysis of the CO2 emission drivers utilizing the Kaya equation and an exploration of the challenges and opportunities associated with the net-zero challenge and a successful energy transition, including critical materials and policy landscapes. Furthermore, the study delves into Morocco’s advancements across these three pillars of the energy transition.

Optimal electrode configuration and system design of compactly-assembled industrial alkaline water electrolyzer

The production of green hydrogen by water electrolysis is an effective way to realize the utilization of renewable energy and decarbonization, efficient water electrolysis equipment is of great significance to reduce energy consumption and accelerate the deployment of hydrogen production infrastructure. This paper reports the optimal electrode configuration adapted to actual industrial conditions. The aim of this paper is to study the electrode structure effect and system design of compactly-assembled industrial electrolyzer under the actual operating conditions, so as to find out more suitable electrolyzer configuration parameters. Firstly, the principle and structural parameter design of industrial electrolyzer are carried out, and compactly-assembled industrial alkaline water electrolyzer (22 cells) was manufactured. Secondly, a balance of plant (BOP) system was built and the fully automatic operation process was designed. Finally, a system-level alkaline water electrolysis (AWE) hydrogen production system (including compactly-assembled electrolyzer and BOP) was established. The effect of different expanded meshes and woven meshes on the energy consumption of AWE equipment was tested, and the system component cost was analyzed. Industrial system-level test results show that the electrode dominant parameters affecting AWE performance are different under varying current densities. From the perspective of industrial application, it is more suitable for the strand width and pore width of the woven mesh to be about 0.2 mm, it achieved 4.3kWh/m3H2@4000 A/m2 based on the optimized configuration parameters. This study is helpful to the selection of electrode parameters and optimal configuration for industrial AWE equipment, at the same time, it provides a reference for the design of electrolyzer, BOP and system control process.

Research into a Marine Helicopter Traction System and Its Dynamic Energy Consumption Characteristics

As countries attach great importance to the ocean-going navigation capability of ships, the energy consumption of shipborne equipment has attracted much attention. Although energy consumption analysis is a guiding method to improve energy efficiency, it often ignores the dynamic characteristics of the system. However, the traditional dynamic analysis method hardly considers the energy consumption characteristics of the system. In this paper, a new type of electric-driven helicopter traction system is designed based on the ASIST system. Combined with power bond graph theory, a system dynamic modeling method that considers both dynamic and energy consumption characteristics is proposed, and simulation analysis is carried out. The results indicate that the designed traction system in this study displays high responsiveness, robust, steady-state characteristics, and superior energy efficiency. When it engages with helicopter-borne aircraft, it swiftly transitions to a stable state within 0.2 s while preserving an efficient speed tracking effect under substantial load force, and no significant fluctuations are detected in the motor rotation rate or the helicopter movement velocity. Moreover, it presents a high energy utilization rate, achieving an impressive energy utilization rate of 84% per single working cycle. Simultaneously, the proposed modeling methodology is validated as sound and effective, particularly apt for the dynamic and power consumption analysis of marine complex machinery systems, guiding the high-efficiency design of the transmission system.

Energy Efficiency Analysis of the Discontinuous Reception Scheme in 5G NR Communication Systems

The purpose of this work is to analyze the energy saving schemes of user equipment in the first releases (Release 15) of the fifth generation (5G NR) cellular mobile communication systems, as well as to identify possible ways to improve the energy efficiency of these systems. The paper describes the identified drawbacks of the existing discontinuous reception scheme (DRX-scheme) used for user equipment energy saving in 5G NR. The effectiveness of discontinuous reception scheme in reducing user equipment energy consumption was analyzed in detail by simulation study for various traffic models and a key scenario for the deployment of 5G wireless cellular mobile communication systems. Analysis of the simulation results shows that the discontinuous reception scheme does not reach the upper limit of possible energy saving for all the traffic models studied for a number of reasons described in this paper.

Pathways toward precise monitoring and low-carbon sustainability in fruit cold chain logistics: A solution enabled by flexible temperature sensing

Cold chain logistics (CCL) technology plays an irreplaceable role in the supply chain of fresh and perishable fruits. However, the rigidity and inflexibility of traditional temperature sensors limit the accurate monitoring of fruit temperature, resulting in temperature abuse, fruit quality degradation, and excessive energy consumption issues. In this paper, we proposed a flexible temperature sensing solution, which could provide efficient cold chain food quality control and reduce cold chain carbon emissions. The fruit cold chain experiment and temperature field simulation results show that the flexible precise temperature sensing solution can achieve approximately 20 % reduction in refrigeration carbon emissions due to the unique benefit of conformal monitoring. This paper has verified that the precise flexible temperature sensing technology could be combined with the intelligent cold chain control strategies. The energy consumption of refrigeration equipment can be greatly reduced, thereby promoting the development of sustainable and precise cold chain logistics technology.

Evaluating carbon emissions from the operation of historic dwellings in cities based on an intelligent management platform

Historic dwellings in cities are considered to be an important resource on the way to carbon neutrality because of their great potential for low-carbon renewal. However, in addition to obligatory heritage conservation requirements, there is no uniformity in historic dwellings’ operation mode and conditioning equipment, which means detailed carbon emissions accounting is critical but challenging. To estimate the spatio-temporal distribution of carbon emissions from historic dwellings and thus support the development of decarbonization retrofit strategies, this research proposed a low-interventional carbon footprint accounting system based on a digital twin management platform. Multiple digital technologies are applied to monitor and evaluate the occupants’ lifestyles, indoor environment, user location, and equipment energy consumption. Based on backend algorithms, the spatio-temporal distribution of carbon emissions is inserted into the building plan to graphically present the evaluation results. Compared with traditional simulation methods, the proposed system enables the automatic updating of energy accounting parameters by fusing multi-source data into one platform, improving accounting efficiency and accuracy simultaneously. A case study in China proves the outstanding performance of the accuracy of accounting results, with an approximate daily spatial error of 20 % and a total accounting error of less than 15 %.

Research on the Multi-Equipment Cooperative Scheduling Method of Sea-Rail Automated Container Terminals under the Loading and Unloading Mode

A sea-rail automated container terminal (SRACT) plays a crucial role in the global logistics network, combining the benefits of sea and railway transportation. However, addressing the challenges of multi-equipment cooperative scheduling in terminal and railway operation areas is essential to ensure efficient container transportation. For the first time, this study addresses the cooperative scheduling challenges among railway gantry cranes, yard cranes, and automated guided vehicles (AGVs) under the loading and unloading mode in SRACTs, ensuring efficient container transportation. This requires the development of a practical scheduling model and algorithm. In this study, a mixed integer programming model was established for the first time to study the multi-equipment cooperative scheduling problem of a SRACT under the loading and unloading mode. A self-adaptive chaotic genetic algorithm was designed to solve the model, and the practicability and effectiveness of the model and algorithm were verified by simulation experiments. Furthermore, this study also proposes an AGV number adjustment strategy to accommodate changes in vessel arrival delays and train container types. Simulation experiments demonstrated that this strategy significantly reduces loading and unloading time, decreases equipment energy consumption, and improves the utilization rate of AGVs. This research provides valuable guidance for ongoing SRACT projects and advances and methodological approaches in multi-equipment co-operative scheduling for such terminals.

Enhanced Multi-Objective Evolutionary Algorithm for Green Scheduling of Heterogeneous Quay Cranes Considering Cooperative Movement and Safety

Heterogeneous quay cranes (HQCs) are the main energy-consuming equipment of automated container terminals, and they need to move from one bay to another along the rail and maintain a safe distance from one another. Improving the operational efficiency of HQCs and reducing the ineffective walking distance of HQCs are key to reducing the energy consumption of QCs. In this paper, an energy-efficient HQC cooperative scheduling problem is studied, and the HQCs are required to ensure safe and efficient operation. A multi-objective scheduling model is formulated to minimize the maximum completion time of containers, the average completion time of HQCs, and the total energy consumption of HQCs simultaneously. An Enhanced Multi-Objective Evolutionary Algorithm (EMOEA) is designed to solve this problem using a problem-feature-based encoding method to encode and initialize the population, a cooperative strategy to ensure the safe operating distance of HQCs, and a novel multi-objective evaluation mechanism with effective evolutionary operators. The results indicate that the different operational capacities of HQCs had a significant impact on the three studied objectives, especially for some large-scale problems, and that our algorithm outperforms three other well-known multi-objective algorithms in solving the EHQCCSP.

Analysis of Influencing Factors of Rural Residential Carbon Emissions Under the Background of Dual Carbon Target

This paper studies the carbon emission of rural residential buildings under the background of ' double carbon target '. Based on the characteristics of rural residential buildings and the current situation of carbon emission, the literature research method is used to identify the residential characteristics, enclosure structure, family situation, lifestyle, energy consumption equipment, renewable energy utilization and agricultural production function. The carbon emission influencing factors of 21 rural residential buildings in the use stage of rural residential buildings are helpful to evaluate the carbon emission of buildings more accurately, provide the basis for formulating carbon emission reduction strategies, and promote the sustainable development of rural residential buildings. It is of great significance.

An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning

As a significant paradigm change in reinforcement learning, federated learning (FL) has emerged to address the efficiency bottlenecks and privacy concerns of centralized training. However, the discrepancy of multi-energy demands among energy hubs (EHs) may cause undesirable instability of FL mechanisms. This study proposes a scheduling algorithm for adaptively controlling multiple EHs with combined heat and power, gas boiler, and electrical boiler. We first present the formulations of twin delayed deep deterministic policy gradient (TD3) to schedule the energy conversion under the time-varying price and demands of electricity, heat, and natural gas. The novelty of the proposed algorithm lies in developing a distributed TD3-agents training that consists of FL and matching-based agent-to-agent learning, namely matching learning (ML). Specifically, the FL is first introduced for collaborative training of EH's TD3 agents by sharing their semi-trained scheduling models. The ML innovatively proposed for addressing the training instability of FL is formulated as a matching game in which the poor-trained agents learn the scheduling knowledge from the agent who has analogous demand patterns and better reward. Simulation results display the advantage and feasibility of the proposed algorithm in terms of the speed of training convergence, energy consumption of equipment, and economic benefit.

Controlled reprocessing of leucoxene concentrate for environmental friendly production of titanosilicate – An effective sorbent for strontium and cesium ions

An environmentally friendly synthesis procedure of titanosilicate with sitinakite structure from quartz-leucoxene concentrates processing products for effective sorption of strontium and cesium ions has been designed. The proposed method minimizes the environmental impact by controlling the acidity of waste waters and reuse of fluorammonium component in the technological process of enrichment (fluorination) of mineral raw materials. The sitinakite synthesis procedure excludes the stage of additional mechanical activation of quartz-leucoxene concentrate using complex and energy-consuming equipment. The precipitate composition and formation of SiO2-TiOn complex aggregates is controlled through step-by-step hydrolysis of dissolved forms of compounds. The new approach reduces sitinakite synthesis temperature and allows regulating composition, crystallinity and sorption properties of titanosilicate. The sorption capacity of the obtained sitinakite by Sr2+ and Cs+ cations is 120 and 170 mg/g, respectively, and does not decrease when its crystallinity changes.

Recycle of waste tire rubber powder in a novel asphalt rubber pellets for asphalt performance enhancement

Asphalt rubber has been considered as an ideal road construction material due to its excellent rutting resistance, fatigue performance, crack resistance, and noise reduction functions. However, the preparation of asphalt rubber requires special equipment and takes higher cost. In this paper, a kind of asphalt rubber modifier pellets (ARP) that can be directly injected was prepared using a screw extruder, so as to simplify the production process and reduce equipment energy consumption during the synthesis of asphalt rubber. Industrial modifier PelletPAVE as control group, firstly, scanning electron microscopy (SEM) was employed to examine the internal microstructure of the two types of modifier pellets, and the storability were further evaluated. The rotational viscosity, High and low temperature rheological behaviors and fatigue properties of asphalt with two modifier pellets were investigated by rotational viscometer, dynamic shear rheometer (DSR), and bending beam rheometer (BBR), respectively. Finally, Infrared spectroscopy was used to explore the mechanism of modification. The findings revealed that 50% crumb rubber, 40% asphalt, and 10% calcium hydroxide are the optimal material compositions for preparing asphalt rubber pellets. The modifier particles coated with anti-sticking agent can achieve the same storability as PelletPAVE. The viscosity of the modifier pellets modified asphalt prepared is marginally higher than that of the PelletPAVE modified asphalt. Incorporating modifier pellets to asphalt demonstrates superior fatigue and crack resistance than PelletPAVE, as well as lower high temperature performance. Action mechanism of modified asphalt with both types of modifier pellets is regarded as a physical process. The asphalt rubber pellets prepared in this study reveal the merits of easy transportation, convenient direct injection, and wide application field.

A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals

The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.