Energy Consumption Of Equipment Research Articles

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

Occupancy and equipment usage prototype schedules for building energy simulations of office building types in China

The accuracy of occupancy and energy-consuming equipment schedules significantly influences building energy simulations. Existing standards provide generalized schedules that do not fully capture variations across different office building types and periods. This study utilizes questionnaire data from office buildings across four Chinese cities to extract refined prototypes for occupancy and equipment usage schedules using hierarchical clustering analysis. Specific schedules are developed for summer workdays, winter workdays, summer weekends, and winter weekends, covering HVAC, lighting, and office equipment. For instance, the extracted lighting schedule increases annual energy consumption intensity by 25.35% compared to standard schedules. Additionally, XGBoost models identify key factors influencing equipment usage; for HVAC, floor number emerges as most significant. The study's prototypes offer more realistic inputs for building energy simulations, enhancing accuracy and guiding energy-efficient building design and management strategies in China.

Twin transitions across enterprises: Do digital technologies and sustainability go together?

This paper examines linkages between digitalisation and information and communication technology (ICT)-related environmental sustainability business practices. Using a unified modelling framework and firm-level data from Ireland over 2020–2022, it provides novel evidence on complementarities between the use of digital technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) and the propensity of firms to adopt ICT-related environmental sustainability measures. The results indicate that the adoption of ICT-related sustainability measures has been uneven across groups of firms, sectors, and regions. The descriptive evidence shows that the most adopted measures are disposing of or recycling ICT equipment while the least adopted measures are measures to reduce the energy consumption of ICT equipment. The estimation results indicate that digitalisation is associated with a higher propensity of firms to adopt ICT-related sustainability practices over and above other firm characteristics such as firm size, employing ICT specialists, and investing in intangible assets. Firms using AI are more likely to adopt ICT-related sustainability practices. Taken together, the results of this paper suggest that targeted policy measures to incentivize more firms, in particular small and medium-sized, to adopt digital technologies and ICT-related environmental sustainability measures, could foster the twin digital and green transitions.

Co-optimization of virtual power plants and distribution grids: Emphasizing flexible resource aggregation and battery capacity degradation

Coordination between virtual power plants and active distribution networks is crucial as these plants increasingly aggregate distributed resources within the power system. This study introduces a bilevel optimization framework to coordinate the scheduling of multiple virtual power plants and an active distribution network using pricing strategies for energy and reserves. The upper-level optimization minimizes total operating costs by incorporating bidding plans of the active distribution network in various markets, its interactions with multiple virtual power plants, and operational costs. The lower-level optimization maximizes revenue for each virtual power plant, considering both battery capacity degradation costs and operational costs of various resources. To facilitate solutions, this research developed a nonlinear transformation method for modeling capacity degradation. Based on the dispatching strategy from the virtual power plant, this study uses the squared difference between energy consumption of equipment for controllable loads and the strategy as the optimization target to derive control strategies for two equipment types. Results show that the framework effectively integrates dispatch and control strategies without oversimplifying the system model, proving its applicability in various scenarios with diverse resource compositions.

DESENVOLVIMENTO DE PROTÓTIPO UTILIZANDO MICROCONTROLADORES PARA CONTROLE DE EFICIÊNIA ENERGÉTICA EM RESIDÊNCIAS UNIFAMILIARES

The search for solutions that aim to improve the way we use electrical energy in our daily lives is of great importance when it comes to sustainability. Energy efficiency plays a fundamental role in the development of sustainable technologies. In a period of high demand for electricity and concerns about the environment, energy efficiency has become increasingly important, both in industrial and domestic applications. In this scenario, microcontrollers play a fundamental role in optimizing and controlling electrical energy. Among its various applications is monitoring and managing energy consumption in real time, making intelligent decisions to minimize waste. These systems, when integrated with sensors and other devices, allow the development of solutions that not only save energy, but also make environments smarter and more sustainable. Observing this context, this study aims to develop a device capable of providing accurate information about the electrical energy consumption of equipment. The developed prototype aims to provide detailed information about the energy consumption of monitored equipment, contributing to better energy management. The developed energy meter prototype demonstrated the potential to contribute significantly to energy efficiency, by providing accurate and real-time data, thus resulting in a more intelligent use of the energy consumed, as, with knowledge of real-time data, it is possible to monitor and work on energy optimization.

A Review of Three-Dimensional RGB Images and Drone Thermal Camera Images Merged for Building Information Modeling for Energy Audit

The site visit is the core of the energy audit, where you inspect, measure, and document the building's energy performance and efficiency. Various tools and techniques may be used for this purpose, such as a walk-through survey to observe the building's condition, a blower door test to measure air tightness and infiltration, a thermographic scan to detect heat loss and gain, a lighting audit to assess lighting quality, quantity, and controls, a plug load audit to quantify appliance and equipment energy consumption, a submetering or data logging system to monitor specific systems or zones, and a power quality analyzer to measure electrical parameters and harmonics. Additionally, it is important to interview the building's owner, manager, and occupants in order to gain their feedback and suggestions on energy performance and issues. This paper review of how a drone can help in the energy audit of a building using thermal images and RGB images from thermal camera which will be use in Building Information Modelling (BIM). There are a variety of relevant literatures that serve as sources of inspiration for the conduct of this study. One example is the utilization of teleoperated helicopters that are equipped with an infrared thermal camera. These helicopters are intended to be investigated for energy audits in order to readily assess the conditions of the structure. More improvements will be made to the collected photographs, such as merging them with three-dimensional RGB images. This will allow for an exact determination of which area of the building need upgrading in order to reduce the amount of energy that is emitted; this notion is similar to that of finite element analysis.

Investigating different scenarios of integrating solar-assisted carbon capture with combined cycle power plant and water desalination system

In this work, an optimal algorithm-based integration of solar thermal energy with carbon capture and desalination processes were proposed. The stripper columns, identified as the most energy-consuming equipment, were designed with constant capacities to ensure practicality and applicability. Results show that, without affecting the power plant's efficiency, using a solvent tank as an energy storage system (scenario-2) increases total CO2 capture from 45 % to 98 % compared to a part-time solar power system (scenario-1). Utilizing two solvent tanks reduces waste energy from 87 % to 67 % (scenario-3), while achieving 100 % CO2 capture. Using three different stripper columns (scenario-4) lowers the levelized cost of energy by 20 %. Incorporating an additional multiple effect distillation system (scenario-5) reduces levelized cost of energy and energy loss by 26 % and 74 %, respectively. Approximately 28 % of the total solar energy collected (4.64 × 106 MWh) was used to capture 1.57 × 106 ton.year−1 of CO2. The remaining energy was used in desalination to produce 9.4 × 106 m³.year−1 of fresh water, increasing overall energy efficiency from 28 % to 81 %, and reducing waste energy to 0.88 × 106 MWh, just 26 % of previous scenarios' waste.

Investigation on aeration efficiency and energy efficiency optimization in recirculating aquaculture coupling CFD with Euler-Euler and species transport model

Aeration plays a crucial role in maintaining the overall health and sustainability of the recirculating aquaculture system. Conventional aeration operates continuous and consumes 35.06 % of equipment energy consumption. How to optimize aeration while ensuring normal fish growth remains a great challenge. To address these challenges, this research paper established a coupled model of computational fluid dynamics and species transport model (CFD-STM). Aeration tests were carried out in a 33.3 m3 culture tank using a 0.31 m diameter aerator to find out the impacts of operating variables (aeration flow rate, bubble diameter, inlet velocity, and aerator position) on oxygen transfer using a microporous aeration system. A CFD-STM model was coupled to obtain the optimum values of aeration parameters. Results revealed that the Standard Aeration Efficiency (SAE) reductions by 32.1 % for 50 % larger aeration flow rate and 86.4 % for 50 % larger bubble diameter, as well as SAE achieved 5.67 kg/kWh with an aerator position scale factor equal to 0.48. In addition, the energy consumption of the Roots blower was compared for a single aeration and dissolved oxygen saturation. Results indicated that smaller bubbles microbubbles can effectively improve oxygen transfer coefficient. The energy consumption was reduced by 15.7 % for 50 % larger aeration flow rate and 38.6 % for 50 % lower bubble diameter. Based on the results, this study demonstrated that CFD is a useful tool to optimize the aeration system design and operation in order to enhance aeration efficiency. It provides a theoretical foundation the future optimization of energy conservation through intermittent aeration practices.

Based on the improved fuzzy analytic hierarchy and the TSE-MLR model energy consumption prediction of university: A case study

The accurate prediction of building energy consumption on university campuses is a significant research area. Current studies often focus on predicting the energy consumption of specific building areas or individual equipment, and typically consider only one factor, limiting the accuracy and applicability of the predictions. This study introduces the Time Segmented Energy-Multiple Linear Regression (TSE-MLR) prediction model, which integrates the improved fuzzy analytic hierarchy and the multiple linear regression algorithm. The model is compared with traditional (MLR, BP) and advanced (RNN) models, and their various indexes are discussed and analyzed. By collecting meteorological and energy consumption data from the study site over the past 12 years, the key factors affecting energy consumption on the university campus were identified using the improved fuzzy analytic hierarchy. Subsequently, the TSE-MLR model was trained using energy consumption data from 2010 to 2016 and validated using data from 2017 to 2019. The prediction results of the TSE-MLR model were compared with those obtained through Multiple linear regression, BP neural networks, and RNN. The results demonstrated that the TSE-MLR model significantly reduced the prediction error by 13.8 % and exhibited higher accuracy compared to the other models. Therefore, the TSE-MLR model introduced in this study offers a new and effective approach to predicting university energy consumption and supporting energy management using existing data from university building operations across different periods.

Automation of transportation of vapor of atmospheric deaerator in boiler installations

At present, when energy prices are unstable and periodically rise, enhancing the energy efficiency of thermal power plants, which implies the production of the same or larger amounts of energy with a lower consumption of energy resources, remains an urgent problem in the field of thermal power engineering. Such tasks aimed at reducing the consumption of energy resources for their own needs are quite urgent for manufacturing enterprises that use energyconsuming equipment, in particular steam boiler plants. Steam boiler houses contain a sufficient amount of energyintensive equipment, be it the boilers themselves or atmospheric deaerators, which are used to purify the boiler feedwater from corrosive gases. The article discusses the option of automating the process of closing the vapor during atmospheric deaeration in boiler plants by installing valves with automatic drives on the vapor removal pipelines. The article also analyzes the annual replenishment flow rate of the deaerator, assesses the quality of the condensate supplied to the deaerator, describes the conditions for opening and closing valves with automatic drives, and provides a calculation of the time delay required to remove vapor from the deaerator before closing the valves. A functional block diagram for controlling automatically driven valves is presented and its components are described. Thus, when implementing the described technology, significant savings in production steam spent on the deaerator will be achieved due to the closing of the deaerator vapor, with no chemically purified water supplied to the deaerator. This solution helps to increase the operating efficiency of the entire boiler installation.

Cryogenic-membrane separation process for helium extraction and ethane co-production from natural gas

In order to address the defects of high energy consumption and low efficiency in the single cryogenic separation process for helium extraction, this paper integrate post-expansion nitrogen cycle refrigeration with membrane separation to establish a novel natural gas cryogenic-membrane helium recovery process. Based on four mainstream processes for ethane recovery from natural gas, an optimal ethane recovery method is selected and incorporated into the proposed cryogenic-membrane helium recovery process. The process is then simulated using ASPEN HYSYS software. The effects of operating parameters on critical equipment energy consumption, helium product concentration and ethane recovery were evaluated. The simulation results indicate that in the proposed helium recovery process, the concentration of refined helium reaches 99.6 %, and the ethane recovery rate is 90.13 %. Compared to the single helium extraction process, the total compressor energy consumption in our process is reduced by 23.10 %, and the integrated energy consumption is reduced by 20.4 %.

Analysis model of energy consumption variables for data processing in high-performance computing systems

One of the main challenges in the efficient operation of a high-performance computing (HPC) center is the energy consumption generated by the operation of the data center where the HPC equipment is housed, mainly because this consumption is reflected in very high accounts payable, and this may affect the level of service offered to users. The study of the different factors and elements that can make energy consumption more efficient in these data centers provides an opportunity to focus these resources on elements that favor the use of HPC. The design variables provided by manufacturers to manage HPC systems and monitoring systems provide an accurate view of the behavior of these variables according to how they are used. HPC architectures are configured in a very particular way for each HPC data center, creating particular scenarios of operation and performance in each implementation. Various proposals and technologies have been developed for the analysis of the energy consumption of a data center, and the processing elements include a series of indicators and technologies that manufacturers have developed to determine the energy efficiency. This article seeks to identify this series of processing and performance variables, which affect the energy consumption of HPC equipment, for the implemented computing architectures based on the analysis of performance models to obtain a general over-view of their effect on energy consumption in a case study to identify the behaviors of particular job assignment factors and provide an analysis of the energy consumption under particular conditions.

Development of a mobile wind power plant for power supply to troops

The study examined the feasibility of using wind energy to supply energy to field military shooting ranges of the Armed Forces of the Republic of Kazakhstan. The object of study is a mobile wind turbine with a vertical axis of rotation. Theoretical studies have identified key factors influencing turbine modes and performance, which has led to the selection of optimal placement options. The proposed system took into account the power supply needs of troops in the field, with special attention to the energy-consuming equipment of field military shooting ranges as the main consumers. A comparative analysis has established the superiority of a mobile wind turbine with a vertical axis over traditional wind generators with a horizontal axis. The developed turbine design provides full coverage of the airflow regardless of direction, with an optimal blade angle of 60°. Theoretical studies included studying the airflow around the blade at different angles of attack, which provides insight into drag and lift. The basis for the development was the designed model of a mobile wind turbine, protected by a patent, characterized by mobility, sectionality and the ability to adapt to various environmental conditions. Its simple and compact design, combined with a vertical axis of rotation, ensures an uninterrupted power supply, especially at remote military installations and border areas. This research led to the development of a model for calculating the parameters of a mobile wind turbine suitable for powering troops in the field. The study demonstrates the potential of mobile wind power plants to significantly enhance energy reliability at military shooting ranges. The calculated results confirm the viability of mobile wind turbines in providing power support for shooting activities at these ranges

Improving the energy efficiency in a walking hearth type reheating furnace by energy balance method and optimizing the resources

Most metal industries use reheating furnaces (RHF) for their finishing operations. This RHF is highly energy-consuming equipment that heats the material inside the chamber for rolling or shaping using the by-product gases, natural gas, or oil as fuel. It is necessary to minimize or optimize the fuel consumption to the extent possible. By analyzing the plant operating data, plant measurements, and energy balance calculation, this work aims to determine the potential for decreasing the fuel consumption of a billet reheating furnace. Predictions are made by modeling operating data to reveal the hidden problems and uncover underlying issues. The study results in increasing productivity by 11 ​% while oil consumption was reduced by 14 ​%. These actions significantly decreased carbon emissions considerably and generated significant cost savings.

Dehydration of Organic Solvents from Ternary Mixtures Containing Toluene/Methanol/Water by Pervaporation.

The separation of a toluene/methanol/water ternary mixture is a difficult task due to the toluene/water and toluene/methanol azeotropes. In this article, low-energy pervaporation is proposed for the separation of the ternary azeotrope toluene-methanol-water. This work investigates the effects of feed temperature, feed flow rate, and vacuum on pervaporation and compares the energy consumption of pervaporation with that of distillation. The results showed that at the optimized flow rate of 50 L/h and a permeate side vacuum of 60 kPa at 50 °C, the water and methanol content in the permeate was about 63.2 wt.% and 36.8 wt.%, respectively, the water/ methanol separation factor was 24.04, the permeate flux was 510.7 g/m2·h, the water content in the feed out was reduced from 2.5 wt.% to less than 0.66 wt.%, and the dehydration of toluene methanol could be realized. Without taking into account the energy consumption of pumps and other power equipment, pervaporation requires an energy consumption of 43.53 kW·h to treat 1 ton of raw material, while the energy consumption of distillation to treat 1 ton of raw material is about 261.5 kW·h. Compared to the existing distillation process, the pervaporation process consumes much less energy (about one-sixth of the energy consumption of distillation). There is almost no effect on the surface morphology and chemical composition of the membrane before and after use. The method provides an effective reference for the dehydration of organic solvents from ternary mixtures containing toluene/methanol/water.

Construction and Application of Energy Footprint Model for Digital Twin Workshop Oriented to Low-Carbon Operation.

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects.

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

The increasing demand for energy is leading to global depletion of fossil fuels and growing environmental pressures, which are issues that need to be addressed. Machine tools are basic energy-consuming equipment in manufacturing systems. However, existing theoretical models ignore tool wear as well as workpiece material properties. This makes it difficult to further improve the accuracy of the model. Therefore, this study begins with the point of view of energy dissipation in the metal material removal process. A milling power model for computer numerical control (CNC) machines, considering tool wear and workpiece material properties during machining, is established. At the same time, milling is taken as the research object. A multi-objective cutting parameter optimization model is established to ensure the surface quality of the workpiece. In addition, the cutting energy consumption is taken into account in the developed models. Based on the multi-objective manta ray foraging optimization algorithm (MOMRFO), the Pareto-optimal solution set under multiple cutting conditions is solved. Finally, the experimental results of optimized parameters are compared with empirical parameters. The average prediction accuracy of the proposed energy consumption prediction model is above 91%. The experiments show that machining quality improves by optimizing the cutting parameters, with SEC, MRR, and Ra increasing by more than 44%, 53%, and 38%, respectively. The goals of reducing energy consumption and increasing productivity are achieved.

Research on Continuous Casting–Hot Rolling Scheduling Model Involving Reheating Furnace Conversion Mode and Improved Bat Optimization Solution Algorithm

As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming four conversion relationships, with the goal of minimizing the coupling function between the maximum completion time of the hot rolling and the energy waste of the reheating furnace, and modelling the scheduling of a continuous casting–hot rolling process. An improved ordered pair bat algorithm is used to solve for special process conditions of the proposed scheduling model. The effectiveness of the proposed model and algorithm is verified by solving the different slab charging sequence and conversion mode. Moreover, a comparative study of different types of batches is also conducted, and it is found that the model can save more than 5000 GJ per month for the steel mill, which can achieve the goal of effective energy saving and increasing enterprise profit.

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.

Prediction of fuel consumption and carbon dioxide emission when replacing gaseous fuels with renewable hydrogen or their mixture

The paper is devoted to predicting changes in fuel consumption and carbon dioxide emissions when industrial equipment is converted to heating with renewable hydrogen or a mixture of it with other gaseous fuels, such as natural gas. The authors developed a simplified methodology that is appropriate for assessing changes in the energy consumption of equipment and the environmental impact of fuel replacement at the stage preceding the equipment reconstruction. The peculiarity of the methodology is that it allows the calculation of fuel consumption and carbon dioxide emissions when fuel replacement is accompanied by the implementation of measures aimed at improving the energy efficiency of equipment. The methodology can be used for heating and thermal furnaces in metallurgy and mechanical engineering, units for heat treatment of raw materials in the mining and processing industries, heating devices in the food industry, and water and steam boilers. In the example of replacing natural gas with its mixture with renewable hydrogen of different compositions, it is shown that the calculation error using the proposed methodology in comparison with the results obtained by mathematical modeling of fuel combustion, gas flow and heat transfer in this furnace does not exceed 1.5%.