Problem Of Energy Consumption Research Articles

Working Mechanism and Parameter Optimization of a Crushing and Impurity Removal Device for Liquid Manure

Aiming to solve the problems of easy clogging and high energy consumption of multi-way fertilization devices for liquid manure, a crushing and impurity removal device for liquid manure was designed by combining the physical characteristics of liquid manure and impurities, and building the corresponding test bench. The proposed device could crush flexible impurities such as straw and filoplume and intercept hard impurities with high density. The main structural parameters of the device were determined according to the survey analysis and the theoretical design. The influences of cutter head shape, cutter edge angle, cutter shaft speed, and cutting clearance on the disqualification rate and energy consumption of straw crushing were obtained by a single-factor experiment. Furthermore, the Box–Behnken central composite design method of the response surface was employed to investigate the effects of the cutter shaft speed, cutting clearance, and cutter edge angle on the disqualification rate and energy consumption of straw crushing. In addition, the working parameters of the device were optimized by employing the response surface method. On this basis, the mathematical relationship model among the disqualification rate, energy consumption, and all influencing factors was established. The results show that the optimal combination of working parameters includes a cutter shaft speed of 312 r/min, a cutting clearance of 1.4 mm, and a cutter edge angle of 45°. From the prediction model, the predicted failure rate was 4.15%, and the predicted energy consumption was 47.53 J. The verification experiment was then performed under the optimal combination of working parameters. The obtained disqualification rate was 4.08% and the energy consumption was 47.56 J, which met the design and work requirements.

Building energy-saving engineering based on ferroelectric ceramic parameter tester

Energy issues have always been one of the most concerned issues in the world. To solve the problem of building energy consumption, the future development of building energy-saving projects is more meaningful. This article aims to study the application of the parameter tester based on ferroelectric ceramics in building energy-saving projects. This paper proposes to use the orthogonal test method to elaborate and accurately calculate the energy-saving projects of green buildings and calculate the annual energy-saving rate and load rate of green buildings, to achieve real building energy conservation and reduce energy consumption. The comparative analysis method is used to conduct strict calculation and analysis of the four specific energy-saving systems in the enclosure structure, such as doors and windows, external wall insulation, roof, and sunshading, to more clarify the advantages and disadvantages of each component in the enclosure structure place. According to the experimental data of this article, under the premise that the energy saving rate of this article fully meets the national requirements exceeds 50%, in this model, in terms of the full life cycle cost, the cost of using the worst solution can be reduced by 614,900 yuan compared with the cost of the best solution.

Application of virtual reality technology in multimedia resource with a focus on the COVID-19 outbreak

In order to solve the problem of high energy consumption caused by unable to extract color space parameters in multimedia resource compression and transmission algorithm, the application of virtual reality technology in multimedia resource compression and transmission algorithm is studied. The research's main part is based on the Covid-19 Outbreak and the use of VR technology. The pixels are classified according to labeled and unlabeled, the multimedia resource allocation strategy is established, the resource target area is extracted by virtual reality technology, and the over-segmentation is controlled to a small extent, so as to control the quality of resources. In order to reduce the number of data transmissions and reception in the process of data aggregation in the network, a transmission signal reconstruction model is constructed, and the data is compressed and encoded. A compression transmission algorithm is designed, which uses compressed sensing and network coding to reduce the amount of data transmission. The experimental results show that the energy consumption of the compression transmission algorithm designed in this paper increases the slowest with the increase of single hop distance, and the energy consumption is also the lowest. Taking the bit rate of 0.25bpp as an example, the maximum energy consumption of the algorithm is 3.89J, which is 4.97J and 2.45J less than the direct transmission algorithm and the centralized compression transmission algorithm, respectively. Therefore, the algorithm can reduce energy consumption and prolong the life cycle of transmission network.

Photothermal Janus PPy-SiO2@PAN/F-SiO2@PVDF-HFP membrane for high-efficient, low energy and stable desalination through solar membrane distillation

Currently, membrane distillation driven by solar energy (solar membrane distillation, SMD) is an effective method to address the high cost and high energy consumption problem of conventional seawater desalination. However, the complex preparation of photothermal membrane and low photothermal conversion efficiency limit the development of SMD. In this work, we fabricated a photothermal Janus membrane (PPy-SiO2@PAN/F-SiO2@PVDF-HFP) with asymmetric wettability (hydrophilic/superhydrophobic), and demonstrated that due to the high light absorption rate (∼96.48 %) of polypyrrole (PPy) coating (photothermal layer), hydrophilic SiO2@PAN nanofibers (serves as skeleton) and superhydrophobic F-SiO2@PVDF-HFP layer (provides channels for vapor escape), the photothermal Janus membrane achieves high water flux (∼44.4 kg m−2 h−1) and superior solar energy utilization efficiency (∼92.20 %) under 1 sun illumination when the temperature difference between the hot feed and cold side water (ΔT) is 60 °C; moreover, the membrane can maintain the high salt rejection rate of 99.99 % after working for 56 hrs. Both experimental and numerical simulation results reveal that the SMD process using photothermal membrane reduces the temperature polarization and increases the water flux (the greater light intensity leads to the generation of more water flux). Increasing ΔT is beneficial to enhance the solar energy conversion efficiency; however, it would also increase the temperature polarization and energy consumption. This work can provide important insights for the development of SMD technology with high water flux and low energy consumption.

Energy-Saving Control Method of Building Central Air Conditioning Based on Genetic Algorithm

In order to solve the problem of huge energy consumption of building central air conditioning, the water system of building central air conditioning was taken as the research object and the genetic algorithm of energy-saving operation and control of building central air conditioning based on MATLAB was designed, so as to build the energy-saving control system model of central air conditioning. On the one hand, the feasibility of the optimized algorithm to solve the energy-saving problem of central air conditioning was proved through the optimization of genetic algorithm. On the other hand, the energy-saving effect was demonstrated through the operation parameters and the variation of air-conditioning energy consumption before and after the experiment. The feasibility and rationality of genetic algorithm to improve the energy-saving effect of central air conditioning was verified by the comparison of the experiment. The results showed that in the design load range of 30%–70%, the average energy-saving rate of central air-conditioning compressor was 16.8%. The maximum energy-saving efficiency was 37.4%, and the minimum energy-saving efficiency was 15.5%. The average energy-saving rate of water system was 26.8%. Therefore, the genetic algorithm had fast convergence speed and high solving accuracy, which was beneficial to improve the energy-saving ability of central air-conditioning water system.

Building Energy Consumption Control Based on BIM and Machine Learning

To solve the problem of building energy consumption (EC) and promote the development of green buildings in China, this paper simulates and predicts EC of building based on BIM and machine learning (ML), so as to provide optimization strategies for building energy conservation. Firstly, this paper uses designbuilder (DB) to simulate the building energy consumption of the design test. Then Support vector machine (SVM) is introduced to fit the functional relationship between energy consumption influencing factors and EC of building, and a EC prediction model is established. Finally, through range analysis, the importance ranking and optimal scheme of six energy consumption influencing factors are obtained. Taking a teaching building in Chengdu as an example, the accuracy of the prediction model is verified, which provides a theoretical basis for the optimal design of building energy conservation.

Predicting the photosynthetic ammonia on nanoporous cobalt zirconate via graph convolutional neural networks

Photocatalytic nitrogen fixation can synthesize ammonia aided by the solar energy as the driving energy, overcoming the problem of energy consumption in traditional thermal catalytic ammonia synthesis. However, it remains a huge challenge to screen the photochemical yield of different catalysts efficiently unless sufficient experimental data. Hence, neural work methods are developed to construct the structure−property relationship between nanoporous cobalt zirconate and their activities in the synthesis of ammonia. Comparing different deep-learning processes, it can be found that a convolutional neural network based on graphs can predict the ammonia yield perfectly only with the help of nitrogen adsorption data. With several structural parameters such as pore volume and surface area, the trained model can be used to estimate the optimal yield at 16.51 mmol·g−1·h−1 with more than 97% accuracy on contour map on a certain condition. All these results open an innovative way to improve photocatalysts with the non-experimental strategy, which has a great potentiality in the cross application of catalysis and artificial intelligence.

Maximum Power Point Tracking Control of PV-TE Hybrid Power Generation System in Greenhouse

The application of photovoltaic-thermoelectric (PV-TE) combined power generation system in greenhouse is an effective way to solve the problems of high energy consumption and high pollution. In order to improve the efficiency of the PV-TE system, maximum power point tracking (MPPT) control is required. Aiming at the shortcomings of the traditional incremental conductance (INC) method, which is fixed in step size, a hyperbolic tangent-type adaptive variable step length INC method is proposed. The method takes advantage of the monotonically increasing and the fast changing speed of the hyperbolic tangent function, so that the step length can be adjusted rapidly and adaptively according to the change of external environmental conditions such as light intensity. The simulation results show that the proposed method can rapidly track the maximum power point when the illumination intensity changes drastically, and meanwhile it has smaller steady-state error and can realize MPPT control well.

Efficiency Evaluation and Analysis of Low-Carbon Tourism and Ecological Construction Based on DEA Model

The rapid development of tourism in China has also caused a lot of energy consumption and serious environmental problems. In the context of sustainable development, low-carbon tourism has become the consensus of management departments, relevant industries, and academia. In environmental-related fields, DEA is considered as an excellent efficiency analysis tool because of its powerful optimization ability. The final variable of DEA method is weight. In order to avoid the influence of human factors with predetermined weights in tourism analysis, this paper studies the evaluation of tourism ecological efficiency based on DEA model. China’s overall tourism ecological efficiency showed a fluctuating upward trend, rising from 0.853 in 2018 to 0.906 in 2021. The national average efficiency over the past four years is 0.855, which is at a low level, indicating that the construction of tourism ecology is still in its infancy, and there is still much room for improvement in the future. As the tourism industry system involves many aspects, such as natural ecology, humanities, economy, and so on, it is an extremely complex system. The research on the ecological efficiency of tourism cannot simply rely on the knowledge system and thinking of a single discipline. Therefore, combined with the relevant theories and knowledge of tourism, management, industrial economics, regional economics, industrial ecology, and other disciplines, this paper makes a systematic study on the problems and countermeasures of Qiaozhai.

PINCH-INTEGRATIONAL OPTIMIZATION OF HEAT EXCHANGE NETWORK OF THE HYDROLYTIC SULFURIC ACID CONCENTRATION PROCESS

The paper considers the question of the possibility of thermal energy saving at an industrial enterprise. As a means of optimizing energy consumption, one of the methods for integrating chemical-technological processes, pinch analysis, was used. It has been established that the problems of significant energy consumption are relevant for researchers and industrialists around the world, and scientists see their solution primarily in the development of alternative energy sources and modern methods of energy saving with well-predictable results. It is indicated that the target functions in this case can be combined: financial-energy and energy-environmental, since exactly these kinds of results are provided by the very essence of energy-and-resources saving applied to the industrial process.
 At the initial stage of the study, an analysis of thermal energy consumption structure provided by apparatuses in hydrolytic sulfuric acid concentration process was made. Based on its results, it was found that the existing minimum temperature difference in the system is far from the optimal and technically achievable value. Taking into account the confirmed energy-saving potential, its value was estimated. For this, the value of the present heat recovery in the system is established through calculation, and the volume of energy coming from external heat carriers and refrigerants is determined. Based on the calculation results, a Grid diagram and Composite curves of the specified technological process were constructed.
 At the second stage, optimization measures, which began with the choice of a new, lower value of the minimum temperature difference for the entire heat exchange network of this industrial plant, were carried out. For this value, shifted composite curves were constructed and an updated grid diagram was developed. In the integrated heat exchange network, there is three additional recuperative heat exchanger and were revised the operating modes of devices that it was decided to save. Based on the optimization results, a technological scheme of the hydrolytic sulfuric acid concentration process was designed while maintaining the key elements of the production technology.
 The result of the work is an optimized heat exchange network of the industrial enterprise section, which makes it possible to increase the thermal energy recuperation by 32,7 %, while reducing the consumption of external heat carriers by 30,3 %, as well as external refrigerants – by 50,1 %. The results obtained indicate a very high economic efficiency and a prospects of implementation the project into production.

High-Throughput Screening of Bicationic Redox Materials for Chemical Looping Ammonia Synthesis.

Ammonia recently has gained increasing attention as a carrier for the efficient and safe usage of hydrogen to further advance the hydrogen economy. However, there is a pressing need to develop new ammonia synthesis techniques to overcome the problem of intense energy consumption associated with the widely used Haber–Bosch process. Chemical looping ammonia synthesis (CLAS) is a promising approach to tackle this problem, but the ideal redox materials to drive these chemical looping processes are yet to be discovered. Here, by mining the well‐established MP database, the reaction free energies for CLAS involving 1699 bicationic inorganic redox pairs are screened to comprehensively investigate their potentials as efficient redox materials in four different CLAS schemes. A state‐of‐the‐art machine learning strategy is further deployed to significantly widen the chemical space for discovering the promising redox materials from more than half a million candidates. Most importantly, using the three‐step H2O‐CL as an example, a new metric is introduced to determine bicationic redox pairs that are “cooperatively enhanced” compared to their corresponding monocationic counterparts. It is found that bicationic compounds containing a combination of alkali/alkaline‐earth metals and transition metal (TM)/post‐TM/metalloid elements are compounds that are particularly promising in this respect.

Superhydrophobic tough hierarchical porous thermal insulation composites prepared by in situ formation of silica aerogel in collagen fiber matrix

AbstractThe serious problem of energy consumption and the requirement for sustainable development has attracted people's extensive attention. The application of high‐efficiency thermal insulation materials can reduce consumption and waste in the process of energy transfer, and is the most effective way to save energy and protect the environment. Among many thermal insulation materials, silica aerogel, especially biomass fiber‐reinforced silica aerogel with good mechanical strength, shows great practical application potential. In this article, the collagen fiber‐silica aerogel composite (MCFF@SiO2) was successfully prepared by in situ sol–gel method and freeze‐drying method, innovatively using skin collagen fiber matrix with natural porous skeleton as reinforcing template. MCFF@SiO2 has good thermal stability, and has a hierarchical porous structure formed by filling nanoscale mesopores of silica aerogel in the macropores of collagen fiber matrix, thus delaying the spread of heat. Particularly, MCFF@SiO2 exhibits good thermal insulation performance, and its thermal conductivity is 0.068 W/(m K), which is lower than that of MCFF. In addition, MCFF@SiO2 shows superhydrophobicity, good mechanical properties, excellent flame retardancy, and self‐extinguishing performance. The prepared composite aerogels have potential application value in the field of thermal management, and this work is expected to provide inspiration for the preparation of biomass‐based porous functional materials.

Application of an Internet of Things Oriented Network Education Platform in English Language Teaching

China’s information technology development is rapid; the organic combination of science and technology and education has promoted the reform of the education system; the education platform supplemented by mobile devices provides students with a new learning mode; student learning is no longer limited by the traditional education system; and learners can use mobile terminals for online learning, self-control learning steps, arrange learning time, and be able to combine their own deficiencies to strengthen learning content. Therefore, this paper discusses the design of English education teaching system in the Internet of Things technology environment, in order to provide students with a good learning environment and improve students’ English integration strength. This paper analyzes the research status of mobile learning at home and abroad, expounds the construction principles of mobile English learning system, and designs and studies the overall structure and functional modules of the system. The application of the system can replace the traditional English learning method and can enable students to improve their English ability through mobile devices. In view of the problems of poor teaching effect and high energy consumption in the traditional Internet of Things education platform, this paper puts forward a teaching effect evaluation method of Internet of Things education platform based on long-term memory network. By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. Finally, through the study of the model, the teaching effect of the Internet of Things education platform is evaluated. The experimental results show that when the method is used to evaluate the teaching effect, the operating energy consumption accounts for 84% of the total energy consumption of the system, which proves the effectiveness of the method.

Influencing Factors for an Integrated Model of Green Building Energy Consumption Using BIM Dynamic Simulation and Multiobjective Decision-Making

Multiobjective decision-making, also known as multiobjective optimization, indicates that the needs of numerous goals impact the system scheme’s selection and decision-making direction. The rapid economic development and the acceleration of urbanization have promoted the prosperity of the construction industry, but at the same time, the problem of building energy consumption is also becoming more and more serious. In order to overcome this issue, this study proposes an optimization method for the impact selection of green building energy consumption. The machine learning (ML) algorithm, random forest (RF) is used to discover and design criteria relating to the influence of green building energy consumption. Then, based on the data of building information modeling (BIM) dynamic simulation, the optimization of the RF model in multiobjective decision-making in green building energy consumption is presented. Furthermore, a comparative study of the proposed system with the existing systems and deep learning (DL) models is also conducted. Performance of the proposed system is measured in terms of accuracy and ROC curve. The proposed system achieved a training accuracy of 95% and a testing accuracy of 83% which is superior to the earlier approaches. The experimental results show that the RF algorithm can effectively determine the relationship between the influencing factors of green building energy consumption. This approach also enables policymakers to better understand the complex relationships between green building energy consumption, which may subsequently improve the acceptability of decision-making.

INVESTIGATE THE INFLUENCE OF THE WELDING SEAMS PROFILE ON DRIVING ENERGY FOR INDUSTRIAL ROBOTS

This paper focuses on investigating the influence of the welding seams in the working space on the driving energy for the six degrees of freedom FD-V8 industrial welding robot. The system of kinematics equations is built on the basis of multi-bodies mechanics theory and industrial robotics theory. The AGV algorithm is applied to solve the inverse kinematics problem (IKP) for each specific welding seam. The Lagrange-Euler method is used to build the differential equations of the robot motion. The robot driving energy is determined on the basis of solving the inverse dynamics problem (IDP). The results of this study have important implications in designing an appropriate welding seams trajectory to reduce energy consumption, as a basis for step by step building the optimizing energy consumption and production costs problem meeting the challenge of the current energy shortage in modern industrial production.

Dynamic coupling real-time energy consumption modeling for data centers

Data center energy consumption modeling is the basis for realizing efficient power management and participating in the integration of power systems. However, the existing static modeling methods are difficult to analyze the problem of real-time energy consumption and heat-transfer characteristics of the data center. To solve the aforementioned problems, a dynamic coupled energy consumption model (DCEC) including IT device and cooling device based on Matlab/Simulink is proposed in this paper. Taking a real data center composed of 1000 servers as an example, the simulation is carried out to validate the effectiveness of the DCEC model.

Energy-Efficient Resource Allocation for UAV-Assisted Vehicular Networks With Spectrum Sharing

Vehicular networks are envisioned to deliver data transmission services ubiquitously, especially in the upcoming autonomous driving era. Accordingly, the high data traffic load poses a heavy burden to the terrestrial network infrastructure. Unmanned Aerial Vehicles (UAVs) show enormous potential to assist vehicular networks in providing services. In a dense UAV-assisted vehicular network with a large number of users, spectrum sharing is leveraged for alleviating the spectrum scarcity. However, the increasing data traffic still leads to the UAV energy consumption problem. This paper considers a specific network scenario where a UAV transmits its cached content files to vehicular users over UAV-to-vehicle (U2V) links while vehicle-to-vehicle (V2V) links reuse the U2V spectrum for safety-critical message exchanges. To improve the UAV's energy efficiency while guaranteeing users’ quality of service (QoS), we jointly optimize content placement, spectrum allocation, co-channel link pairing, and power control, which are the key factors affecting energy efficiency and QoS. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by combining Hungarian and DDQN. We perform system performance evaluations, demonstrating that our approach can not only improve the UAV's energy efficiency while satisfying the users’ QoS requirements but also increase the timeliness of making decisions.

Research on intellectual property protection, innovation, and data protection algorithm based on wireless communication network

In today's world, with the developing technologies, data protection has become an absolute requirement, many organizations are focusing on the security of the data and information. Because many unauthorized individuals can hack computer system, putting the confidential files of the organization at danger. Therefore, there is need to employ various data security techniques in order to secure information. Furthermore, to resolve the problem of high energy consumption caused by large amount of data transmission in data protection algorithm, a data protection algorithm based on wireless communication network is proposed. Furthermore, we investigated that nowadays the protection of intellectual property is become significant issue. Because intellectual property security is essential for promoting innovation. Without intellectual property protection, individuals and firms would not gain the full advantages of their creations and would devote less time to development. The communication channel and data transmission link are established. Each node calculates the residual energy when clustering and sends it to the cluster head before the link is generated. The random number of nodes is encrypted and sorted according to the order of data link. Based on the wireless communication network, the privacy data within and between clusters are fused, and the data difference of each node is calculated respectively. Each cluster head uses homomorphic transformation to realize data sharing. The data protection algorithm is designed. The node data is partitioned and encrypted and decrypted by hybrid operation. The fusion results are uploaded to the base station to complete data protection. Apart from this, in this research, we proposed various methodologies in order to secure information as much as possible including text, photography, forms, videos and many more. Experimental results show that compared with CPDA algorithm, CMT algorithm and HRCDA algorithm, the data protection algorithm designed in this paper consumes the least total energy in each round. The algorithm can protect the privacy of data, reduce the consumption of data, and extend the life cycle of the whole network.

Influence of Different Passing Methods of Physical Fitness in Football Using Deep Learning.

Deep learning is a new direction in the field of machine learning, which learns the inherent laws and levels of data sample representation. The information gained during learning plays an important role in interpreting data such as text, images, and speech. This paper aims to study how to analyze and study the physical energy consumption of passers and receivers in different passing methods in football based on deep learning. This paper proposes the problem of physical energy consumption, which is based on deep learning, then elaborates on the concept of deep learning and related algorithms, and designs and analyzes the case of physical energy consumption of athletes. The experimental results showed that the average heart rhythm (184.35) of the subjects in the first and third experiments was more than twenty points higher than the average heart rhythm (159.85) of the kickers in the second and fourth experiments. Different passing styles have significantly different effects on the physical energy expenditure of players and defensive receivers.

Highly efficient and low pollution catalytic oxidation of ramie degumming by NHPI

A new ramie degumming method, N-Hydroxyphthalimide (NHPI) catalytic oxidation degumming method (NCODM), was proposed to solve the environmental pollution problems as well as the energy consumption problems of the traditional alkaline degumming method (TADM) and the 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidation degumming method (TODM). In this paper, the effect of parameters on the properties of ramie fibers degummed by NCODM was investigated. Then, the degumming process was optimized by comparing with the different degumming methods using central composite design method. The results showed that the H2O2 concentration and reaction time were significant factors that determined the properties of ramie fibers. The optimum H2O2 concentration was 16.70 g/L and the optimum reaction time was 19.38 min. The time required for NCODM was only 79.38 min, which was 26.46% and 62.19% of that of TADM (300 min) and TODM (127.64 min), respectively. Moreover, the chemical oxygen demand (COD) value of the wastewater from NCODM was 31% and 3% lower than that of TADM and TODM, respectively. Therefore, NCODM has great potential to replace the TADM and TODM for ramie degumming.