Problem Of Energy Consumption Research Articles

Design and Optimization of the 4-Bit Absolute Value Logic Gate Circuits

Absolute value computation of binary numbers has important applications in digital signal processing and arithmetic operations, but conventional circuit designs usually face high latency and energy consumption problems. In order to improve computational efficiency, this paper proposes an optimization method based on logic gate circuits aimed at reducing the number of physical components and simplifying the circuit structure. By using a truth table for logic simplification, a more efficient 4-bit absolute value calculation circuit is designed in this study. Experimental results show that the optimised circuit performs significantly in reducing latency and power consumption and maintains the accuracy of the operation. The results of the study provide a better implementation of digital circuit design for application scenarios that require efficient computation.

Simulation and optimization of DMC synthesis process based on conventional and advanced exergy analyses

Dimethyl carbonate has received more and more attentions as an organic solvent in electrolytes. Aiming at the problem of high energy consumption in the process of dimethyl carbonate (DMC) production, the root of the problem was analyzed by using exergy analysis, which provides a theoretical basis for the construction of energy-saving process. Firstly, the overall process of DMC production was constructed, including propylene carbonate (PC) synthesis, DMC synthesis, azeotrope separation and propylene glycol (PG) purification processes. Secondly, the conventional and advanced exergy analyses of the four proposed processes were carried out. The distributions of exergy and exergy destruction were obtained through conventional exergy analysis. The exergy destructions were further divided through advanced exergy analysis, and the avoidable parts were focused on. Based on the above analyses, the corresponding heat integration processes were proposed. Finally, the conventional processes and the heat integration processes were evaluated with the performance indicators of the exergy destruction, total annual cost (TAC), total energy consumption (TEC) and CO2 emissions.

Image acquisition based on computer vision technology for optimizing thermal energy in building environments and simulating VR interior design

With the acceleration of urbanization, the problem of building energy consumption has become increasingly prominent, and the construction of low energy consumption and high comfort building environment has become a hot research field. The purpose of this study is to explore the use of computer vision technology for image acquisition, optimize the thermal energy management in the building environment through intelligent analysis, and improve the energy efficiency and comfort of interior design. Through the establishment of image acquisition system based on computer vision, the indoor and outdoor environment is monitored in real time. A deep learning algorithm is used to process the acquired images and extract the thermal energy characteristics of the buildings. Combined with virtual reality (VR) technology, a 3D model of interior design was constructed to simulate the impact of different design schemes on energy consumption. The experimental results show that the real-time data obtained by computer vision technology can effectively identify the heat energy distribution in the building, and provide optimization suggestions for interior design. Simulation results of different design options show that the optimized design reduces energy consumption while improving occupant comfort. Therefore, computer vision technology shows a good application prospect in the thermal energy optimization of building environment. By combining VR interior design simulation, the technology not only improves the accuracy of thermal management, but also provides architects with more information-based decision support and promotes the development of smart buildings.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

A multi-scale method for the study of deep drying of ethylene with 3A zeolite

Coal-based ethanol to ethylene (ETE) is an emerging production pathway with the advantages of a short construction period, high product purity, and few by-products. N2 is commonly used in industry to regenerate zeolites during the ETE process, with the problems of excessive material consumption, energy consumption and environmental pollution. To solve this problem, this paper proposed a method of using the ethylene product as regeneration gas to regenerate the zeolite and recover the desorbed gas. In this work, we adopted a multi-scale simulation method combining microscale apparent diffusivity and macroscale mass transfer models. Reactive force field and molecular dynamics (ReaxFF-MD) were used to obtain an equation for the diffusion coefficient versus temperature. The start-up of the twin-tower temperature and pressure swing adsorption (TPSA) process was simulated using Computational Fluid Dynamics (CFD). The TPSA process achieved an outlet water content below 5 ppm, fulfilling the process specifications.

Energy Consumption Optimization for the Cold Source System of a Hospital in Shanghai-Part I: Analysis of Operating Characteristics and the Control Strategies of the Chillers

Background: Hospitals account for the most proportion of energy consumption in the public building sector. Chillers usually account for most of the overall energy consumption of the cold source system. Objective: To solve the problem of chillers' large energy consumption problem, novel technologies were developed, and achievements were patented. Methods: The operating characteristics influencing factors of the magnetic suspension centrifugal chiller (MSCC) and variable frequency screw chiller (VFSC) of a hospital in Shanghai were analyzed and discussed by actual measurements. Then, based on the operating characteristics of the chiller obtained from the analysis of the measured data, the cooling capacity was classified by the K-Means clustering method to obtain the startup strategy of the chillers. Results: The effects of the supply chilled water temperature, the supply cooling water temperature and variable cooling water flow rate on the maximum cooling capacity and coefficient of performance (COP) of both chillers were explored. The load distribution scheme was discussed based on the chillers' startup strategy. Conclusion: The average part load ratio operation scheme was the preferred chiller distribution scheme. A chiller's maximum allowable part load ratio should not exceed 80% during the low-load operation period and should not be less than 60% during the conventional operation period. It provided a reference for optimizing the chiller operation strategy to reduce system energy consumption.

SDERP: Stable‐aware differential evolution‐based routing protocol for mobile wireless networks

SummaryA mobile ad hoc network (MANET) is a network with non‐wired communications and non‐centralized administration where all its nodes are mobile. Firstly, one of the leading drawbacks of wireless networks is their energy consumption because the constituent nodes of the network have a restricted battery capacity yet greater consumption caused by the mobility of the nodes, their processing power, the frequent sending of data, and so on. Secondly, data packets may be lost due to traffic, noise, or the mobility of nodes, whose data transmission in real‐time applications would be compromised by this loss. In order to resolve the problem of energy consumption and that of the loss of data packets for MANETs, we propose a new stable‐aware differential evolution‐based routing protocol (SDERP). We include a fitness function to select the best new path from all new paths issued at the end of this algorithm. This fitness function is based on energy constraints and link stability. The performance of our proposal is compared with OPAOMDV‐EE, FT‐AORP, EE‐LB‐AOMDV and AOMDV. The results show that our SDERP has better network performance than the other routing protocols. Specifically, it obtains a packet delivery ratio that is higher by up to 17%, an overhead ratio reduced by up to 33%, an energy efficiency better by up to 39%, and end‐to‐end delay reduced by up to 16%.

Study on the Improvement of the Degradation Efficiency of Methylene Blue Using Pulsed Direct-Current Self-Power Supply.

Effective treatment of dye wastewater is currently a great concern and a research hotspot. Electrocatalysis has unique advantages in treating toxic and harmful refractory dye wastewater; however, it requires an external power supply, which increases energy consumption and cost. As a new energy collection technology, triboelectric nanogenerators (TENGs) have gained considerable attention. In this study, an origami multilayer spherical friction nanogenerator (Q-TENG) was developed for the removal of methylene blue (MB) from dye wastewater. The current and voltage output performances of Q-TENG were explored, and the removal and degradation mechanisms of MB were discussed. Results indicated that when the water wave acceleration a = 3 m/s2, the open-circuit voltage and short-circuit current reached the maximum values of 179 V and 9.4 μA, respectively. The self-powered catalytic degradation of MB using Q-TENG can produce more •OH and SO4-•, and the free radicals increase with increasing action time of Q-TENG, thus increasing the degradation efficiency of MB. This study provides a new strategy for solving the problem of high energy consumption during electrochemical reactions in wastewater treatment.

Research on energy-saving renovation of old oceanarium based on energy consumption monitoring technology

This study takes an old oceanarium in Jiangsu Province, China, as a case study and monitors the energy consumption values related to electricity, water and gas consumption for the whole year before the renovation of the oceanarium. Based on the energy consumption monitoring data, an in-depth analysis of the energy consumption defects of the oceanarium before the renovation is conducted using energy audit technology. We combine the characteristics of the oceanarium itself according to: (1) the results of the building energy audit; (2) the results of the envelope calculation; and (3) the latest energy-saving retrofit technology. We developed a more scientific building energy-saving retrofit programme. Using this method, we can accurately monitor the energy consumption problems in old oceanarium buildings and find building energy consumption defects. In this study, we monitor the real-time energy consumption of nine old government office buildings in Jiangsu Province, China using energy monitoring technology. We analyse and study the monitoring results and finally target the scientific energy-saving retrofits. This method can be used to carry out targeted energy-saving building renovation in the future and put forward new methods and ideas for studying the energy consumption of existing public buildings.

A truthful randomized mechanism for task allocation with multi-attributes in mobile edge computing

Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.

Smart Materials for Modern Facades: An AI-Powered Selection Process

Abstract According to the mid-21st century’s technological advancements, artificial intelligence has gained popularity as a transformative tool due to its immense integration in the evolving field of architecture. Evidently, it is being used in the building skin design to enhance the energy efficiency and to reduce the continuously rising energy waste and consumption problem in office buildings. Despite AI’s immense advancements, it lacks sufficient data with regards to the turf of smart skin design in office building for energy optimisation. Hence, this research aims to develop a framework for the optimal integration of artificial intelligence in the selection process of smart materials for energy efficient design for office buildings’ skin. To reach this aim, a mixed-method approach using quantitative and qualitative analysis will be implemented. First, theoretical research utilising archives and case study analysis is conducted to determine the various applications of AI in architecture, the components of a smart office building skin focusing on smart materials, the definition of energy efficiency and its measurement tools and the miscellaneous AI tools that are inaugurated to design an office building skin. As a result, a framework will be created by integrating the optimal AI tools in the selection process of smart materials for energy efficient smart skin design. Second, the applied studies are performed using empirical and experimental analysis to test and support the framework through case study analysis and comparison to test the framework obtained from the literature review aiming to cover the gap identified in the selection process of the materials.

Room-temperature flexible phase change material with high emissivity and high enthalpy for building energy saving

Summer heatwave events become more and more frequent, building cooling load continues to rise, and the traditional cooling method using air conditioning consumes a lot of energy. New technologies and materials with high efficiency, energy saving and low cost are urgently needed to solve the problem of high energy consumption in buildings. In this paper, by combining phase change storage technology with passive cooling technology of sky radiation, a new type of high-emission flexible composite phase change material (CPCM) was prepared using polypropylene hollow fiber (PP hollow fiber), Styrene-Ethylene-Butylene-Styrene (SEBS) and paraffin wax (PA). The prepared CPCM with excellent thermophysical properties, including a latent heat of 175.19 J/g, room temperature flexibility, the contact angle of the hydrophobic test of 129.15°, and the infrared emissivity of the atmospheric window of 0.942. The cooling experiment results show that the maximum temperature difference between CPCM and the environment is 2.6 °C lower than the ambient temperature. In addition, the simulation results show that the external surface temperature of the CPCM roof can be reduced by 23.15 K at the highest compared with a common roof, and the maximum energy saving can reach 13.24 W/m2. Compared with the high emissivity roof without CPCM, the external surface temperature is further reduced by 0.47K, the delay time is 50min, and the maximum energy saving can reach 0.76 W/m2. It's worth getting excited that CPCM can delay the opening time of air conditioning for 10 days. In addition, the application efficiency of cities in typical climate regions is simulated, and outstanding energy-saving efficiency is obtained, among which the maximum saving energy in Guangzhou is as high as 20.16 W/m2. The successful preparation of the novel CPCM material provides a new direction for the development of radiation-cooling technology.

Improved stabilization of loess soil using magnesium oxysulfate cement-based ecological composite binder

The technology of loess solidification plays a crucial role in addressing issues such as soil erosion, with the key material in this process being the loess stabilizer. To tackle the high energy consumption problem associated with traditional loess stabilizers primarily composed of Portland cement, this study employed a more ecological sustainable alternative by incorporating solid waste coal gangue and magnesium oxysulfate (MOS) cement as a binding agent for consolidating loess. The alterations in bulk density, porosity, mineral structure, and microstructure of the consolidated soil with different MOS content were systematically investigated and validated. The pore size of MOS-solidified loess decreased significantly to a range of 0.04–3.60 μm compared to 0.80–3.88 μm for pure loess. Scanning electron microscope (SEM), energy dispersive spectrometer (EDS) and thermogravimetric (TG/DTG) analysis revealed that the addition of MOS binder generated hydrated magnesium silicate (M-S-H), Mg(OH)2, and a small amount of 5Mg(OH)2·MgSO4·7H2O phase which filled the gaps between soil particles and bonded them together effectively. When the proportion of MOS binder added reached 11 %, the compressive strength of stabilized loess after curing for 28 days increased up to 9.4 MPa. After immersion in water for 24 h, the strength only decreased by 5.3 %, with a softening coefficient of 94.7 %. The test results consistently indicate that the enhancement in mechanical properties can be attributed to the binding effect facilitated by MOS. The ecological composite binder based on coal gangue and MOS demonstrates great potential in the field of soil stabilization.

Load prediction and energy efficiency improvement of steelmaking waste heat centralized heating systems under mild climate in China

HVAC energy consumption accounts for more than 50 % of the total building energy consumption globally and using industrial waste heat has huge potential for energy savings in central heating systems. Under the background of the use of renewable energy and the gradual rise of central heating in mild climate areas in China, it is worth paying attention to the problem of excessive energy consumption caused by the inaccurate load prediction of central heating systems when the ambient temperature changes. L City, Guizhou Province, is the first city with central heating in southern China. The temperature variation during winter in L City can manifest the climatic characteristics of a mild region. Therefore, based on the operational data of the steel waste heat central heating system in L City, Guizhou Province, China, during the heating season of 2022–2023, this study discusses the load prediction and energy-saving optimization strategy of the central heating system in a mild climate. SketchUp was used to conduct 3D modeling of the actual buildings in the heating area and TRNSYS was imported to simulate the system load. The difference between the heating load forecast and the actual operating data was analyzed in detail by using the calculation results. Based on the simulation results, the waste heat utilization strategy was adjusted and the influence of different heating schemes on the pump energy consumption under different load demands was discussed. The results reveal that based on the temperature change rule, the accurate load prediction can lead to a 28.64 % saving of the system energy. Concerning system energy consumption, by adjusting the water flow and choosing the large and small pump system, the energy saving amounts to 39.48 % compared with the traditional single-pump system. Meanwhile, in the transition season when the heating demand is less than 20 MW, the adjusted heating scheme can achieve a 38.5 % saving based on the constant flow scheme and a 5.4 % saving based on the constant temperature difference scheme. This article provides a scientific energy-saving solution for heating in mild climates using industrial waste heat, not only reducing energy consumption but also providing an important reference for the energy-saving renovation of heating systems in similar areas.

Towards stable and efficient nitrogen removal in wastewater treatment processes via an adaptive neural network based sliding mode controller

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration (SNH) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the SNH with nitrate nitrogen (SNO) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

The balance issue of the proportion between new energy and traditional thermal power: An important issue under today's low-carbon goal in developing countries

The stability of the new power system depends on the balance between controllable flexible resources and uncontrollable uncertain resources. For many developing countries and regions lacking advanced flexible resources, thermal power is the main force in solving the problem of new energy consumption. Finding the balance between new energy and traditional thermal power is the key for today's low-carbon goal. To solve this problem, this paper calculates the power range and climbing rate range of thermal power based on physical principles and actual data, and characterizes the characteristics of wind power, photovoltaic power, load and net load based on the five proposed volatility indicators. Then, a logically clear method for determining the required number of thermal power units based on the volatility indicators of new energy from the mechanism and principle perspective is proposed. Finally, under the premise of meeting the power balance and flexibility balance of the power system, the reasonable ratios between the installed capacity of new energy and the number of thermal power units are calculated in four seasons. The results indicate that the judgment system can provide guidance for the energy structure of developing regions.

Highway traffic planning and management from the perspective of sustainable development

Highway traffic system is an important infrastructure of the city and one of the key elements of urban sustainable development. However, for a long time, the concepts and methods of highway traffic planning and management are relatively lagging behind, leading to the serious traffic congestion, environmental pollution, energy consumption and land use conflict problems in the highway traffic system. Therefore, it is necessary to re-examine the traditional concepts and methods of highway traffic planning and management, and formulate a more scientific and reasonable highway traffic planning and management scheme from the perspective of sustainable development.

Ant colony optimization-based method for energy-efficient cutting trajectory planning in axial robotic roadheader

The traditional cutting trajectory of the axial robotic roadheader (spiral or reciprocating) is too simple to meet the requirements of adaptive planning. We proposed a method for planning the cutting trajectory of an axial robot roadheader based on ant colony optimization, which enables the machine to automatically adapt to the rock characteristics of the tunnel face, solves the problems of high energy consumption and low cutting efficiency in existing methods. This method uses machine vision to capture the fractures in the tunnel face, construct a grid map of the fractures, and calculate the fracture parameters within each grid, including the numbers, length, width, and density, through the connected domain method. Among them, the fracture parameters is used as input and the geological strength index as output. The BPNN is established to predict GSI of the grid environment. Based on this, an improved ant colony optimization for full coverage path planning is introduced, the cutting trajectory can be planned adaptively according to the geological conditions. The method is validated on a axial robotic roadheader, demonstrating its ability to adaptively plan cutting paths based on the geological conditions of the tunnel face. Compared to traditional methods, it reduces axial robotic roadheader energy consumption. This research enhances the intelligence level of coal mining robots, reduces energy consumption, and increases equipment lifespan.

Energy Aware On-Demand Routing Protocol Scheme of DSR Protocol (EAORP)

Recently, there has been a need for connectivity in places with no infrastructure. In order to meet this need, new technology known as MANET is used to fulfil the market demand. Despite the many benefits that MANET will provide, a number of shortcomings still need to be further studied, especially the energy consumption problems, that stand in the way of not allowing widespread acceptance of this technology. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management.

A genetic algorithm‐based virtual machine scheduling algorithm for energy‐efficient resource management in cloud computing

SummaryTo address the unbalanced resource load of a virtual machine cluster, the author proposes an energy‐saving virtual machine scheduling algorithm based on resource management cloud computing technology. This article analyzes the current cloud computing and virtual machine scheduling research in the cloud computing environment. It discusses the concept, characteristics, classification, application scenarios, and key cloud computing technologies. A genetic algorithm is used to solve the problem of high energy consumption in the data center. The test results show that in the same original configuration scheme, the migration times based on the greedy algorithm adopted by GA2ND are about 1000, and the migration times of GA1ST are between 200 and 500. The GA2ND migration scheme requires fewer virtual machines. In the result analysis, the experiments compare the proposed algorithms—DVFS, IMC, GA1ST, and GA2ND—with a focus on energy consumption and virtual machine migration. Notably, DVFS serves as a reference for energy efficiency, IMC represents the proposed algorithm without genetic optimization, GA1ST denotes the genetic algorithm under a heterogeneous model, and GA2ND signifies the enhanced genetic algorithm introduced in this article. The comparison aims to assess the energy efficiency and virtual machine migration performance of each algorithm in the context of a simulated cloud computing environment. Therefore, the algorithm proposed in this article can effectively reduce energy consumption and avoid frequent migration of virtual machines.