Problem Of Energy Consumption Research Articles

Mechanism analysis and performance evaluation of clean and efficient recovery of N-heptanol/N-hexanol from wastewater via extractive distillation with green mixed entrainer

A clean and sustainable process for separating n-heptanol/n-hexanol/water industrial mixing system by extractive distillation via mixing organic solvent with ionic liquid as an entrainer was proposed. The mechanism of intermolecular microscopic interaction was explored by quantum chemistry calculation. By using multi-objective optimization, the optimal operating parameters of the process were obtained. In addition, process intensification technology was introduced, which improved the problems of high cost and high energy consumption in the conventional extractive distillation process. Finally, multiple performances were evaluated and analyzed for different processes. It was found that the coupled heat integration and heat pump technology to assist the extractive distillation process saved 10.45% in terms of economy, reduced gas emissions by 49.00%, and reduced energy consumption by 48.99% compared to the basic extractive distillation process. This confirmed the reliability and feasibility of implementing the process intensification technology. This work demonstrates the application of organic solvent mixed with ionic liquid as entrainer and process intensification technology in the extractive distillation process, providing guidance and reference for the design of the extractive distillation process.

Reduce energy consumption for organic amine regeneration by MnOOH/HZSM-5 catalysts

Catalytic regeneration has become one of the most promising technologies to solve the high energy consumption problem of CO2 capture in organic amine solutions. As the most mainstream type of catalyst, the instability of acidic catalysts in alkaline solutions attracts increasing attention from researchers. Herein, we proposed a composite catalyst of MnOOH loaded HZSM-5 to weaken the adsorption of alkaline substances and improve the catalytic stability of the catalytic material. The optimized catalytic effect is obtained when the mass ratio of MnOOH is 20% The CO2 desorption efficiency is increased by 68.2%, and the energy consumption is reduced by 31.8% when the dosage of the catalyst is only dosage of 0.2 wt% amine solution. Through materials characterization and DFT calculations, it was found that the surface O and hydroxyl groups on MnOOH are the key factors for proton cycling, and HZSM-5 has an important impact on improving proton transfer rate. This work provides a valuable approach to solving the stability problem of acidic catalysts.

Evaluation of warm mix effect in laboratory using a self-developed WMA additive Alube

In response to the problems of high mixing temperatures and energy consumption of polymer-modified asphalt mixtures in road construction, a surfactant-based warm mix additive ‘Alube’ was developed. The warm mix SBS-modified asphalt and its mixture were prepared. Their rheological performance and chemical structure were analysed by the rheological and microscopic tests. The active mixing power (P) of asphalt mixtures were examined by the variable speed mixing (VSM) test. The mixing flowability model was then established to evaluate the warm mix effect. The results indicated that the mixing flowability of asphalt mixtures obeyed the linear Bingham visco-plastic model, and the slope and intercept of the flow straight characterised the mixing viscosity and plastic limit. It is evaluated that 0.7% Alube1 and Evotherm reduced the mixing temperature of SBS-modified asphalt mixture by 14°C and 12°C, respectively. Moreover, Alube1 is suitable for a wide range of applications through warm mix asphalt technology.

Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG’s transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted.

A polyhedral analysis of the synchronous management of energy production and consumption problem

A polyhedral analysis of the synchronous management of energy production and consumption problem

Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

AbstractIn this paper, we exploit the spatial and transmission diversities in cooperative non-orthogonal multiple access (C-NOMA) networks to improve the system sum-rate. To achieve this, we propose a user-pairing scheme where near-field user pairs serve as relays for user pairs in the far-field region. Based on this pairing scheme, we incorporate a space-time block code transmission technique at the near-field user pairs to maximize the transmission diversity in the cooperative phase. Moreover, we consider a non-linear energy harvesting model at the near-field user pair to alleviate the problem of energy consumption during the cooperative transmission phase. Further to this, we formulate a sum-rate maximization problem that is addressed from the viewpoint of joint power allocation factor and power splitting ratio optimization. We develop a low-computational iterative algorithm based on the concepts of the Stackelberg game and the Nash bargaining solution. We benchmark our findings with different scenarios, such as energy harvesting C-NOMA with a fixed power allocation factor and power splitting ratio, energy harvesting C-NOMA without STBC, non-cooperative NOMA, and orthogonal multiple access. The results obtained via simulations outperform the benchmark schemes.

Toward intelligent food drying: Integrating artificial intelligence into drying systems

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies.

Coordinated Control of Proton Exchange Membrane Electrolyzers and Alkaline Electrolyzers for a Wind-to-Hydrogen Islanded Microgrid

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme.

A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

The design of the insulation layer and energy consumption optimization of the injection mould for the motor commutator

The injection molding of the motor commutator requires a long-term high-temperature operation, which poses a problem of high energy consumption. The heating zone is located behind the mold, and there is thermal radiation and convection during heating, resulting in uneven temperature distribution. To solve the above problems, an automatic lifting and insulation mechanism is proposed. An insulation frame is built with an aerogel insulation layer inside. It is connected up to the upper table of the machine, moving up and down during processing, and connected down to the crank slider mechanism. Simulation software analyzes its insulation and temperature distribution and, through experimental verification, compares the temperature distribution and energy consumption of injection molds before and after the installation of insulation mechanisms. The experimental and simulation results show that using the automatic lifting insulation layer design scheme can effectively reduce energy consumption, improve temperature distribution unevenness, and increase the yield rate.

An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering

Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92j), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Study on Pyrolysis Characteristics of Phosphate Tailings under H2O Atmosphere.

The pyrolysis separation of calcium and magnesium from phosphate tailings is an important process due to its high-value resource utilization. In this paper, aiming to address the problems of high energy consumption, a slow decomposition rate and the low activity of decomposition products in the high-temperature pyrolysis of phosphate tailings, the medium-temperature pyrolysis of phosphate tailings under a H2O atmosphere was carried out, and the phase reconstruction and activation of pyrolysis process were discussed. The results showed that compared with N2, air and CO2 atmospheres, the pyrolysis process of phosphate tailings in a H2O atmosphere was changed from two stages to one stage, the starting decomposition temperature was reduced to 500 °C and the decomposition time was shortened to 30 min. The order of the influence of each factor on the pyrolysis of phosphate tailings was temperature > H2O pressure > holding time. Under the optimized pyrolysis conditions, the yield of CaMg(CO3)2 decomposition of phosphate tailings into MgO and CaO was 97.3% and 98.1%, respectively, and the reactivity of MgO was 31.6%. The distribution of Ca and Mg elements in the phosphate tailings after pyrolysis showed a negative correlation, and both of them no longer formed associated compounds; Ca mainly existed in the form of Ca(OH)2, Ca5(PO4)3F, CaSiO3 and CaF2, and Mg mainly existed in the form of MgO, MgF2 and Mg(OH)2.

Research on Optimization of University Classroom Lighting Environment Based on Computer Simulation

In order to improve the comfort of classroom users and improve the lighting environment of university classrooms, this paper puts forward different optimization schemes based on computer simulation. Dialux, a lighting environment design software, is used to numerically simulate the lighting environment of university classrooms. The classroom lighting scheme is optimized and different solutions are proposed. Through computer simulation, the effect of lighting is compared and analyzed, in order to find the most beneficial scheme for students’ mental health, which provides the basis for intelligent control of classroom lighting. The results show that the average illuminance decreases with the increase of suspension height, and the illuminance uniformity increases first and then decreases with the increase of suspension height. The solution of adjusting the projection direction and surface reflectivity of lamps is not suitable, and upgrading the type of lamps can solve the lighting environment problems and control glare. Increasing the number of lamps and adjusting the arrangement of lamps can improve the lighting environment, increase the average value but reduce the uniformity, but lead to a significant increase in energy consumption. Intelligent control based on computer simulation can solve the coordination problem of energy consumption and lighting effect. Through comparative analysis of the effects of different schemes on the classroom lighting environment in colleges and universities based on computer simulation, this paper provides data support for the selection of improved schemes, which is conducive to the selection of more suitable intelligent lighting control schemes, to ensure students’ mental health and work efficiency, and has certain reference significance for creating a healthy and comfortable classroom lighting environment.

Optimization analysis of distributed energy consumption based on dynamic data synchronization and intelligent control

SummaryWith the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real‐time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real‐time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in‐depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller‐led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non‐processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real‐time data in the process of parallel processing, as well as the evaluation and detection of real‐time data replacement.

Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing

INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed.
 OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems.
 METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed.
 RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data.
 CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.

Performance Analysis of Application of BIM Technology and Two-Way Progressive Optimization Algorithm in Energy-Saving Design of Residential Buildings Based on BIM Technology

The current residential energy-saving design is lagging behind, and lack of systematic design methods and scientific technical means seriously restricts process of improving quality of residential buildings in China. With development and application of digital information technology in construction field, BIM technology has brought new opportunities construction industry, and building performance data has become information basis for scientific promotion of energy conservation. This paper uses BIM technology as a platform to establish an information base of common building materials and components in Chongqing through literature research, field studies, summarization, and application practice, and to help architects make wise decisions at early design stage by using "one-click" scheme comparison and design evaluation to avoid a large number of potential energy consumption problems at a later stage, It greatly improves energy efficiency and design quality of buildings.

Numerical Simulation of the Effect of Feeding Mode on the Reduction of Ore Powder in Hydrogen‐Based Flash Ironmaking Process

The efficiency and heat utilization of modern large‐scale blast furnace in China are close to limits. The potential for energy saving and consumption reduction by continuing to improve blast furnace operation is limited, so there is a need to develop nonblast furnace ironmaking technologies to solve the problems of high energy consumption and pollution. The emerging flash ironmaking technology, which is highly efficient and has low pollutant emissions, has become an option. This article uses CFD software to simulate the 3D steady‐state hydrogen‐based flash ironmaking process at a pilot scale. It is a complex system involving gas‐particle two‐phase flow, heat transfer, and chemical reactions. The article mainly studies the effects of the feeding mode and hydrogen concentration in the reducing gas on the reduction of ore particles. The results show that the residence time of the particles in the furnace is very sensitive to the feeding method, and the maximum residence time of the particles reaches about 2.8 s under the optimal feeding method. In addition, increasing the hydrogen concentration in the reducing gas decreases the particles’ residence time. However, the minimum residence time is more than 2.2 s, and the ore powder can still obtain a high reduction degree.

Research Progress of Preparing Titanium Alloy By Molten Salt Method

Titanium alloy has excellent characteristics that cannot be compared with other metals such as light specific gravity, high specific strength, wide applicable temperature range, corrosion resistance, and hydrogen storage. The traditional titanium alloy smelting process is mainly based on titanium sponge as raw material, which has a long process, high energy consumption, heavy pollution, and other problems, which seriously restricts the sustainable development of titanium alloy in the world. The common methods of preparing titanium alloy are alloy melting, metal thermal reduction, powder metallurgy, and the gradually emerging molten salt electrolysis. Among them, molten salt electrolysis is recognized as the most promising method to replace the traditional titanium alloy production process, and certain achievements have been made in recent years in terms of both process and basic theory research. In this paper, the common processes of titanium alloy preparation by molten salt electrolysis at home and abroad, such as molten salt electrolytic co-deposition method, cathodic alloying method and liquid cathode method, new FFC method, and its derived SOM method, etc., will be summarized systematically, and the future research will be put forward.

Drag reduction of locomotive and practical scene

As an important tool for peoples long-distance travel and transportation, the design accuracy of the high-speed railway must be strictly controlled. In addition, the train design needs to conform to aerodynamics, such as improving the linearity of the train flow to ensure that the gas flows smoothly through every part of the train, or reducing the front pressure to reduce the wind resistance, so as to reduce the problems of train instability, high noise and high energy consumption. This paper will analyse the design concept, flow linearity and how to reduce the resistance of locomotives under high-speed motion of Shinkansen trains in Japan. Because the Shinkansen trains developed in the early and later generations are mainly flat-headed, the study of Shinkansen can understand the significance of flat-headed trains. The practicability and manoeuvrability of ' flat head and 'bullet head' locomotives and the application of locomotives in real life are analysed in detail. Finally, it is concluded that the bullet train has a large turning angle and needs to open up a special road to run smoothly, but the bullet train is faster than the flat head. The flat-head train is highly practical and suitable for driving in the city, and another advantage of the flat-head train is that it can still maintain high stability and safety when carrying many passengers.