Unnecessary Energy Consumption Research Articles

(Organic and Biological Electrochemistry Division Manuel M. Baizer Award, Digital Presentation) Electron-Transfer-Triggered Smart Reactions Boost a Better Anthropocene

It is important that chemical reactions initiated by electron transfer associated with electrodes can be achieved by precise control of intramolecular and intermolecular electron transfer of organic compounds, which is difficult to achieve by general chemical methods. Expectations for a new production process that positively controls the electron transfer is also expanding dramatically. Here, the solvent that dissolves the substrate for reaction, the network structure of the ionic species in the solution, and the role of coexisting substances are exactly grasped, and the total energy consumption is controlled under milder conditions. If it is possible to develop a chemical process that reduces the consumption of substances such as reaction reagents to the utmost while significantly suppressing the above, it is expected that structural conversion of chemical substances based on the electron transfer process can be realized. This means that advanced control of the formation, stabilization, and subsequent reactions of active open-shell molecules opens the door to new chemical reactions that reduce excess reagent and energy consumption. There is a possibility that the atom economy will be dramatically enhanced by the electron transfer reaction process for the reagents that need to be charged. This achieves a chemical reaction that is regarded as "Electrons as Reagents", and it will be a core technology that will bring about innovation in the chemical substance manufacturing method that is currently produced with the production of a large amounts of unnecessary substances and energy consumption. Research achievements: In such a background, K. Chiba pioneered original organic electrolytic reactions based on the research activities on chemical synthesis of biologically active natural compounds, and achieved various carbon skeleton formation, chemical synthesis of useful substances and biologically active natural substances. One of the notable results is the electrolytic synthesis reaction method using a unique electrolyte solution composed of lithium perchlorate / nitromethane. It was shown that the cation species generated by electrode electron transfer using this electrolyte solution are stabilized and can be applied to the formation of a wide variety of intermolecular carbon-carbon bonds. That is, numerous electrode process-triggered reactions such as varied (hetero)Diels-Alder reactions, [3+2], and [2+2] cycloaddition reactions have been achieved. In addition, the olefin metathesis without transition metal catalysis was successful for the first time. The importance of the role of the electrolyte solution in the organic electrolysis reaction was widely shown. The elucidation of these reaction mechanisms and new findings on the action of the reaction field have contributed to the rapid development of the field in recent years by utilizing and enhancing the functions of various electrolyte solutions.In addition, the application of the new organic electrolysis method has widely developed chemical synthesis methods for natural and non-natural peptides, artificial nucleic acids, etc. It is noteworthy that it was successful in electrolytic synthesis of various aza-nucleosides by utilizing the cation species under the stabilizing action of the lithium perchlorate / nitromethane electrolyte solution. This basic skeleton paves the way for mass synthesis of nucleic acid derivatives, which are promising as therapeutic drug for covid-19.Furthermore, K. Chiba achieved organic electrolytic reactions that mimic the electron transfer process in the living body. Inspired by the bio-mimic chemical reaction system, he proposed a chemical process based on biphasic solutions. The key technology is the introduction of a solution system that combines a highly polar electrolyte solution and a hydrophobic organic solvent. By controlling the temperature, phase-fusion and phase-separation can be repeated, so that the product, for example, can be taken out from the reaction system while reusing the electrolyte solution. By forming reverse micelles in a hydrophobic solvent, a continuous reaction system can be constructed, which has led to the proposal of an important method for industrial application of electrolytic reactions and of multi-step medium-size molecule syntheses reducing environmental load. Outlook for the future: We must achieve a more efficient cycle of material, food and energy to support the world's population of 9 billion in the near future. For that purpose, it is extremely important to pay attention to and apply the functions of electrons involved in the production and decomposition of all chemical substances, including the elucidation and utilization of biological functions. The key technology of controlling electron transfer is indispensable for achieving a smart green society boosting a better Anthropocene.

MAC Protocol Analysis for Wireless Sensor Networks

In wireless sensor networks, MAC protocol is used to achieve efficient, fair and balanced allocation of wireless channel resources and access control of nodes in the network, and to control the communication process of nodes in the network. The energy consumption of wireless sensor nodes is mainly concentrated in the communication unit, including: data transceiver, idle listening, protocol control overhead, etc. A good MAC protocol is beneficial to reduce unnecessary energy consumption and enhance node life time. At the same time, MAC protocol, as the bottom protocol of wireless sensor network, provides a stable and reliable communication foundation for the realization of the upper protocol. Therefore, a large number of scholars at home and abroad have carried out extensive and in-depth research on MAC layer protocol of network. In this paper, the research process and progress of MAC layer protocol in wireless sensor networks are analyzed, and the research progress of MAC layer protocol in multi-radio frequency and multi-channel wireless sensor networks is analyzed.

The air distribution and flow resistance of tuyeres with coupling effect

The tuyere plays a critical role in creating a comfortable the indoor environment and is significant for the obtaining hydraulic balance in ventilation and air condition (VAC) systems. Currently, the internal flow state of the tuyeres in VAC duct engineering design is idealized for full development. Ignoring the coupling effect causes the problem of ineffective air distribution and places an enormous load on the entire system. Therefore, the deviation in the air distribution and the change in the flow resistance of circular diffusers and spherical nozzles under the coupling effect are studied through full-scale experiments and numerical simulations. In this research, the deviation law under coupling effect conditions is presented, and the relative deviation formula of the circular diffuser under the coupling effect is fitted. Considering the total pressure loss, relative deviation, and target value, a table that shows connection approaches in engineering applicable to circular diffusers is proposed. The reduced structure of the spherical nozzle overcomes the disturbance caused by the coupling effect but increases the resistance by approximately 50.8%. Consequently, the research results from the study of tuyeres under the coupling effect are expected to lead to a reduction in unnecessary energy consumption and supplement the traditional design theory of the VAC duct system.

Computer Simulations of Injection Process of Elements Used in Electromechanical Devices.

This paper presents the computer simulations of the injection process of elements used in electromechanical devices and an analysis of the impact of the injection molding process parameters on the quality of moldings. The study of the process was performed in Autodesk Simulation Moldflow Insight 2021. The setting of the injection process of the detail must be based on the material and process technological card data and knowledge of the injection molding machine work. The supervision of production quality in the case of injection moldings is based on the information and requirements received from the customer. The main goal of the analysis is to answer the question: how to properly set up the process of filling the mold cavities in order to meet the quality requirements of the presented molding. In this paper, the simulation was compared with the real process. It is extremely important to optimize the injection, including synchronizing all process parameters. Incorrectly selected values of the parameters may lead to product defects, leading to losses and destruction of raw materials, and unnecessary energy consumption connected with the process.

Energy-saving service management technology of internet of things using edge computing and deep learning

The purpose is to solve the problems of high transmission rate and low delay in the deployment of mobile edge computing network, ensure the security and effectiveness of the Internet of things (IoT), and save resources. Dynamic power management is adopted to control the working state transition of Edge Data Center (EDC) servers. A load prediction model based on long-short term memory (LSTM) is creatively proposed. The innovation of the model is to shut down the server in idle state or low utilization in EDC, consider user mobility and EDC location information, learn the global optimal dynamic timeout threshold strategy and N-policy through trial and error reinforcement learning method, reasonably control the working state switching of the server, and realize load prediction and analysis. The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30–45, the batch size is 128, the training time is 788 s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.

Interrupted plate porous media design for ionic liquid-type liquid piston hydrogen compressor and analysis of the effect on compression efficiency

Hydrogen is regarded as an important energy source in the future. A hydrogen compressor is the key equipment in hydrogen-fueled vehicles. The high compression speed leads to the low efficiency for its adiabatic conditions, and the heat transfer condition of the compressor is an important solution. In this study, the porous media installed in the compression cylinder with ionic liquid is proposed to improve the heat transfer conditions and compression efficiency. A mathematical model of heat transfer and fluid resistance in the compression process was constructed. Multiple computational fluid dynamics simulations were applied to summarize the relationship among the Nusselt number, fluid flow resistance, and Reynolds number. Meanwhile, the main influencing factors affecting the convective heat transfer strength of porous media were obtained by analyzing the flow field conditions of fluid basin units in porous media inserts. Then, the relevant mechanism of enhanced heat transfer was proposed. Results show that: all different fluids conform to the quadratic relationship despite the order of magnitude differences in flow resistance. In addition, the Nusselt number is related to the Reynolds number as a power exponential function, and the value of Nusselt number increases as Reynolds number increases. The main heat dissipation surface of porous media is the side of the interrupted plate. Furthermore, the thinner the velocity boundary layer near the wall, the stronger the heat transfer capacity. For a specific compressor at 120 rpm, the porous media designed in this study can reduce the temperature at the end of compression by 14.33 K, saving 11.76% of the unnecessary energy consumption caused by heat dissipation problems.

Parametric study on the effect of radiant heating system on indoor thermal comfort with/without external thermal disturbance

For radiant heating system, less concern has been given to the fact that supply water temperatures are determined according to local conditions, and it is unclear how surface emissivity of the system affects indoor thermal comfort. Therefore, the application of radiant heating system with fixed water supply temperature in two different types of buildings is analyzed. The transient simulation is verified experimentally and the results show that when the radiator area is determined, adjusting water supply temperature has a great effect on thermal comfort. Changing the basic water temperature by 5 °C can reduce the thermal comfort by 20%. Therefore, it is more reasonable to determine the water supply temperature first and then design other parameters, which can improve thermal comfort and reduce unnecessary energy consumption. The radiator surface emissivity changes thermal comfort mainly by affecting the response speed. The higher the emissivity, the better the thermal comfort. However, if only considering the acceptable thermal comfort zone, changing the emissivity will have little effect on thermal comfort. The time proportion of acceptable thermal comfort zone is only reduced by 5% when emissivity changes from 0.95 to 0.5. Therefore, users can choose radiator surface materials more flexibly according to their needs.

Dot6/Tod6 degradation fine-tunes the repression of ribosome biogenesis under nutrient-limited conditions

SummaryRibosome biogenesis (Ribi) is a complex and energy-consuming process, and should therefore be repressed under nutrient-limited conditions to minimize unnecessary cellular energy consumption. In yeast, the transcriptional repressors Dot6 and Tod6 are phosphorylated and inactivated by the TORC1 pathway under nutrient-rich conditions, but are activated and repress ∼200 Ribi genes under nutrient-limited conditions. However, we show that in the presence of rapamycin or under nitrogen starvation conditions, Dot6 and Tod6 were readily degraded by the proteasome in a SCFGrr1 and Tom1 ubiquitin ligase-dependent manner, respectively. Moreover, promiscuous accumulation of Dot6 and Tod6 excessively repressed Ribi gene expression as well as translation activity and caused a growth defect in the presence of rapamycin. Thus, we propose that degradation of Dot6 and Tod6 is a novel mechanism to ensure an appropriate level of Ribi gene expression and thereby fine-tune the repression of Ribi and translation activity for cell survival under nutrient-limited conditions.

AI 기반 외부환경 대응형 광융합 리빙케어 스마트홈 시스템 개발에 대한 연구

There are many problems such as unnecessary electric energy consumption of smart home lighting systems used and commercially available, and biometric rhythm disorders of users due to malfunctions. To improve this, we would like to research and development a plan implement an AI-based external environment-responsive optical fusion living care smart home system that analyzes and predicts users

Geographically extended occupant clothing behavior model using convolutional neural networks with hyperband technique

Occupant basic clothing insulation is essential in the computation of occupant thermal comfort indices and heating ventilation and air conditioning (HVAC) sizing processes. Consequently, inaccurate estimations of occupant clothing behavior result in poorly designed indoor environments and could indirectly result in unnecessary energy consumption and/or thermal comfort complaints. As a solution, several studies have attempted to develop data-driven predictive models for occupant basic clothing insulation but face two challenges; (I) lack of sufficiently large datasets that are universal and (II) use of simple analytical methods that could potentially misrepresent occupant clothing behavior given its complexity. We develop a convolutional neural network (CNN) based on a large universal dataset, that predicts occupant basic clothing insulation. The developed CNN model accurately predicts occupant basic clothing insulation (R2 = 0.94) and outperforms conventional deep learning architectures. The application of the developed models could substantially reduce clothing-related errors in occupant thermal comfort estimation and HVAC sizing processes.

Determination of loading capacity index of a continuous miner in soft rock roadway and virtual test method

To improve the loading efficiency and reduce energy consumption of a continuous miner in soft rock roadway, a seven-arm star wheel designed with Gaussian fitting method was proposed, and a coal loading model of the continuous miner star wheel loading mechanism was reconstructed with EDEM software. The loading capacity of the seven-arm star wheel and the three-arm star wheel of the EML340 continuous miner at different working speeds were studied respectively. The scientific and reasonable identification index was formulated and the index evaluation system of loading star wheel was established. It has been found that the performance of the loading star wheel is a collection of various identification indicators, the coal returning mass reducing the loading efficiency and increasing unnecessary energy consumption, therefore, it is difficult to identify by a single index. Loading coal and rock by the star wheel is a process that consumes energy and pays attention to output, therefore, the identification index should include two kinds of efficiency parameters and energy parameters. Rake coal torque and loading specific energy consumption have reflected the degree of energy utilization, which can be comprehensively used for preliminary design of the star wheel. The performance parameters such as loading power and loading efficiency are reliable indicators for designing and performance evaluation of the star wheel. Based on the statistical analysis of the test data, compared with the three-arm star wheel of the EML340 continuous miner, the loading efficiency of the seven-arm star wheel has been significantly improved. The loading power for coal loading has been reduced by 46%. The feasibility of the Gaussian design method of loading star wheel has been verified.

Aggregated PSO for Secure Data Transmission in WSN Using Fog Server

Privacy of data in Internet of Things (IoT) over fog networks is the biggest challenge in security of Wireless communication networks. In Wireless Sensor Network (WSN), current research on fog computing with IoT is gaining popularity among IoT devices over network. Moreover, the data aggregation will reduce the energy consumption in WSN. Due to the open and hostile nature of WSN, secure data aggregation is the major issue. The existing data aggregation methods in IoT and its associated approaches are lack of limited aggregation functions, heavyweight, issues related to the performance overhead. Besides, the overload on fog node will result in high latency, scalability, storage, degraded reliability and energy overhead. In order to overcome these issues, this proposed work has used two schemes for secure transmission of data over the network and reduce the energy consumption of the transmission. The secret data transferred between the IoT devices and the Fog server are transmitted through the aggregator node. If the aggregator node is placed far away from the Fog node, it may send the data to its neighbor aggregator. And it will append it with the current data and send it to the fog server through aggregator message receiving method. In addition to that, the fog server can extract the data through the fog message extractor method. In order to reduce the transmission cost and energy, Clustered Particle Swarm Optimization (CPSO) method is used to form the clusters. This proposed work can avoid the unnecessary energy consumption during the transmission and ensures secured aggregation so that the base station can know the origin of the sender and the validity of the received message. Therefore, the computation cost of the proposed work in authorization requires1MC+1H and the aggregation requires (n+2) MC+1H which is lesser than the existing methods.

DaaS: Towards Energy-Efficient Data Collection Optimization for Data as a Service in IoT networks

The Internet of Things (IoT) networks have been adopted ubiquitously to support domain applications. Specially, social robot devices have become an important part of IoT networks as smart devices, where gathering sensory Data functionality can be encapsulated as a IoT Services (denote DaaS), as a foundation for supporting widely domain applications. In this setting, gathering sensory data from service-aware social robots in an energy-efficient manner is of importance for prolonging the network lifetime and promoting proper decision-making. Considering the large-scale and spatial-temporal evolutional characteristic of IoT networks, social robots roam over time to deal with tasks, especially considering the fact that it may hardly be predicted for the regions and time durations that certain anomalies may occur. Therefore, this paper proposes to adopt mobile edge computing to support sensory data gathering. Edge nodes in edge networks gather sensory data from their subordinating social robots in a periodic manner. We design an edge network division method by constructing an improved Sort-Tile-Recursive (STR) tree, which can cluster the edge nodes and decrease unnecessary energy consumption. Experimental results show that our technique is more efficient than traditional ones in decreasing energy consumption.

IoT Based Street Lighting and Computer Aided Control

Traditional lighting systems have many disadvantages such as energy efficiency, failure tracking and lack of remote control. These disadvantages cause unnecessary energy consumption, extra labor, security threats and lack of follow-up. To overcome these problems, in this study, instead of traditional lighting, an IoT-based smart lighting prototype was developed and computer-assisted control was performed. In order to ensure energy efficiency, the light intensity of the street lamps was adjusted according to the intensity by taking data from the motion sensors. Data such as fault tracking and density obtained from LED armature sensors were transferred to the cloud and controlled independently of the venue. Thus, the data on the cloud can be easily tracked through the ThingSpeak platform. With the visualized ThingSpeak web interface, the electrical power, current drawn, temperature value, ambient light value, location information, motion detection and remote switching of the LED armature can be done independently of the location.

A review of the structure and application of active chilled beams (ACBs) for central air conditioning systems

Active chilled beams (ACBs), as indoor air terminal units in heating, ventilation, and air conditioning (HVAC) systems, have been of great concern in recent years due to the requirements of energy savings and indoor thermal comfort. This paper reviews the structure and application of ACB, which is helpful in identifying current research scenarios and prospects. The results indicate that the structure of ACBs will be improved based on the entrainment ratio (ER), cooling performance and acoustic behavior as the manufacturing level evolves. The mathematical model of ACBs is constantly being optimized, although there is still room for simplification to fit practical applications. Research on airflow patterns and thermal comfort can be extended to larger spaces (including buildings with large sensible heat ratios) and various climates. Based on the optimization of the ACB mathematical model, modern control theory can be focused on the research on ACB control system. ACB system has a coupling trend with other systems to achieve the comprehensive utilization of energy due to the characteristics of high-temperature cooling. In addition, numerous technical issues remain to be resolved to reduce unnecessary energy consumption. These conclusions also provide a simple reference for research and design in the area where this technology is still in its infancy.

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

Verifying of the feasibility and energy efficiency of the largest certified passive house office building in China: A three-year performance monitoring study

The high energy efficiency of passive houses (PH) has been widely known and proved over the past thirty years. In its operation phase, it is normally to investigate its energy consumption through the monitoring of building systems, in which the HVAC system is much more complicated than expected. In particular in a complex HVAC concept, it can easily lead to system failures or an increase in large and unnecessary energy consumption, if the HVAC system is not properly commissioned before/after being put into operation. The building systems could be commissioned and optimized based on long-term monitoring results. In the research framework of a PH design and commissioning mode, this article verifies the feasibility of the largest certified PH office building in China, taking into consideration the local climate conditions, the actual energy consumption, and the corresponding indoor environmental comfort, etc. Compared with similar local conventional office buildings, the electricity consumption of this building can be greatly reduced, while indoor environment is still within the comfort range. Its annual energy efficiency can be improved by approximately 69% higher than current Chinese public building standards. The comparison of the monitoring data with the predicted results, also confirms its energy efficiency of this PH building and shows that the HVAC systems commissioning and optimization can bring further energy saving improvement. This case study can provide a good experience for subsequent PH public buildings and a basis for policy makers in China.

Fuzzy Logic Based Smart Home Automation and Forecasting Electric Energy Consumption

Monitor and control home electrical network in smarter way creates the base of Smart Grid technology while presenting solutions for energy crisis. The main purpose of this study is to implement a low-cost smart controller system for Smart Homes based on fuzzy logic functions, reducing unnecessary energy consumption, estimating the consumption by using time series applications. Forecasting and controlling the consumption in customer side makes the creation of load profiles easier. The created load profiles increase the efficiency while managing MV network with smart grid applications. In the study, STM32 microcontroller is the brain of the system. On the other hand, as software part Processing and Matlab is used.

LECAR: Location Estimation-Based Congestion-Aware Routing Protocol for Sparsely Deployed Energy-Efficient UAVs.

Energy-efficient routing has become a critical issue for advanced energy-hungry unmanned aerial vehicles (UAVs). Routing in a flying ad hoc network is always challenging and becomes even more critical when a small number of UAVs must cover a large area. The routing protocols based on the delay-tolerant network (DTN) are best suited for such scenarios. However, traditional DTN-based routing protocols depend on data dissemination to offer a better packet delivery ratio, leading to congestion and excess transmissions, causing heavy and unnecessary energy consumption. We propose a location estimation-based congestion-aware routing protocol (LECAR) to balance these two issues. Considering outdated location information, LECAR takes advantage of the mobility model to estimate the current location of the destination. In addition, LECAR routes a packet by considering both the distance to destination and buffer occupancy of the neighboring UAVs. Simulation results show that LECAR could ensure both a high packet delivery ratio and low energy consumption. Moreover, LECAR could provide a minimal number of transmissions, while minimizing the number of copies per packet at a time.

EEHCHR: Energy Efficient Hybrid Clustering and Hierarchical Routing for Wireless Sensor Networks

In cluster-based Wireless Sensor Network (WSN), the Cluster Heads (CHs) consume massive amount of energy due to uneven processing and routing of the information of their Cluster Members (CMs) to the Base Station (BS), and results in lower network lifetime e.g., REHR (Residual Energy based Hybrid Routing). Also, the centralized clustering algorithms introduce unnecessary consumption of energy e.g., FFTHR (Fitness Function based Two-Hop Routing) and UCRA-GSO (Uneven Clustering Routing Algorithm using Glowworm Swarm Optimization). Thus, for extending the network lifetime, we introduce a new clustering algorithm named as Energy Efficient Hybrid Clustering and Hierarchical Routing (EEHCHR) in WSN. Here, a new scheme of adaptive and hybrid clustering has been proposed for minimum usage of the node’s energy using the Euclidean distance parameter, Fuzzy C-Means (FCM) technique, location of BS, and residual energy of the nodes. Here, the clustering is performed only in a few rounds, and this results in reduction of energy consumption of the network. All the CHs are selected using the energy efficient fitness function, which works in an adaptive way with the residual energy of the nodes to improve the CH selection process. For efficient usage of the network’s energy, we have also given a hierarchical packet routing strategy by introducing the concept of DCH (Direct Cluster Head) and CCH (Central Cluster Head), which are selected by different fitness functions, and work as a relay for few other CHs. The simulation results of EEHCHR proved that it extends the network lifetime, coverage, and saves the network energy as compared to similar existing algorithms e.g., FCM, REHR, FFTHR, UCRA-GSO and CCA-GWO.