Optimization Of Energy Utilization Research Articles

The C. elegans Myc-family of transcription factors coordinate a dynamic adaptive response to dietary restriction.

Dietary restriction (DR), the process of decreasing overall food consumption over an extended period of time, has been shown to increase longevity across evolutionarily diverse species and delay the onset of age-associated diseases in humans. In Caenorhabditis elegans, the Myc-family transcription factors (TFs) MXL-2 (Mlx) and MML-1 (MondoA/ChREBP), which function as obligate heterodimers, and PHA-4 (orthologous to FOXA) are both necessary for the full physiological benefits of DR. However, the adaptive transcriptional response to DR and the role of MML-1::MXL-2 and PHA-4 remains elusive. We identified the transcriptional signature of C. elegans DR, using the eat-2 genetic model, and demonstrate broad changes in metabolic gene expression in eat-2 DR animals, which requires both mxl-2 and pha-4. While the requirement for these factors in DR gene expression overlaps, we found many of the DR genes exhibit an opposing change in relative gene expression in eat-2;mxl-2 animals compared to wild-type, which was not observed in eat-2 animals with pha-4 loss. Surprisingly, we discovered more than 2000 genes synthetically dysregulated in eat-2;mxl-2, out of which the promoters of down-regulated genes were substantially enriched for PQM-1 and ELT-1/3 GATA TF binding motifs. We further show functional deficiencies of the mxl-2 loss in DR outside of lifespan, as eat-2;mxl-2 animals exhibit substantially smaller brood sizes and lay a proportion of dead eggs, indicating that MML-1::MXL-2 has a role in maintaining the balance between resource allocation to the soma and to reproduction under conditions of chronic food scarcity. While eat-2 animals do not show a significantly different metabolic rate compared to wild-type, we also find that loss of mxl-2 in DR does not affect the rate of oxygen consumption in young animals. The gene expression signature of eat-2 mutant animals is consistent with optimization of energy utilization and resource allocation, rather than induction of canonical gene expression changes associated with acute metabolic stress, such as induction of autophagy after TORC1 inhibition. Consistently, eat-2 animals are not substantially resistant to stress, providing further support to the idea that chronic DR may benefit healthspan and lifespan through efficient use of limited resources rather than broad upregulation of stress responses, and also indicates that MML-1::MXL-2 and PHA-4 may have distinct roles in promotion of benefits in response to different pro-longevity stimuli.

Optimization of Energy Utilization in Aquaculture Ponds by Automation and IoT Monitoring

Optimization of Energy Utilization in Aquaculture Ponds by Automation and IoT Monitoring

CuEMS: Deep reinforcement learning for community control of energy management systems in microgrids

Nowadays, energy management assumes a pivotal role in ensuring the stable operation of microgrids, facilitating enhanced monitoring, analysis, and optimization of energy utilization for end-users. The implementation of an energy management system (EMS) offers invaluable assistance to microgrid users, enabling them to cut electricity costs, ensure reliability, and provide an elevated level of comfort. However, as the scope of microgrid management proliferates, balancing between the comfort of community microgrid users with more devices and their profitability becomes increasingly challenging. To address this issue, this paper proposes a novel community control approach for EMS, called CuEMS, that utilizes deep reinforcement learning (DRL) to manage multiple appliances and energy storage systems (ESSs). The proposed approach optimizes energy profitability for operators while taking into account user comfort. It reformulates energy scheduling as a Markov decision process and learns the optimal scheduling strategy through deep Q-networks. The presented CuEMS approach is evaluated using real world data from Finland, and the results demonstrate that it can optimize the operation of multiple appliances and improve the profitability and user comfort compared to other methods.

Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach

Wireless Sensor Networks (WSNs) are widely studied for their data collection and monitoring capabilities across diverse applications. However, the limited energy resources of sensor nodes present a significant challenge in extending the network's lifespan. To overcome this, we introduce a Deep Learning based Grouping Model Approach (DL-GMA) that optimizes energy usage in WSNs. DL-GMA employs advanced deep learning techniques, particularly Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation, Cluster Head (CH) selection, and CH maintenance. Evaluation using key metrics—Energy Efficiency (88.7 %), Network Stability (90.8 %), Network Scalability (87.1 %), Congestion Level (18.3 %), and Quality of Service (QoS) (93.4 %)—demonstrates the effectiveness of DL-GMA in energy utilization optimization and overall network performance. Incorporating deep learning and intelligent grouping, our approach extends WSN lifespan and improves data transmission efficiency. DL-GMA represents a significant advancement in energy optimization for WSNs, addressing the challenges of limited energy resources and maximizing the network's potential while improving data transmission efficiency.

Optimizing Energy Usage and Smoothing Load Profile via a Home Energy Management Strategy with Vehicle-to-Home and Energy Storage System

This study investigates an energy utilization optimization strategy in a smart home for charging electric vehicles (EVs) with/without a vehicle-to-home (V2H) and/or household energy storage system (HESS) to improve household energy utilization, smooth the load profile, and reduce electricity bills. The proposed strategy detects EV arrival and departure time, establishes the priority order between EV and HESS during charge and discharge, and ensures that the EV battery state of energy at the departure time is sufficient for its travel distance. It also ensures that the EV and HESS are charged when electricity prices are low and discharged in peak hours to reduce net electricity expenditure. The proposed strategy operates in different modes to control the energy amount flowing from the grid to EV and/or HESS and the energy amount drawn from the HESS and/or EV to feed the demand to maintain the load curve level within the average limits of the daily load curve. Four different scenarios are presented to investigate the role of HESS and EV technology in reducing electricity bills and smoothing the load curve in the smart house. The results demonstrate that the proposed strategy effectively reduces electricity costs by 12%, 15%, 14%, and 17% in scenarios A, B, C, and D, respectively, and smooths the load profile. Transferring valley electricity by V2H can reduce the electricity costs better than HESS, whereas HESS is better than EV at flattening the load curve. Transferring valley electricity through both V2H and HESS gives better results in reducing electricity costs and smoothing the load curve than transferring valley electricity by HESS or V2H alone.

A distributed routing-aware power control scheme for underwater wireless sensor networks

During the data routing process in underwater wireless sensor networks, the communication ranges of nodes profoundly affect the network performances, such as energy utilization and delay overhead. Thus, in this paper, we design a routing-aware power control scheme for underwater wireless sensor networks, whose primary purpose is to assign a differentiated optimal communication radius for each sensor node. Compared with the existing studies, this scheme pays more attention to the combination of power control and existing opportunity routing mechanisms, the distributed implementation of algorithms, and the specific characteristics of underwater sensor network topology. The scheme consists of two main algorithms, namely, the rough adjustment algorithm and the fine adjustment algorithm. These two algorithms employ the game theory and the flooding mechanism to achieve the trade-off among network connectivity maintenance, routing performance promotion, and energy utilization optimization during the power control process. Based on the theoretical analysis, with the proposed algorithms, each node can adjust its transmitted power in a distributed, convergent, and low-complexity manner. According to our numerical simulation experiment, the designed power control scheme could improve the diverse networking performances, including the network lifetime, the packet delivery ratio, the energy efficiency, the load balance, and the end-to-end delay.

Investigation on the two-phase loop cooling system composed of maglev compressor and liquid pump for data centers

• The cooling system composed of maglev compressor and liquid pump is proposed. • The two-phase loop cooling system is oil free and structurally reliable. • As pressure ratio varies from 2.64 to 1.13, system COP increases from 3.85 to 8.16. • The outdoor unit COP is 31∼40 and the system COP is above 12 in pump-driven mode. • The annual GCOP is above 6.5 and PUE is 1.2 or less in most climate zones of China. The increasing demand for data processing, storage and distribution is escalating the energy usage of data centers, in which cooling system energy consumption could take up 30%∼50% of the total energy consumption. In this regard, improving cooling system efficiency will contribute to the optimization of energy utilization. In this paper, a two-phase loop cooling system driven by maglev compressor and liquid pump, which is oil free and structurally reliable, is proposed and investigated experimentally. The system performance under three different operating modes is explored experimentally. The application effects of proposed system in typical climate zones of China are analyzed. The results show that the coefficient of performance (COP) of the system is about 12 and the COP of the outdoor unit is as high as 31∼40 in pump-driven mode. In integrated mode and vapor compression mode, the COP of system increases from 3.85 to 8.16 while pressure ratio varies from 2.64 to 1.13, which means that the maglev compressor and the liquid pump can cooperate well to achieve an expected performance. The annual COP of the integrated system applied in China is above 6.5 in most climate zones. The power usage effectiveness of data center can be as low as 1.2, under the assumption that the energy consumption of auxiliary system except the cooling system accounts for 5% of the information technology equipment energy consumption.

Towards a digital twin approach – Experimental analysis and energy optimization of a multi-bed adsorption system

An adsorption chiller system is one of the most promising technologies that utilize waste thermal energy to simultaneously produce cooling and potable water. However, the energy utilization optimization and detection of desiccant’s sorption capacity degradation are two unresolved issues that have severely impeded the development and commercial applications of adsorption chiller technologies. This study is pioneered to develop a digital twin platform specifically designed for an experimental four-bed two-evaporator adsorption chiller system prototype. Leveraging this platform, system monitoring, performance prediction, and optimization functions are achieved. Relying on the monitoring function, the digital twin can detect the capacity degradation of desiccant-coated heat exchangers. By employing the prediction and optimization functions, the application performance of the adsorption chiller system under varying ambient and load conditions can be simulated and optimized under real-time operating conditions. Additionally, this work projects a first-time experimental parametric study analysis for a four-bed two-evaporator adsorption chiller system prototype under a heat recovery scheme that considers fourteen operating parameters. Key results revealed that COPth reaches 0.68 when the cycle time is 2240 s. Case studies also showed that the adsorption chiller system can yield significant energy-saving performance for climatic conditions in Malaysia and Saudi Arabia. The proposed digital twin optimization method demonstrates that COPth is enhanced by 8.5 %, 9.5 %, and 8.5 %, respectively. In contrast to the conventional method, optimizing the adsorption chiller’s performance through the digital twin platform enables a reduction of the annual electricity consumption by up to 10.3 %.

Hierarchically Fractal PtPdCu Sponges and their Directed Mass- and Electron-Transfer Effects.

Fractal Pt-based materials with hierarchical structures and high self-similarity have attracted more and more attention due to their bioinspiring maximum optimization of energy utilization and mass transfer. However, their high-efficiency design of the mass- and electron-transfer still remains to be a great challenge. Herein, fractal PtPdCu hollow sponges (denoted as PtPdCu-HS) facilitating both directed mass- and electron-transfer are presented. Such directed transfer effects greatly promote electrocatalytic activity, regarded as 3.9 times the mass activity, 7.3 times the specific activity, higher poison tolerance, and higher stability than commercial Pt/C for the methanol oxidation reaction (MOR). A new "directed mass- and electron-transfer" concept, characteristics, and mechanism are proposed at the micro/nanoscale to clarify the structural design and functional enhancement of fractal electrocatalyst. This work displays new possibilities for designing novel nanomaterials with high activity and superior stability toward electrocatalysis or other practical applications.

Micro-Energy Grid Energy Utilization Optimization With Electricity and Heat Storage Devices Based On NSGA-II Algorithm

Micro-Energy Grid Energy Utilization Optimization With Electricity and Heat Storage Devices Based On NSGA-II Algorithm

Optimization of Energy Utilization in Cognitive UAV Systems

Wireless communication with unmanned aerial vehicle (UAV) has attracted much attention due to its flexibility and low-cost in providing wireless connectivity. In this article, we investigate the spectrum sharing between UAV and terrestrial wireless communication. Two scenarios are considered in this work. Scenario 1: the UAV is energy sufficient to transmit the required data. In this case, our objective is to minimize the time that is needed to complete data transmission. Scenario 2: the battery of the UAV is energy insufficient. In this case, the goal becomes maximizing the size of data that can be transmitted. Different scenarios require different placement of the UAV. When the UAV flies towards the primary user (PU), the sensing result will be more accurate and more opportunities can be utilized to transmit data. However, the interference to primary receiver (PR) and the propulsion energy due to UAV's movement will be increased. In this article, the sensing performance, the power allocation and the UAV's position are jointly optimized to satisfy different requirements of the UAV system. To efficiently utilize the licensed spectrum, a hybrid transmission mode is proposed for the UAV system, in which sensing and power control are jointly employed to protect the PR. Then, efficient iterative algorithms are proposed to solve the optimization problems. Simulations results show that proposed designs can significantly reduce the time that is required to complete data transmission in energy sufficient scenario and enhance the size of data that can be transferred in energy insufficient scenario.

Strategic integration of vehicle-to-home system with home distributed photovoltaic power generation in Shanghai

The energy utilization optimization strategies in a smart house without and with vehicle to home (V2H) and/or home distributed photovoltaic (HDPV) in Shanghai are investigated in detail for the efficient household energy utilization and the reduction of net electricity expenditure. Such influences as EV travel distances, weather conditions and different PV subsidies are also taken into account. The results show that transferring valley electricity and PV by V2H can not only improve the utilization rate of valley electricity and PV, but also obtain considerable economic benefits. Transferring PV by V2H can get more revenues than transferring valley electricity by V2H. The energy arbitrage of V2H decreases with the increase of the EV travel distance. The HDPV-V2H mode in the case studied can completely cover the electricity demand of the household load in sunny and cloudy days without additional grid electricity while the combination of PV with transferred valley electricity by V2H is enough to support the household load demand in rainy days. The positive return of HDPV still can’t do without the support from government’s subsidy in Shanghai in the coming time. However, the HDPV-V2H mode can improve the benefit of HDPV. Meanwhile, there are a lot of EVs in Shanghai, charging with green power in priority. The HDPV-V2H mode can promote the synergetic development of HDPV and EVs in Shanghai.

Technological development of multi-energy complementary system based on solar PVs and MGT

The complementary micro-energy network system consisting of solar photovoltaic power generation (solar PVs) and micro-gas turbine (MGT), which not only improves the absorption rate and reliability of photovoltaic power, but also has the advantages of low emission, high efficiency, and good fuel adaptability, has become one of the most promising distributed power systems in the field of micro grid. According to the development of current technology and the demand of actual work, this research described the domestic and foreign development of micro-energy network system based on solar PVs and MGT. Moreover, it analyzed the challenges and future development regarding the micro-energy network system in planning and design, energy utilization optimization and dispatching management, and system maintenance, respectively. Furthermore, it predicted the future development of the key technology of the multi-energy complementary system. These results will be beneficial for the progress of this field both in theory and practice.

A time to fast.

Nutrient composition and caloric intake have traditionally been used to devise optimized diets for various phases of life. Adjustment of meal size and frequency have emerged as powerful tools to ameliorate and postpone the onset of disease and delay aging, whereas periods of fasting, with or without reduced energy intake, can have profound health benefits. The underlying physiological processes involve periodic shifts of metabolic fuel sources, promotion of repair mechanisms, and the optimization of energy utilization for cellular and organismal health. Future research endeavors should be directed to the integration of a balanced nutritious diet with controlled meal size and patterns and periods of fasting to develop better strategies to prevent, postpone, and treat the socioeconomical burden of chronic diseases associated with aging.

Green Cloud: An Energy Efficient Load Balancing Approach Using Global Load Optimization

High Performance Green Cloud Computing is an emerging field in the upcoming IT industry. With the main focus on balancing resource utilization in green cloud environment, IT applications need a highly rapid and scalable access services. Cyber Guarder Approach (CGA) based on trust management in green cloud facilitates access control to resources pool but the energy expenditure is of higher rate while balancing the load. One of the important aspects of green cloud being optimization of energy utilization, Energy-aware Allocation Policies (EAP) maximized the revenues. However, EAP policies increased the running server, resulting in higher amount of frequency and imbalance in running server during resource utilization in green cloud. For balancing the load on the Infrastructure as a Service (IaaS), an energy efficient load balancing approach using global load optimization (GC-GLO) method is presented on the green cloud data center in this paper. The energy efficient load balancing approach uses the Global Load Optimization (GLO) method to allocate the resources in a dynamic manner using the load factor in two ways. First, GC-GLO approach uses the logical hierarchy process to allocate the resources in the servers present in green cloud zone. The logical hierarchy process in GC-GLO selects the task in a hierarchical manner and allocates the task between cloud severs’ based on the resource need. With the exact form of resource utilization in GC-GLO, improves the utilization rate in the green cloud environment. Based on the resource utilization, the GC-GLO approach then uses the global load optimization algorithm to measure the load rate of each green cloud server. The energy expenditure on the resource allocation and the IaaS maintenance on the green cloud are eventually measured. The approach, GC-GLO has been validated by conducting careful performance measure study using CloudSim platform in terms of factors such as energy expenditure rate on IaaS maintenance, resource utilization rate and running time. The results demonstrate that global load optimization has extensive potential as it offers significant performance gains to load balancing efficiency and false positive rate under dynamic workload scenarios.

Development of Efficient Designs of Cooking Systems. II. Computational Fluid Dynamics and Optimization

Sections 2–6 of Part I were devoted to the analysis of heat transfer characteristics of cookers. In all the experiments, only water was employed as a working medium. Now, we extend such an analysis to the actual cooking process in order to arrive at an improved cooking device. The major strategies for the optimization of energy utilization is to design appropriate insulation that has been obtained by two cover vessels. In order to select an air gap, the flow and temperature patterns in the air gap have been extensively analyzed using computational fluid dynamics (CFD). The flow pattern and heat transfer in cooking pots have also been analyzed by CFD. This has enabled us to design suitable internals for minimizing the stratification of temperature. The understanding of fluid mechanics has also given basis for selection of heat flux, gap between burner tip and cooker bottom, and temperature of flue gases leaving the cooker. Chemical engineering principles have been used for modeling and optimization. Kinetics have been obtained in batch cookers. The knowledge of kinetics, thermal mixing, axial mixing, and optimum selection of insulation have been employed for the development of continuous cookers. The continuous mode of operation also helps in saving of energy. Systematic data have been collected for the design and scale up of continuous cookers.

Studies on the Optimal Method Adaptable to the Rural Community as the Biomass Energy and Optimization of Energy Utilization (II)

Studies on the Optimal Method Adaptable to the Rural Community as the Biomass Energy and Optimization of Energy Utilization (II)

Seasonal energy utilization optimization in an enterprise

Significant energy savings can be achieved in some manufacturing enterprises through process integration. But in certain cases, the optimization of energy utilization should be combined with many items, such as the rational structures of energy consumption, the influence of seasonal variations, and application of waste heat recycling technologies, etc. This paper shows an example for process synthesis extending traditional pinch analysis according to the situation of energy utilization in different seasons. Seen from the optimization analysis, the energy consumption will decrease apparently through the heat integration over the year and the adjustment of the consumption structure of energy.

Optimization of Energy Utilization and Productivity of Heat Treating Batch-Type Furnaces

Abstract Due to the energy-intensive nature of the heat treating industry and the recent substantial increase of energy prices, the availability of predictive tools that can be used to optimize heat treatment processes has become a very pressing must. Load configuration (size and arrangement) during batch-type heat treating operations is the main factor that controls the rate of heat transfer between the furnace and the load, and hence it affects energy utilization and productivity of such operations. The main objective of this work is to develop a numerical model that can be used as a predictive tool for determining optimum loading of batch-type furnaces in order to achieve maximum productivity (mass treated per unit time) and minimum energy consumption per unit mass. A numerical model has been developed to simulate heat treatment processes in batch-type furnaces. The model has been validated by comparing numerical results with experimental data collected under laboratory and real-life conditions. Experiments have been carried out at the research facility at TPL as well as at different industrial sites. The paper presents the development and validation of the model as well as case studies of batch heat treatment cycles where best load configurations have been investigated.

Studies on the Optimal Method Adaptable to the Rural Community as the Biomass Energy and Optimization of Energy Utilization (II)

Studies on the Optimal Method Adaptable to the Rural Community as the Biomass Energy and Optimization of Energy Utilization (II)