Unnecessary Energy Consumption Research Articles

Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN

SummaryInternet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering.

Occupant-Detection-Based Individual Control of Four-Way Air Conditioner for Sustainable Building Energy Management

In this study, individual control of a four-way air conditioner was developed based on the distribution of occupants to prevent unnecessary energy consumption during room-wide control. An occupancy detection algorithm was created in Python using YOLOv5 object recognition technology to identify the occupants’ distribution in space. Recorded video data were used to test the algorithm. A simulation case study for a building energy model was conducted, assuming that this algorithm was applied using surveillance cameras in commercial buildings, such as cafés and restaurants. A grey-box model was established based on measurements in a thermal zone, dividing one space into two zones. The temperature data for the two zones were collected by individually turning on the air conditioner for each zone in turns for a specific period. Manual closure was applied to each supply blade using a tape to provide cooling to the target zone. Finally, through energy simulations, the decreased rates in energy consumption between the proposed individual control and existing room-wide controls were compared. Different scenarios for the occupants’ schedules were considered, and average rates in energy savings of 21–22% were observed, demonstrating the significance of individual control in terms of energy consumption. However, marginal comfort violations were observed, which is inevitable. The developed control method is expected to contribute to sustainable energy management in buildings.

Exploration of Energy-Saving Technologies in Building Electrical System Design

Green energy conservation is the mainstream trend in the current development of the construction industry. The application of energy-saving technology in building electrical system design can effectively reduce energy consumption, avoid unnecessary energy consumption, and truly achieve energy conservation and environmental protection. Based on this, the article elaborates on the principles of energy-saving design in building electrical systems, and actively explores the application of energy-saving technologies from different perspectives such as optimizing power supply and distribution system design, adopting high-efficiency energy-saving lighting equipment, applying renewable energy, promoting smart home technology, and improving the efficiency of building electrical equipment.

An aeration requirements calculating method based on BOD5 soft measurement model using deep learning and improved coati optimization algorithm

Conventionally, the supply-side is over-aerated to meet the oxygen demand of biological wastewater treatment in wastewater treatment plants (WWTPs). Such systems do not consider the various influent loadings and actual oxygen consumption. It cannot respond effectively to fluctuating influent, resulting in unnecessary aeration and energy wastage. In this study, we developed a novel approach to calculate aeration requirements in real time. Firstly, the oxygen demand formula for biological nitrogen and phosphorus removal is modified by considering realistic factors (temperature, oxygen partial pressure and so on) to construct the aeration demand formula. Then, to address the problem that BOD5 in aeration formula is difficult to measure in real time, a novel hybrid model integrating Fuch mapping, reverse learning strategy, Coati Optimization Algorithm (COA), and Backpropagation Artificial Neural Network (BP-ANN) is proposed in this study. Finally, aeration requirements are calculated based on real-time water quality data. Experimental results show that the ICOA-BP model with 9 neurons in hidden layer predicted the optimum BOD5 concentration by achieving a 25.146 mean absolute error and a 0.735 coefficient of determination (all-R2) in all datasets. The optimization led to a 15.164 % aeration savings and 38.942 % energy savings. The proposed method of aeration calculation is automated and intelligent, responding to fluctuating changes in influent quality and reducing unnecessary aeration and energy consumption while meeting the requirements of effluent quality.

Time‐energy consumption optimal path‐constrained trajectory planning of excavator robotics

AbstractReasonable operation of each excavator joint under path constraints directly affects operation efficiency and energy consumption. Efficiency and energy consumption are considerably important performance indicators, particularly for large equipment, such as excavators. Therefore, we propose a quadratic trajectory optimization method based on time–energy consumption. With the pseudo‐path velocity, acceleration, and equivalent convex jerk as constraints, the interior‐point method was used to obtain the time–energy consumption optimal trajectory of an excavator. The time and energy consumption values for different weight coefficients were obtained, and the optimal trajectory planning of the excavator was achieved. Furthermore, the simulation results of the proposed method for lifting 70 kg of materials for a weight coefficient of 0.5 were compared with the result of second‐order cone programming. Experimental results showed that using proposed method to plan the motion of each joint reasonably and effectively reduced the pseudo‐path jerk, acceleration, and velocity peaks by 40.67%, 25.03%, and 12.54%, respectively, ensuring smooth excavator movement. It also improved the working efficiency of the excavator; reduced unnecessary energy consumption; and improved the stability, efficiency, and energy saving of the autonomous operation of the excavator.

Effectiveness of natural ventilation through single-sided window opening in air-conditioning rooms

Since many buildings are not equipped with fresh air systems, many people open windows to allow for natural ventilation while the air conditioner is running. However, prolonged window opening may cause unnecessary energy consumption. Additionally, the diverse ways of window opening lead to significant variations in indoor airflow distribution, consequently impacting ventilation efficiency. Therefore, this study used field experiments and computational fluid dynamics (CFD) to determine the air exchange efficiency and air change rate of single-sided natural ventilation in air-conditioning rooms by fitting the decay curves of tracer gas. CFD simulation was performed using the unsteady Reynolds-averaged Navier-Stokes (RANS) model to examine how window openings affect the room’s air distribution, and the computed indoor CO2 concentration was compared to the corresponding field experiment data. The results show that natural ventilation through the window opening induced constant heat and mass exchange between indoor and outdoor air, which caused extra cooling losses but was globally effective in eliminating indoor contaminants. The air exchange efficiency is influenced to varying degrees by factors such as the air supply register’s position, window opening percentage, and window opening position. The air supply register’s position exerted the most significant impact at 46.45%. Efficient indoor-outdoor air exchange can be achieved by opening windows intermittently. When the window is opened from one side and the opening percentage is 100%, the indoor environment can be improved by opening the window for about 10 min every 39 min for X-direction air supply and about 7 min every 39 min for Y-direction.

The Importance Of Vo2 Max In Young Soccer Players

"Soccer is a high-level skill sport that requires aerobic and anaerobic strength, as well as physical skills related to speed, agility, strength, and power. To meet these physical needs of soccer players, coaches now offer a variety of aerobic exercises that essentially use the lower and upper extremities, as well as large muscle groups. Correct technical performance of exercises protects not only from various injuries but also saves unnecessary energy consumption. For this reason, the purpose of this research is to critically analyze research that has examined the importance of aerobic endurance in young soccer players. The following databases were searched: Pub Med, Scopus, Google Scholar, CrossRef, etc. to find all relevant publications. The following keywords were used in this search: ""soccer"", ""young soccer players"", ""VO2 max"", and ""soccer performance"", along with adjectives such as ""effect"", ""change"" and ""impact"". It was found that the aerobic demands of soccer players are crucial and strategic for the game. Keywords: ""soccer"", ""young soccer players"", ""VO2 max"", ""soccer performance"", ""effect"", ""change"" and ""impact""."

Model predictive control for thermal comfort and energy optimization of an air handling unit system in airport terminals using occupant feedback

The dynamic fluctuations in occupant flow within airport terminals contribute to delays in the system's response under traditional feedback control. This imbalance between supply and demand can result in discomfort and unnecessary energy consumption. While Occupant-Based Model Predictive Control (OBMPC) has gained research interest as it leverages occupant forecasts to enhance control performance, there is limited knowledge regarding its application in the air-conditioning systems of airport terminals, mainly due to the intricate dynamics of occupant flow in terminal buildings. In this study, we proposed a model predictive control strategy based on dynamic occupant flow with the goal of fast responding to occupant flow and reducing energy consumption. We integrated mathematical formulas and the Anylogic simulation software to predict occupant variations in the baggage claim hall. The model predictive control strategy of the air conditioning system was proposed with the predicted occupant flow. The proposed strategy was evaluated throughout the whole cooling season in the validated simulation model of the air handling unit system of the baggage claim hall at an airport terminal. The results demonstrated that the proposed strategy could effectively respond to variations in occupancy and achieve 10% energy savings throughout the entire cooling season, compared to conventional feedback control. Our work presents a viable solution to address system regulation lag and reduce energy consumption without jeopardizing thermal comfort.

A Scenario-Based DVFS-Aware Hybrid Application Mapping Methodology for MPSoCs

Sound techniques for mapping soft real-time applications to resources are indispensable for meeting the application deadlines and minimizing objectives such as energy consumption, particularly on heterogeneous MPSoC architectures. For applications with input-dependent workload variations, static mappings are not able to sufficiently cope with the run-time variation, which can lead to deadline misses or unnecessary energy consumption. As a remedy, hybrid application mapping (HAM) techniques combine a design-time optimization with run-time management that adapts the mappings dynamically to the changes of the arriving input. This paper focuses on scenario-based HAM techniques. Here, the application input space is systematically clustered such that data inside the same scenario exhibit similar characteristics concerning workload when being processed under the same operating points. This static clustering of the input space into data scenarios has proven to be a good abstraction layer for simplifying the design and employment of high-quality run-time managers. However, existing state-of-the-art scenario-based HAM approaches neglect or underutilize the synergistic interplay between mapping selection and the usage of dynamic voltage/frequency scaling (DVFS) when adapting to workload variation. By combining mapping and DVFS selection, variations in the input can be either compensated by a complete re-mapping of the application, evoking a potential high reconfiguration overhead or by just changing the DVFS settings of the resources, offering a low-overhead adaptation alternative and thus significantly reducing the necessary overhead compared to DVFS-agnostic HAM. Furthermore, DVFS enables a fine-grained adaptation of a mapped application to the input data variation, e.g., by slowing down tasks with no impact on the end-to-end latency for the current input using low-frequency DVFS settings. It is shown that this combined approach can save even more energy than a pure mapping adaptation scheme, especially in the presence of data scenarios. In particular, scenario-based design operates as a catalyst for eliciting the synergies between a combined DVFS and mapping optimization and the peculiarities inside a data scenario, i.e., exploiting the commonalities inside a data scenario by perfectly tailored DVFS settings and task mapping. In this scope, this paper proposes two supplementary scenario-based DVFS-aware HAM approaches that consistently outperform existing state-of-the-art mapping approaches in terms of the number of deadline misses and energy consumption as we demonstrate in an empirical study on the basis of four different applications and three different architectures. It is also shown that these benefits still apply to target architectures with increasing mapping migration overheads, thwarting frequent mapping reconfigurations.

Study on the rigid-discrete coupling effect of scraper conveyor under different chain speed-load conditions

The complexity of the underground environment in coal mines often leads to varying load conditions during the operation of the scraper conveyor, which can affect the lifespan of its components and result in unnecessary energy consumption. A test platform for the scraper conveyor was constructed based on the similarity theory to measure torque, speed, chain tension, and scraper acceleration during transportation. A DEM-MBD model of the scraper conveyor was developed and validated through transport tests and similarity theories to analyze the rigid-discrete coupling effect under different chain speed-load conditions. The results revealed a stratification phenomenon and a Brazilian fruit effect in the movement of coal. The average velocity of the upper and lower coal layers gradually increased during the transportation, while the difference between them gradually decreased. As the load increased, the stacking density and height of coal between scrapers also increased, leading to a higher force exerted on the scraper and chain. As the chain speed increased, the stacking density and height of coal between scrapers decreased, along with a decrease in the force applied to the scraper and chain. The formation of three-body wear necessitates a specific positional condition. When the scraper (chain)- coal-deck plate (chute liner) forms a particle stagnation state, severe wear occurs on the parts. This study provides a foundation for analyzing the transport mechanism of scraper conveyor from the particle perspective, offers a simulation reference for analyzing the mechanical and tribological characteristics of the line pan and scraper chain, and serves as a guideline for the future development of transportation state monitoring and the optimization and enhancement of components under different working conditions.

UGV에서 효율적인 노면 모니터링을 위한 퓨전 센서 시스템

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of =10, =10.

Secure multi-cloud resource allocation with SDN and self-adaptive authentication

The multi-cloud-based resource allocation is based on multiple constraints and multiple configurations that may vary based on customer requirements and cloud service providers' schemes. Here, security is a serious concern that can be ensured by an authentication scheme based on the Self-Adaptive Flower Pollination based RSA (SA-FPRSA) scheme to ensure data integrity. The SA-FPRSA plays an initial role in ensuring security by enabling authenticated data availability. On the other hand, the resource allocation to the Virtual Machine (VM) is a challenging task due to overwhelmed data in a VM that may face unnecessary energy consumption and time complexity issues. Therefore, the proposed model has introduced a Self-adaptive Heap-based optimizer (HBO) for Multi-Cloud based resource allocation. The performance of the models is evaluated. The proposed model attained an Efficiency of 99.09% and Security of 98.72%. Thus, from the results, the recommended model is proven to be better than the existing models.

ENVIRONMENTAL IMPACT OF CRYPTOCURRENCY MINING: SUSTAINABILITY CHALLENGES AND SOLUTIONS

The rapid growth of cryptocurrencies over the past 14 years has led to increased deep-level mining activities. This research aims to explore the environmental impacts resulting from the surge in crypto mining and proposed solutions to mitigate these impacts. Cryptocurrencies, gaining popularity as alternative investments and global payment tools, have significantly boosted crypto mining activities. However, the increasing number of transactions requiring computer validation has resulted in adverse consequences for the environment, particularly in terms of substantial energy consumption. Literature review and systematic analysis were conducted to comprehend the environmental impact of crypto mining, focusing on major cryptocurrencies such as Bitcoin, Ethereum, and others. The analysis highlights that crypto mining, especially Bitcoin, requires a significant amount of electricity, leading to a substantial carbon footprint and broad environmental repercussions. Proposed solutions to address the environmental impact of crypto mining include the use of renewable energy sources such as solar and wind power, enhancing the efficiency of specialized mining devices (ASICs), and exploring more energy-efficient consensus mechanisms like Proof of Stake (PoS) compared to the currently utilized Proof of Work (PoW). Reducing redundancy in blockchain technology has also been identified as a crucial step in minimizing unnecessary energy consumption. However, this research has limitations concerning data consistency, a comprehensive understanding of overall environmental impacts, and continuous technological changes in the crypto world. Therefore, future research should focus on developing more efficient consensus mechanisms, effective policy frameworks and governance, as well as real-world implementation studies to evaluate the sustainability solutions proposed.

FUTURE FACTORY: A CASE STUDY ON ENERGY AND CARBON REDUCTION IN A BIO-PHARMACEUTICAL PLANT

Manufacturing industry is recently facing many challenges such as shorter product life cycles, diversified demands and green manufacturing. According to the deployment of carbon peaking work in Zhejiang province of China, pharmaceutical and chemical companies must accelerate product upgrading and green technology transformation of production methods to promote the carbon emission peaking. To this end, the bio-pharmaceutical company named Conba optimizes, upgrades and innovates existing technologies and equipment when applying energy-saving and consumption-reducing processes, so that they can better meet the application conditions of energy-saving and consumption-reducing technologies in the actual production process. Using the key production parameters of the equipment and equipment monitoring technology, Conba improves the utilization rate of operating equipment and reduces unnecessary energy consumption through data analysis to build a future factory with high energy efficiency, green environment protection and comfortable work environment.

Energy Consumption Reduction in Wireless Sensor Network-Based Water Pipeline Monitoring Systems via Energy Conservation Techniques

In wireless sensor network-based water pipeline monitoring (WWPM) systems, a vital requirement emerges: the achievement of low energy consumption. This primary goal arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the need for frequent battery replacement. Given that sensor nodes in such applications are typically battery-powered and often physically inaccessible, maximizing energy efficiency by minimizing unnecessary energy consumption is of vital importance. This paper presents an experimental study that investigates the impact of a hybrid technique, incorporating distributed computing, hierarchical sensing, and duty cycling, on the energy consumption of a sensor node in prolonging the lifespan of a WWPM system. A custom sensor node is designed using the ESP32 MCU and nRF24L01+ transceiver. Hierarchical sensing is implemented through the use of LSM9DS1 and ADXL344 accelerometers, distributed computing through the implementation of a distributed Kalman filter, and duty cycling through the implementation of interrupt-enabled sleep/wakeup functionality. The experimental results reveal that combining distributed computing, hierarchical sensing and duty cycling reduces energy consumption by a factor of eight compared to the lone implementation of distributed computing.

Automatic Transfer Switch Kontrol Menggunakan Internet Of Thing (IoT)

This research aims to develop and implement an Automatic Transfer Switch (ATS) control system using the Internet of Things (IoT) within the Nurul Jadid University environment. ATS is an important device in an electrical distribution system that allows automation of switching between primary and backup electrical power sources in fault situations. By utilizing IoT technology, this research aims to improve the efficiency, performance and monitoring of ATS at Nurul Jadid University, The research methodology involves designing, manufacturing, and testing a prototype of an IoT-based ATS system. Smart sensors will be installed on ATS devices to monitor parameters such as voltage, current, temperature and device status. Data collected by sensors will be sent via the IoT network to a monitoring platform that can be accessed in real-time by operators and maintenance personnel. It is hoped that the results of this research will increase the availability and reliability of electricity supply at Nurul Jadid University, by enabling early detection and quick action in disturbance situations. In addition, this system will also help optimize the use of electrical resources and reduce unnecessary energy consumption.
 By combining IoT technology with ATS control, this research presents an innovative solution to increase the efficiency and reliability of electricity supply in university environments. The results of this research can be a guide for other institutions interested in adopting similar technology in their electricity resource management

Modification effects of nanosilica on asphalt binders: A review

Abstract Nanosilica (NS) may be obtained as a by-product in several industrial processes and is associated with high availability and affordability among other nanoadditives. NS has been used for asphalt modification, and improvement in high-temperature performance and several other physical properties has been reported. However, due to the wide varieties and intrinsic variability of asphalt materials, concerns have been raised about the reproducibility of some conclusions, and therefore, a review was conducted. According to the results, NS additives with a purity of over 99% were usually used for modification and the specific surface area was over 100 m2/g, leading to the superior absorption of asphalt and thus improvement in rutting resistance of the mixtures. Due to a lack of guidance for the blending procedures, inconsistency exists among studies in the shear speed, blending duration, and temperature, leading to possible excessive blending and unnecessary energy and time consumption. Rises in NS concentration would cause higher softening point and rotational viscosity, and therefore elevated temperatures for mixing and paving, but penetration would be reduced. According to rheological evaluations, with the rise of NS concentration, the rutting parameter would increase, indicating an improvement in the high-temperature performance, but the low-temperature performance may deteriorate.

Development of an automatic personal comfort system (APCS) based on real-time thermal sensation prediction

Most existing building thermal environment design methods focus on the overall environment without considering individual demands, which can result in inadequate comfort and unnecessary energy consumption. To address individual differences, appropriate measures must be followed at the individual level. This study presents the development of an automatic personal comfort system (APCS) for regulating the local environment of individuals by integrating two personal thermal comfort technologies, personal comfort system (PCS) and personal comfort model (PCM). The equipment enables thermal sensation prediction through infrared skin temperature and environmental parameters measurements, which is used to regulate the corresponding PCS device. The proposed thermal sensation prediction model based on random forest can achieve a prediction accuracy of 69%, while the PCS control strategy comprehensively considers thermal sensation vote (TSV) and airflow acceptability. The APCS performance test experiment involved 25 male adult subjects. The results indicated that PCS operating states were consistent under both manual and automatic control conditions. Moreover, TSV remained within the neutral zone for most participants throughout the experiment. These findings demonstrate that the APCS realized good automatic operation to ensure the comfort of the personnel. The APCS framework holds reference value for research on personal thermal comfort and future thermal environment construction, extending beyond the equipment itself.

Reusing wastewater from Coffea arabica processing to produce single-cell protein using Candida sorboxylosa: Optimizing of culture conditions.

Coffee is a crop of significant socioeconomic importance, and the reuse of agri-food by-products and biowaste has great potential across several industries. Coffee wastewater (CWW) is a valuable resource containing essential nutrients that can be utilized by Candida sorboxylosa for single-cell protein (SCP) production. This utilization contributes to mitigating the negative impacts of agro-industrial waste. The optimization of culture conditions using the design of experiments (DoE) technique is crucial in understanding the environmental factors influencing metabolite production. In our study, the DoE technique was employed to analyze culture conditions, including room temperature, pH 8.4, agitation at 200 rpm, a headspace of 60% (v/v), and an inoculum of 0.75 DO600nm over 28-h period. This approach resulted in a remarkable SCP yield of 64.4% and dry cell weight (DCW) of 2.26 g/L. It is noteworthy that there is no literature reporting SCP production under alkaline pH conditions in yeast. Interestingly, our work demonstrated that an alkaline pH of 8.4 significantly influenced SCP production by C. sorboxylosa. The DoE technique proved to be an efficient statistical tool for optimizing culture conditions, offering several advantages, such as: (i) conducting cultures at room temperature to minimize unnecessary energy consumption; (ii) reducing the incubation time from 46 to 28 h, thereby enhancing overall productivity; (iii) achieving 1.7-fold increase in SCP yield compared to previous basal production levels.

Energy-saving thermal management system coupling phase change material with discretely-operating liquid cooling

Hybrid battery thermal management (BTM) systems consisting of passive phase change material (PCM) cooling and active liquid cooling (LC) demonstrate the advantages of relatively simple structure and excellent heat-dissipating performance. However, the conventional continuously-operating mode of the LC commonly leads to unnecessary energy consumption and “edge-overcooling” phenomenon. In this work, we design a hybrid BTM system and propose an energy-saving mode by introducing a flexible discretely-operating LC strategy. The results show that under normal working conditions with relatively low discharge rates and low ambient temperature (Tamb), the PCM plates have been able to meet the temperature-control requirements without launching LC. Under most of the harsh working conditions with higher discharge rates and Tamb, a discretely-operating mode of the LC with a suitable operating ratio (Rope) may relieve the “edge-overcooling” phenomenon, reducing the temperature difference (ΔT) of the battery module, and achieve an energy-saving purpose. For example, under the Tamb of 30 and 35 °C with a high-rate discharge of 3 C, the ΔT of the battery module can be reduced by 1.7 and 0.5 °C by selecting a Rope of 0.10 and 0.50, and the energy consumption can be reduced by 90 % and 50 % as compared to the conventional continuously-operating mode, respectively.