Energy Consumption Parameters Research Articles

Carbon footprints of incineration, pyrolysis, and gasification for sewage sludge treatment

Thermal technologies have gained increasing attention in sludge management. This study applied life cycle assessment to assess the impacts to climate change of ten technological configurations (TCs) treating sludge with incineration, gasification, and pyrolysis. We used distributions of process parameters for quantifying the associated uncertainties and considered different energy exchanges. In a 55 %-fossil energy system, the TCs with various thermal processes showed impacts to climate change in a wide range of −2000 to 2000 kg CO2 eq/t total solid. A probabilistic comparison indicated that with a 10 %-fossil energy system, TCs with gasification and pyrolysis showed a > 95 % probability of performing better than TCs with incineration. Energy consumption and dewatering parameters contributed significantly to the uncertainty due to their large variation and sensitivity. This study emphasized the potential of optimizing key parameters and provided evidence from a climate change perspective for better technological selection and development in sludge management.

System for the Acquisition and Monitoring of Energy Consumption in the Context of Industry 4.0

This paper examines methodologies for monitoring energy consumption and operational parameters of an industrial assembly line, with a particular emphasis on the utilization of virtual tools, augmented reality, and real-time data visualization. The research examines the integration of an interface and the implementation of a data acquisition system, employing augmented reality to facilitate interaction with the collected data. The proposed system employs the Node-RED interface to facilitate the establishment of connections between constituent elements. The data is stored in a database, which provides support for decision-making in the analysis of energy consumption and operational parameters of the industrial assembly line. The results demonstrate that the solution is effective in enhancing energy resource management, identifying inefficiencies, and optimizing the performance of industrial equipment. The findings indicate that the implementation of such a system can markedly enhance the industrial process.

Application of energy sustainability model based on optical sensing technology in intelligent warehousing performance management in green manufacturing industry

As the global focus on sustainable development continues to grow, green manufacturing, as a way to optimize resource use and reduce environmental impact, needs to be combined with advanced technologies to improve its efficiency. This study aims to explore the application of energy sustainability model based on optical sensing technology in smart storage of green manufacturing industry, evaluate its impact on storage performance management, and further promote the development of green manufacturing industry. In this paper, an optical sensing system is constructed to monitor and analyze the energy consumption in storage environment in real time. In this study, a green manufacturing enterprise was selected as an example, and the optical sensor was used to monitor energy consumption, inventory status and environmental parameters in real time to ensure the real-time and accuracy of data. The collected data is analyzed to identify patterns and anomalies in energy use to optimize inventory management and logistics scheduling. Evaluate the energy efficiency of storage systems by setting energy efficiency indicators and compare them with traditional models to quantify improvements. On the basis of monitoring and analysis, the energy management strategy is continuously adjusted to enhance the adaptability and flexibility of the intelligent storage system. The research results show that the application of optical sensing technology can significantly improve the energy management performance in warehousing, through real-time monitoring and intelligent optimization, enterprises can achieve more efficient energy use, further promote the implementation of green manufacturing, and provide strong support for the realization of environmental and economic benefits.

Hybrid fuzzy logic and gravitational search algorithm based routing for wireless sensor networks

Over the recent years, the wireless sensor network (WSN) has garnered significant attention from both researchers and the general populace. Its application in diverse environmental scenarios, including weather monitoring, temperature regulation, humidity tracking, and military surveillance, extends beyond conventional boundaries. WSNs consist of numerous nodes, each functioning as a sensor with the primary responsibility of data sensing. These nodes operate under constraints such as power, energy, efficiency, and deployment considerations. Moreover, the power and other resources cannot be replaced and renewed therefore prolonging the network lifetime has become the main aspect of WSNs. Energy aware routing play’s important role to ensure the efficient data transmission with minimal power consumption and ensures the prolonged network lifetime. In this work, we focus on optimizing the routing process therefore we present a hybrid model which uses fuzzy logic for path identification and gravitational search optimization (GSA) for efficient path selection. The fuzzy logic considers energy consumption, residual energy, distance and delay parameters to identify the most suitable path for data transmission. The experimental analysis shows a significant improvement in network lifetime, delay an aspect delivery.

The Effect of a Wearable Assistive Trunk Exoskeleton on the Motor Coordination of People with Cerebellar Ataxia

The motor features of people with cerebellar ataxia suggest that locomotion is substantially impaired due to incoordination of the head, trunk, and limbs. The purpose of this study was to investigate how well a wearable soft passive exoskeleton worked for motor coordination in these patients. We used an optoelectronic system to examine the gait of nine ataxic people in three different conditions: without an exoskeleton and with two variants of the exoskeleton, one less and the other more flexible. We investigated kinematics using trunk ranges of motion, the displacement of the center of mass in the medio-lateral direction, and the parameters of mechanical energy consumption and recovery. Furthermore, we investigated the lower limb and trunk muscle coactivation. The results revealed a reduction of the medio-lateral sway of the center of mass, a more efficient behavior of the body in the antero-posterior direction, an energy expenditure optimization, a reduction of muscle coactivation and a better coordination between muscle activations. As a result, the findings laid the groundwork for the device to be used in the rehabilitation of individuals with cerebellar ataxia.

A review of electrooxidation systems treatment of poly-fluoroalkyl substances (PFAS): electrooxidation degradation mechanisms and electrode materials.

The prevalence of polyfluoroalkyls and perfluoroalkyls (PFAS) represents a significant challenge, and various treatment techniques have been employed with considerable success to eliminate PFAS from water, with the ultimate goal of ensuring safe disposal of wastewater. This paper first describes the most promising electrochemical oxidation (EO) technology and then analyses its basic principles. In addition, this paper reviews and discusses the current state of research and development in the field of electrode materials and electrochemical reactors. Furthermore, the influence of electrode materials and electrolyte types on the deterioration process is also investigated. The importance of electrode materials in ethylene oxide has been widely recognised, and therefore, the focus of current research is mainly on the development of innovative electrode materials, the design of superior electrode structures, and the improvement of efficient electrode preparation methods. In order to improve the degradation efficiency of PFOS in electrochemical systems, it is essential to study the oxidation mechanism of PFOS in the presence of ethylene oxide. Furthermore, the factors influencing the efficacy of PFAS treatment, including current density, energy consumption, initial concentration, and other parameters, are clearly delineated. In conclusion, this study offers a comprehensive overview of the potential for integrating EO technology with other water treatment technologies. The continuous development of electrode materials and the integration of other water treatment processes present a promising future for the widespread application of ethylene oxide technology.

Drying kinetics and moisture migration mechanism of yam slices by cold plasma pretreatment combined with far-infrared drying

The aim of this study was to evaluate the effects of a cold plasma (CP) pretreatment on the energy consumption, drying characteristics, and physicochemical parameters of yam slices at different temperatures during the far-infrared drying (IRD) process. The outcomes demonstrated that CP greatly increased drying efficiency while consuming less energy. At 60 °C, the drying time of yam slices with thicknesses of 3 mm, 5 mm, and 7 mm was shortened by 16 min, 24 min, and 32 min respectively. 5 mm thick yam slices during cooperative drying at 60 °C showed improved color retention which △E was only 5.09, the retention of bioactive components improved 20.5%. The loss of free water predominated and the degree of binding between moisture and material tightened as the drying time increased. MRI images showed that the proton density of the cooperative drying method disappeared faster, the moisture dropped rapidly from the center of the yam slice, followed by the edge of the yam. In general, the results showed that IRD + CP had a broad potential in improving the drying efficiency and product quality of the food industry.

Sustainability evaluation of C3MR natural gas liquefaction process: Integrating life cycle analysis with Energy, Exergy, and economic aspects

In a transition towards a clean energy future, natural gas serves as an interim fuel due to its lower emissions whereas the C3MR (propane pre-cooled mixed refrigerant process) is widely recognized for high efficiency and commercial scale application converting natural gas to LNG. Previously, numerous studies have been conducted to optimize the energy efficiency and performance of C3MR process but very little contribution has been made towards its sustainability analysis. To address this, a life cycle analysis approach is established to evaluate the carbon footprint of C3MR process. In order to showcase its link with energy consumption and put forward the groundwork of analyses, the research focuses on using simulation – assisted optimization to reduce the specific energy consumption (SEC) of C3MR process by optimizing design variables. The study highlights the use of mixed refrigerant (MR) as a process variable and validates that its optimal implementation has decreased SEC to 0.2195 kWh/kg LNG, while achieving a minimum inlet temperature approach (MITA) of main cryogenic heat exchanger (MCHX), less than 3 °C. The knowledge − based optimization approach applied in this research, developed comparative case studies on which multiple analyses are performed to verify scientific findings. Energy and exergy analyses are conducted to determine thermal efficiency of the process where the optimized case exhibits a reduced difference in its composite curves and an 8.65 % improvement in exergetic performance compared to the base case. The economic indicators assessment reveals that optimized case demonstrates lower capital and operating costs, establishing economic viability. Furthermore, the energy consumption parameters are utilized for evaluation of ecological impact ofLNGsupply chain through life cycle assessment (LCA) which reflects reduced greenhouse gas (GHG) emissions for optimized case. Hence, the current study fills existing research knowledge gaps in terms of providing a comprehensive process system engineering by evaluating energy synergistic point, thermodynamic irreversibility, process viability and sustainability using life cycle assessment.

Dimensional Analysis based Prediction Model for Fuel Consumption in the Deutz Agrotrac Tractor

Background: Fuel consumption is a critical factor in the performance of an agricultural tractor. And it can be affected by numerous parameters. Farming practices is an essential aspect that affects the cost of operation and the overall efficiency of the tractor. Predictions of fuel consumption rates in tractors and the energy consumption parameters of primary tillage equipment under local conditions are therefore essential selection criteria in purchasing decisions. Methods: A prediction model for fuel usage during tillage operations was created using dimensional analysis. The model was established by incorporating experimental findings into an equation. The experiment was conducted in the El Ouracia region of Setif, Algeria in 2022/2023 crop season, with fuel usage per time unit determined through topping-up the tank method. The fuel experiment parameters were measured according to their respective standard procedures. Result: By simulating fuel usage with tillage depth, tillage width, travel speed, soil bulk density and cone index. The constant in the prediction model was obtained. This developed model has the potential to forecast the tractor fuel consumption during tillage operation.

Design of proactive management system for residential buildings by using smart equipment

This study's object is an energy efficiency of residential sector. The work is aimed at solving the task to improve the energy efficiency of the housing sector by devising technical solutions for monitoring and managing energy consumption and microclimate parameters of buildings. The proposed proactive management system for residential buildings consists of multi-sensors measuring CO2, temperature and humidity, smart meters of heat and electricity consumption, and smart plugs. The equipment is combined into single system through an integration controller with remote user access through an interactive web interface. A feature of the technical solution is the ability to collect, process, visualize, and archive data on the consumption of energy, as well as on the key parameters of the microclimate of residential premises. The advantages of the system are its flexibility due to the possibility of integrating additional devices during operation, as well as the use of standard communication protocols, which enables the interchangeability of component elements. The implementation and testing were carried out under the conditions of a real pilot site. The use of the system in practice confirmed the efficiency and stability of the operation, making it possible to obtain data on the parameters of energy consumption and microclimate and devising recommendations for reducing energy consumption at the pilot site. It was established that the microclimate meets the requirements of the standards (air temperature is about 22 °С while relative humidity does not exceed 60 %). Decrease in energy consumption can be achieved by reducing the temperature of the heat carrier in the absence of residents, as well as by considering the influence of weather conditions. During periods of residents activity, an excess of the permissible level of CO2 was recorded, therefore, automatic ventilation systems should be provided in the apartments

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.

MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environments

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users’ data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption.

An Energy Consumption Model for SRAM-Based In-Memory-Computing Architectures

In this paper, a mathematical model for obtaining energy consumption of IMC architectures is constructed. This model provides energy estimation based on the distribution of a specific dataset. In addition, the estimation reduces the required simulation time to create an energy consumption model of SRAM-based IMC architectures. To validate our model with realistic data, the energy consumption of IMC is compared by using NeuroSim V3.0 for the CIFAR-10 and MNIST-like datasets. Furthermore, an application is created with our model to select highest performing quantization mapping based upon the parameters of energy consumption and accuracy.

Multi-objective modeling and evaluation for energy saving and high efficiency production oriented multidirectional turning considering energy, efficiency, economy and quality

To promote energy saving, high efficiency and quality production in the mechanical manufacturing industry, a large number of studies have been conducted on modelling and evaluation. However, most of the approaches have focused on single objective modelling, evaluation and conventional machining method like unidirectional turning (UDT). This paper presents a multi-objective modelling and evaluation method for energy, efficiency, economy and quality using a new process of forward-and-reverse multidirectional turning (MDT). Firstly, the multi-objective influential factors of cutting parameters for energy consumption, cutting time, production cost and surface roughness of MDT are analyzed to establish the corresponding models, respectively. Then taking the energy consumption, cutting time, production cost and surface roughness as the objectives, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for the multi-objective evaluation through different processes. Finally, the case study shows the practicability of the MDT method and evaluation method for energy optimization and energy conservation.

Energy consumption and environmental parameters in Madrid social housing. Performance in the face of extreme weather events

Housing energy rehabilitation is currently a priority in Europe. In vulnerable neighbourhoods, improving energy efficiency and comfort conditions has an important impact on people living conditions. Rehabilitation strategies are usually designed according to standard scenarios, without taking into account variations related to the users or the type of building. Monitoring this type of housing offers valuable information to be able to select the best strategies based on a diagnosis of their real performance.In this paper we present one year data on energy consumption, hygrothermal comfort and CO2 monitored for 22 dwellings in Madrid, located in six different vulnerable locations. In addition to normal periods, the monitoring campaign includes periods of extreme heat and cold, and two exceptional situations, the “Filomena" snowfall and the COVID pandemic period.The results report poor indoor environmental quality in these dwellings, both in the main rooms and in the bedrooms. Also poor building quality is observed. The recorded energy consumption is lower than that given as a reference in the standards or models, at the cost of a worse indoor environment. The analisys supports the importance of retrofit strategies that take into account the composition of the household, such as single-person households or those with children, the higher vulnerability of dwellings with higher demand, the subjective perception and the role of users, and the need to incorporate improvements in indoor air renewal.

Energy Metabolism in Residents in the Low- and Moderate Altitude Regions of Central Asia with MAFLD and Type 2 Diabetes Mellitus.

The knowledge about the features of energy metabolism in MAFLD in the population living at different climatic and geographic heights is lacking. The goal of this study is to explore the biochemical parameters of blood and erythrocyte energy consumption in patients with MAFLD with and without DM2 living in the low- and moderate-altitude regions of Central Asia. Our study was carried out on patients living in low-altitude mountains: Bishkek, altitude=750-800 m; n=67 (MAFLD with DM 2: n=24; MAFLD without DM2: n=25; control: n=18), and At-Bashy District, Naryn Region, altitude=2046-2300 m; n=58 (MAFLD with DM2: n=28; MAFLD without DM2: n=18; control: n=12). Non-alcoholic fatty liver disease was diagnosed according to history, laboratory tests, liver ultrasound, and exclusion of other liver diseases. The level of liver fibrosis was determined using the FIB-4 score. Blood adenosine 5'-triphosphate (ATP) was determined using the CellTiter-Glo method. Healthy residents living in moderate altitudes have significantly higher levels of cytosolic ATP in their blood (p+≤+0.05) than residents living in low mountains. MAFLD is characterized by an increase in the level of ATP concentration in their blood. ATP concentration decreased significantly in patients with MAFLD with DM2 living in moderate-altitude in comparison to those living in low-altitude mountains. The results suggest that chronic altitude hypoxia leads to a breakdown in adaptive mechanisms of energy metabolism of ATP in patients with MAFLD with type 2 DM.

Application of spring energy accumulators in forging and pressing equipment

The development of forging and stamping production requires automation of various pressure treatment equipment. An important feature of the pressure treatment mechanisms is their inherent unsteady mode of operation, consisting of often alternating acceleration and deceleration, so the proportion of inertial loads is quite large. Such mechanisms are considered to be among the least efficient in terms of energy consumption parameters The low efficiency of the known designs is caused by the inability to exclude the influence of inertial forces on energy consumption and the speed of the equipment. The use of damping devices is not able to solve this problem, since they are designed to absorb a certain amount of energy and when the number of strokes per minute changes, they can cause an increase in dynamic loads.

Combined infrared-convective drying of banana: Energy and quality considerations

This work evaluated the infrared (IR) drying combined with forced convection for processing banana using response surface methodology and desirability function. The effect of IR power (118–238 W) and unheated air velocity (1.5–4.5 m/s) on drying time, drying rate, effective moisture diffusivity (Deff), specific energy consumption (SEC), CO2 emission and product colour parameters were investigated at a significance level of 0.05. The experimental drying curves were predicted by the diffusive model solved with Robin boundary condition and a good agreement was obtained (R2 ≥ 0.9651). The values of Deff ranged from 1 × 10−8 to 4 × 10−8 m2/s increasing at higher IR powers and lower air velocities. The optimal condition (P = 238 W and v = 1.5 m/s) reached a global desirability value of 0.96 under which the final moisture content was 15.6 % with SEC of 12.51kWh/kgH2O and 0.050 gCO2/kWh. Therefore combined infrared-convective drying holds promise for the dried banana production.

Modeling of energy consumption parameters of a dairy enterprise

The problem of high energy intensity of Belarusian dairy products in comparison with the main competitors, leading to their insufficient energy efficiency due to an imperfect methodological basis for assessing energy consumption, is considered. Scientific proposals have been formulated for adjusting the procedure for calculating the energy and resource saving indicator and modeling energy consumption parameters, allowing for systematic management of this process.

Prediction of Energy Consumption in Horizontal Roughing Process of Hot Rolling Strip Based on TDADE Algorithm

The steel industry is the key industry of energy consumption. The optimization of rolling process parameters is an effective measure to reduce and optimize energy consumption. Accurate energy consumption prediction has an indispensable guiding function in the planning of process parameters. However, the traditional energy consumption prediction and machine learning methods have been unable to fit the needs of high precision and reliability. Taking the horizontal roughing process (HRP) of hot rolling process as the research object, this paper mainly introduces an energy consumption prediction mechanism model (ECPMM) based on a roller adaptive wear strategy by analyzing the strip forming mechanism and rolling process. Then, two directions adaptive differential evolution (TDADE) algorithm is proposed based on a novel adaptive strategy. This algorithm has better convergence speed and global optimization ability than other optimization algorithms in the parameter optimization of ECPMM. We carried out comparative experiments on five machine learning algorithms. The results show that the ECPMM optimized by TDADE is superior to the five machine learning algorithms in prediction accuracy and stability. Finally, we conducted ablation experiments on the energy consumption characteristics of the HRP process, which turns out that using a smaller reduction and roller radius can reduce energy consumption. Summarizing the above experimental results suggests that the ECPMM has high accuracy and robustness, and satisfies the requirements of practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —It is necessary to carry out the deep analysis and research of energy consumption prediction involved in the process planning of strip hot rolling, the inspiration of this article is stem from this point. The commonly used energy consumption prediction methods include mechanism modeling and machine learning, but both have disadvantages, like low prediction accuracy and black box, which must have to be performed to solve by adapting the production demand of high reliability. In this paper, the combination of the mechanism model and data-driven method is used to realize the accurate prediction of energy consumption and improve the reliability of the model. To the best of our knowledge, this is the first paper about the research on energy consumption prediction of hot rolling process based on mechanism model and data-driven, especially in data size, time granularity, algorithm design, and prediction performance. Such research is helpful to guide engineers to design rolling energy consumption. We will dive a bit deeper into learning the energy consumption and process parameters during the rolling process in future studies.