Energy Consumption Parameters Research Articles

Mathematical and artificial neural network modeling for vacuum drying kinetics of Moringa olifera leaves followed by determination of energy consumption and mass transfer parameters

Mapping the drying characteristics of biological products is essential for drying time estimation and reduction of energy consumption. The knowledge of mass transfer parameters during different drying conditions is required for process and equipment design and is of great industrial importance. In this work, an Artificial Neural Network (ANN) approach was adopted to model the vacuum drying kinetics of moringa leaves. Levenberg–Marquardt’s training algorithm with LOGSIGMOID and TANSIGMOID hidden layer transfer functions gave superior results for the prediction of moisture content and moisture ratio, respectively. Further, a comparative evaluation of the predictive capability of ANN and 7 different semi-empirical models was performed. The Page model was found suitable to fit the experimental data with a R2 comparable to that of ANN. However, the MSE observed for ANN (1.05 × 10−6) was significantly lower than that of Page model (2.56 × 10-6 to 5.81 × 10-4). Effective moisture diffusivity and mass transfer coefficient increased with increase in temperature from 0.71 × 10-9 to 1.91 × 10-9 m2/s and, 1.07 × 10-7 to 4.07 × 10-7 m/s, respectively. Activation energy for drying of moringa leaves was calculated as 42.84 kJ/mol which showed moderate energy requirements for moisture diffusion. Specific energy consumed was directly affected by drying time and varied from 6.07 to 22.26 kW h/kg. Drying temperature of 60 °C resulted in higher drying rate, lower drying time and energy consumption and therefore, recommended for drying of Moringa olifera leaves.

A sensitivity analysis of design parameters of BIPV/T-DSF in relation to building energy and thermal comfort performances

In recent years, Building-Integrated Photovoltaic/Thermal Double-Skin Façade (BIPV/T-DSF) shows great potentials on improving indoor thermal comfort and energy efficiency for buildings. Previous studies assessed both indoor thermal comfort and energy efficiency of a BIPV/T-DSF building through the use of the photovoltaic devices and ventilation types of the BIPV/T-DSF, however, which did not evaluate the effect of other dominant design parameters on the BIPV/T-DSF's performance. This paper presents a simulation study of sensitivity analysis on the correlations between indoor thermal comfort and energy consumption and various design parameters of the BIPV/T-DSF operating in different configurations and climatic conditions. The results showed that solar heat gain coefficient of the BIPV/T-DSF's external window possessed the highest importance affecting indoor thermal comfort and energy consumption, while thermal transmittance of both the internal and external windows and cavity depth of the BIPV/T-DSF showed also the notable importance to the building performances.

Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling

Improving the energy efficiency of chemical industries and increasing their environmental friendliness requires an assessment of the parameters of consumption and losses of energy resources. The aim of the study is to develop and test a method for solving the problem of optimizing the use of energy resources in chemical production based on the methodology of descriptive statistics and training of neural networks. Research methods: graphic and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing their consumption; correlation analysis with the construction of scatter diagrams to identify the dependences of the range of limit values of electricity consumption on the average rate of energy consumption; a method for training neural networks to predict the optimal values of energy consumption; methods of mathematical optimization and standardization. The authors analyzed the trends in the energy intensity of chemical industries with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy; determined the dynamics of energy losses at Russian industrial enterprises; established the correlation dependence of the range of limiting values of power consumption on the average rate of power consumption; determined the optimal limiting limits of the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The results of the study can be used in the development of software complexes for intelligent energy systems that allow tracking the dynamics of consumption and losses of energy resources. Using the results allows you to determine the optimal parameters of energy consumption and identify reserves for improving energy efficiency.

A Mini Review on Energy System Modelling for Enhancing Energy Efficiency in Malaysia

Energy efficiency is an important energy consumption parameter to indicate the efficient use of energy. Through enhancing energy efficiency, the harvested energy (fossil or non-fossil) could be used in the most impactful manner, which reduces the energy wastage and carbon emission. This study performs a critical review of energy system modelling, which involves the modelling studies of renewable energy and energy efficiency measures for the national energy sector. The energy value chain is typically related to the national energy policies. Thus, this study continues with a review of the national energy policies from several countries. This study concludes with comprehensive comments and analysis on the feasibility of the national energy system modelling perspective for Malaysia to achieve the 8% of energy efficiency savings by 2025 for sustainable energy development.

Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment

Energy consumption estimation and management of the maritime Unmanned Surface Vehicles (USV) is an important issue to deal with energy minimization techniques such as path planning, tasks scheduling, etc. In this paper, we introduce the energy consumption parameter in USV simulation through three contributions: 1) An analytic USV's energy consumption model is developed based on the three-degrees-of-freedom dynamic model of surface vessels. 2) A reverse engineering approach is proposed to identify the previously used dynamic model parameters based on a set of scenarios executed within a recent simulation environment. 3) The simulator engine is enriched with the consumption modelling tools such that the power absorbed by the USV is instantaneously calculated and returned; thus, the required energy of any predefined scenario is available as a new simulation result.

The potential of building integrated Photovoltaic (BIPV) systems for reducing the energetic impact of Italian supermarkets

In the perspective of sustainable cities, urbanist and planners have to deal with a constantly increasing penetration of renewable energy systems (RES) in the urban structure: in tertiary sector, retail and supermarket stores as particularly energy-intensive compounds play a core role in this scenario, as they are often characterized by similar kinds of structures mostly, large surfaces and a single floor, and analogous composition of energy loads. Most of the current research focuses on energy efficiency, but these buildings are particularly interesting for a systematic application of PhotoVoltaic (PV) generation technology as the energy uses associated with them are mainly electrical, in connection with air conditioning and food cooling. This article analyses the energy consumption in the supermarket sector for the sale of food. After an analysis of the main energy consumption parameters, standard solutions for the sizing of PV systems are proposed based on different design objectives, highlighting the potential of each proposed solution. Results show that a high percentage of self-consumption can be achieved, and that a battery storage set at a mean daily PV potential production level (4 kWh/kW in the case) perfectly suits to reach a self-sufficiency between 50-70%. Retail and food stores have proven to be a perfect promoter for PV diffusion either in a high self-consumption configuration, or turning them into energy hub for mobility to building or energy sharing policies.

Embodied exergy-based analysis of a municipal solid waste treatment system with uncertainty inclusion

The development of an integrated solid waste management (ISWM) system is still a matter of interest in many countries. One of the main challenges is to efficiently allocate the material streams in order to save energy and recover materials. The aim of this paper is to use the embodied exergy criteria to evaluate the distribution of the material streams into a solid waste (SW) treatment system composed by: a mechanical biological treatment (MBT) plant for refuse derived fuel (RDF) production and a paper recycling plant for cardboard production. Two scenarios are compared, based on the inlet mass flow to the MBT plant and the cardboard production. Stochastic tools based on Monte Carlo simulation are adopted for generating simulation scenarios, in order to account for the uncertainty that occurs in external (e.g., waste composition) and internal (e.g., equipment energy consumption) parameters.

Methodology and Software Tool for Energy Consumption Evaluation and Optimization in Multilayer Transport Optical Networks

In communication networks, the volume of traffic, the number of connected devices and users continues to grow. As a result, the energy consumption generated by the communication infrastructure has become an important parameter that needs to be carefully considered and optimized both when designing the network and when operating it in real-time. In this paper, the methodology of calculation of complex parameters of energy consumption for transport telecommunication networks is proposed. Unlike the known techniques, the proposed methodology takes into account heterogeneity and multilayer networks. It also takes into account the energy consumption parameter during the downtime of the network equipment in the process of processing the service data blocks, which is quite an important task for improving the accuracy of energy consumption at the stage of implementing the energy-saving network. We also developed simulation software to estimate and manage the energy consumption of the optical transport network using the LabVIEW environment. This software tool allows telecommunication network designers to evaluate energy consumption, which allows them to choose the optimal solution for the desired projects. The use of electro-and acousto-optical devices for optical transport networks is analyzed. We recommended using electro-optical devices for optical modulators and acousto-optical devices for optical switches. The gain from using this combination of optical devices and the parameter of rij electro-optical coefficient and M2 acousto-optical quality parameter found in the paper is about 36.1% relative to the complex criterion of energy consumption.

Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks

Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

SDN‐enabled Cognitive Radio Network Architecture

In this study, a new network architecture based on the software-defined networking (SDN) approach is proposed for cognitive radio networks (CRNs). The proposed network architecture [software-defined cognitive radio (SDCR)] assumes the responsibilities of network resource management for CRNs and provides a dynamic spectrum management mechanism with an SDN controller. In this way, the dependency of network users on base stations is reduced in dynamic cognitive radio environments, and network performance is improved by delegating some of the management responsibilities to the controller. The performance analysis of the SDCR is carried out through the RIVERBED MODELER simulation software. End-to-end delays and packet loss rates for the primary network are investigated by selecting different offered loads for secondary users. In addition, for the equal and different packet sizes, primary network and SDCR throughput are examined and network performance is improved by using channel bonding technique. The results indicate that the SDCR outperforms the traditional CRN architecture, in terms of the throughput, and the proposed architecture can provide effective performance. Bit error rate parameter is investigated in the study and the energy consumption parameter of the SDCR is also compared with the cognitive radio wireless network.

Improved Whale Optimization Algorithm and Convolutional neural network based Cooperative Spectrum Sensing in Cognitive Radio Networks

ABSTRACT In this manuscript, we establish a Cognitive Radio Network (CRN) based on Cooperative Spectrum Sensors (CSS) with Improved Whale Optimization Algorithm (IWOA). In our work, the main users are provided for the support of multiple cooperating secondary users. To obtain, the computed particular exposure samples spatial and spectral correlation is utilized. To instruct the particular exposure samples of the secondary user, the convolutional neural network (CNN)-based Cooperative Spectrum Sensors (CSS) framework is used. An Improved Whale Optimization Algorithm (IWOA) is comprised to calculate the weight function of CNN optimally raises the accuracy. A weight of inertia for Whale Optimization Algorithm (WOA) used to perform the WOA (IWOA). Finally, the primary user of the CNN structure can accurately perceive. Assessment metrics are performed based on delay, delivery ratio, energy consumption, impartiality index, and performance parameters. According to the proposed method, accuracy is 95.36%.

Operation Analysis of a SAG Mill under Different Conditions Based on DEM and Breakage Energy Method

As one of the machines widely used in mining, a semi-autogenous grinding (SAG) mill can significantly improve the roughing efficiency of rock. But the SAG mill still faces the obstacles of significant energy consumption and empirical operation parameters. In order to obtain the optimal operation parameters of a SAG mill, in this paper, the discrete element method (DEM) is used to simulate the breakage process of the particles by controlling three parameters, i.e., the mill speed ratio, the mill fill level ratio, and the steel ball ratio. This method simulates the particles size, mill power, and qualified particles quality of crushed particle, which reveal the grinding strength and energy consumption of the SAG mill. In this paper, the grinding changes of a SAG mill under different parameter conditions are explored. Firstly, an experiment on the influence of a single parameter change on the mill’s operation is set up, and then the influence of three parameter changes on the mill’s operation is analyzed. These changes are characterized by particle size and mill power. Simulation results under the ∅5250 × 500 mm mill model show that the mill operates with the optimal effect when the mill is under the condition of 80% critical speed and 15% fill level; the power of the mill does not increase linearly with an increase in the mill speed ratio, but will decrease after 85% of the critical speed, and finally increase again; the optimal steel ball ratio in the SAG mill depends on the simulation time (mill actual working time) and the limitation of the rated power. The mill speed, fill level ratio, and steel ball ratio can significantly affect mill operation, and our conclusions can provide a reference for an actual situation.

Effective energy consumption parameters in residential buildings using building information modeling.

Building information modeling can help in predicting the energy efficiency in future based on dynamic patterns obtained by visualization of data. The aim of this study was to investigate the effective parameters of energy consumption using BIM technology which can evaluate the buildings energy performance. First, three forms of general states in the building were modeled to evaluate the proposed designs in Autodesk Revit Software. Then, the main building form for energy modeling and analysis was selected. Autodesk Revit 2020 software was also used to obtain the results of climate data analysis and building energy consumption index. Finally, the most optimal mode was selected by examining different energy consumption modes. The results showed that the use of building information modeling technology in adjusting the parameters affecting energy consumption can save energy cost up to 58.23% in block D. Energy cost savings for block C and the western lobby were obtained as 51.03% and 43.05%, respectively. Based on energy use intensity, energy cost savings for blocks C, D, and the western lobby were estimated as 16.67%, 16.30%, and 11%, respectively. The results of parametric studies on alternative schemes of energy use intensity optimization showed that 16.30% savings could be achieved by the base building model in a 30-year time horizon. Therefore, it was concluded that optimization of energy consumption would reduce the environmental pollutants emission and contribute to preservation and sustainability of the environment.

Analysis of power parameters of a rotary mill

The paper (publication) presents a description of the developed design of a rotary mill with a complex mechanical and acoustic effect on the dispersed material, as well as analytical studies of the parameters of energy consumption of a rotary mill, based on the main physical processes occurring in it. The research was carried out by mathematical and simulation modeling of technological processes in the grinding chamber of the mill, as well as on various technological paths of the unit. To determine the main parameters that make up the total power consumption of the unit, an approach was chosen to differentiate the processes by their physical nature and contribution to the formation of the total power consumption of the rotary mill.The obtained results of mathematical modeling of processes occurring inside the technological paths of the unit allowed getting expressions for determining the main power costs of the unit depending on the characteristics of the unit design for each component of the total power required for effective operation of the unit at the required parameters of the dispersion of the finished product.

Comparison of the property of homogeneous and heterogeneous ion exchange membranes during electrodialysis process

Recently, electrodialysis technology has received widespread attention in the fields of water resource utilization. The core of the electrodialysis technology is the structure and performance of the ion exchange membrane. This study uses the single variable method to investigate the effects of energy consumption, current efficiency, and other parameters of homogeneous and heterogeneous membranes, then by chemical analysis method and scientific numerical analysis to determine the best operating parameters for the electrodialysis process of different ion exchange membranes. The experimental results show that the current efficiencies of heterogeneous and homogeneous membrane can reach 0.9295 and 0.9577 respectively. The highest concentration in the concentration chamber by using heterogeneous and homogeneous membrane can reach 149.30 g/L, 191.24 g/L, respectively. Meanwhile, the structural stability of the two ion exchange membranes was compared by concentration experiment and system stability analysis. These experimental results indicated the direction of homogeneous and heterogeneous membrane in electrodialysis engineering.

Hierarchical WSN protocol with fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB-ER)

Wireless Sensors Network (WSN) is used for monitoring and recording the physical environment and to transfer the sensed data to a central location by means of widely distributed sensor nodes. One of the major issues with WSNs is limited energy of sensor nodes and high energy consumption during data relay. An energy efficient routing protocol that consumes less energy in data transmission can enhance the network lifetime to a great extent. Considering it, this paper presents, a hierarchical routing protocol Fuzzy Multi-criteria Clustering and Bio-inspired Energy-efficient Routing (FMCB-ER) to enhance network lifetime thus improving upon the operational time of WSN based applications. Here in this approach a grid-based clustering technique is used to form the robust clusters. An adaptive Fuzzy Multi-Criteria Decision-Making (AF-MCDM) that combines Fuzzy-AHP and TOPSIS is then applied for optimum cluster head (CH) selection. For the purpose, the approach considers three broad parameters namely, energy, QoS and node position along with their six sub-criteria. After CH selection, the Emperor Penguin Optimization (EPO) is used to find optimal route for data transmission from CH to sink. The proposed method is evaluated and compared with other existing routing approaches on parameters of energy consumption, lifetime, throughput, end to end delay, jitter, packet delivery ratio, latency, number of dead, alive nodes. Simulation results confirm that the proposed approach outperforms the compared approaches on above discussed parameters. Especially, the proposed approach reduces the energy consumption up to 13% and increases the network lifetime up to 8% in comparison to compared approaches.

A new adaptive PSO-PID control strategy of hybrid energy storage system for electric vehicles

Research on optimization of control strategy for hybrid energy storage system (HESS) of the electric vehicle (EV), a new adaptive control strategy based on particle swarm optimization (PSO) algorithm is proposed in this paper. The steady-state power of the filtered power is used as the ideal output power of the battery. For the steady-state current output of the battery, the output power of the ultracapacitor is dynamically adjusted by the proportional-integral-derivative (PID) controller to construct a power difference control structure. The parameters of PID controller are optimized by PSO algorithm, and the target test is compared and analyzed based on MATLAB/Advisor. The research results show that the proposed PSO-PID control strategy can quickly eliminate the power deviation and achieve the approximate global optimization of the EV energy management strategy. Compared with the pre-optimized PID control strategy, the output current and power of the battery pack are smoother and the total power consumption is reduced by 3.8360% and 0.5125%, respectively. Then, the energy consumption parameters of PSO-PID are compared with the theoretical minimum energy consumption calculated by dynamic programming (DP) algorithm, and the deviation is less than 1% under both conditions.

Big data driven predictive production planning for energy-intensive manufacturing industries

Improving energy and resource efficiency in manufacturing is an important goal for enterprises to sustain their competitive advantages. Predictive production planning is a new solution to achieve such goal, following the direct improvement of energy efficiency and indirect energy savings through better scheduling. With the emergence of new information and communication technologies under the background of Industry 4.0, the amount of various energy and resource data obtained through Internet of Things is reaching the magnitude of big data. It poses a challenge to traditional data processing and mining methods for predictive production. To solve this challenge, in this paper, the big data driven predictive production planning is proposed to improve energy and resource efficiency for energy-intensive manufacturing industries. Additionally, the cube-based energy consumption models and long short-term memory based energy prediction model are established for data preprocessing and mining correspondingly. An industrial case study is presented to process energy big data and predict energy consumption parameters and production status. The performance evaluation results indicate that the proposed long short-term memory models outperform back propagation neural network, autoregressive moving average and support vector regression. Based on data preprocessing and forecasting results, the energy and resource efficiency could be improved during the whole manufacturing process for energy-intensive manufacturing industries.

Internet of Things-Based Firefighters for Disaster Case Management

In this study, Internet of Things (IoT) based firing zone monitoring and firefighter surveillance system (IoT-FFM) is developed with IEEE 802.15.6 standard and Ad hoc On-Demand Distance Vector (AODV) routing protocol. Fireground coordinates, fire temperature, and gas type in the environment are collected, and also, respiration rate, heart rate, pulse oximeter of the firefighter, and nearest firefighter number are gathered with the proposed IoT-FFM wireless communication system. The gathered data are saved in InfluxDB database with Node-RED programming tool and monitored in real-time with Grafana monitoring system at the remote control center. End-to-end delay, throughput, and energy consumption parameters are considered for performance evaluation of the IoT-FFM. The Nakagami channel model is used for a more realistic wireless environment and compared with the Free Path Loss model. Thanks to the IoT-FFM, the health conditions of firefighters can be kept under control, and information about the fire zone can be mapped, and the fire can be intervened appropriately.

Performance Analysis of Virtual Communication Between Thermal Cameras and Contact-tracing Applications for COVID-19

Late 2019 and a significant part of early 2020 have witnessed the outbreak of Coronavirus disease-19 (COVID-19) across the world. As a desperate attempt to control the virus spread, many countries are enforcing measures to restrict large concentration of people at one place and are implementing some form of contact tracing mobile application to quickly track close interactions between people. Furthermore, as businesses come out of lockdowns, they are required to record the temperature of all visitors and staffs that move in and out of their premises. Some businesses are employing medical grade contactless thermal imaging cameras for scanning temperature. Communication and sharing of information between the contact tracing application and thermal cameras would make an effective tool against COVID-19. However, there is a disconnect between the contact tracing applications and contactless thermal imaging solutions because they employ different communication stacks, platforms, data formats, and protocols. Furthermore, any kind of middleware to mediate between the cameras and the mobile applications would render the solution useless because of the induced latencies. In this paper, we are proposing to virtualize the communication between the cameras and mobile applications so that they could communicate and interoperate over a common protocol stack. We further model and simulate the proposed virtualized communication algorithm, under various topologies and configurations to comprehensively evaluate the performance, scalability, and deployment feasibility. The simulation aptly and efficiently evaluates the results for latency, energy, and bandwidth consumption parameters.