Electrical Energy Consumption Research Articles

Sustainable production of eco-friendly roofing tile using methyl ester residue as the alternative binder – A preliminary study

Making full use of industrial by-products is an effort to achieve ‘zero waste’ and promote greener development. Methyl ester residue (MER) is a waste commonly found in oleochemical manufacturers, such as palm oil mills. Contrary to conventional cement and clay, MER was employed as an alternative binder to fabricate a novel eco-friendly roofing tile, henceforth referred to as MER-RT. Parameters tested were transverse strength, water absorption, sulfate resistance, and fire resistance. During the optimization phase, a range of 6–11% of MER was mixed with a fixed composition of fly ash and fine sand, compacted and cured at 190° C for 12–48 hours. Considering the electrical energy consumption, the optimal curing duration chosen was 24 hours as this MER-RT achieved a dry transverse strength of 4112 N whereas curing for an additional 18 hours further contributed to only 16% strength gain. The optimal MER-RT was found to contain 10% MER as it showed the highest dry transverse strength (6410 N), the highest wet transverse strength (3681 N) and the lowest water absorption percentage (2.67%), meeting the American Society for Testing and Materials (ASTM) standards. Besides exhibiting minimal corrosion under the sodium sulfate test, the optimal MER-RT was non-flammable and did not undergo structural changes when exposed to fire. The study reveals the potential of both MER and fly ash as green binders and fillers for roof tiles, despite their origins as industrial waste.

Electric vehicle energy consumption taking into account the route topology

Purpose. Determining the impact of the route topology factor on the costs of mechanical work of an electric vehicle is the main task of this work. The impact is determined by calculating the costs of mechanical work during the movement of an electric vehicle, taking into account energy recovery. The task also includes assessment of the forces acting on an electric vehicle using the example of the 2014 Nissan Leaf AZEO. Methodology. The paper uses a mathematical model that estimates the amount of mechanical work required to overcome one of the chosen routes, taking into account energy recovery. Evaluation is performed using the most common standardized cycle WLTC class 3b. Findings. The result of the research is a developed mathematical model that will allow one to effectively estimate the amount of mechanical work to overcome the given route and the possible recovery energy. The proposed method makes it possible to determine the most economical route from the starting point to the destination, taking into account the cost of mechanical energy. Originality. A description of the main components affecting the consumption of electricity is given, taking into account the full picture of the forces acting on the electric vehicle during movement. Practical value. The obtained results are of practical importance for choosing the most optimal route of the electric vehicle, which contributes to the efficient use of energy. The proposed technique can be used in practice to plan routes from the point of view of maximum energy recovery.

FORECASTING THE CONSUMPTION OF ELECTRICAL ENERGY ON THE CEB ELECTRICAL NETWORK IN LOME IN TOGO BY APPROACHES: SIMPLE LINEAR REGRESSION, RECURRENT NEURAL NETWORKS AND GENETIC ALGORITHMS

Forecasting the electrical power to be consumed requires planning production on several levels. In this work we used data from the Electric Community of Benin. Temperature, relative humidity, wind speed, normal direct irradiance, precipitation and diffuse radiation are the meteorological variables that made it possible to analyze the forecasts. The objective is to do learning with genetic algorithms, LSTM recurrent neural networks and simple linear regression after a characterization and a correlation study and then to submit the results to performance evaluation criteria. The results of the characterization made it possible to understand that certain variables are significant and influence the consumption of electrical energy. The study of the correlation gives 94% between direct normal irradiance and diffuse irradiance. Both give with the temperature 67% for one and 68% for the other. Regarding modeling, the results are bad with genetic algorithms if we take into account the correlation coefficient (R² = 28.84%), good with simple linear regression (R² = 69.08%) and very interesting for networks of recurrent neurons where we find: MAE = 0.11 MSE = 0.02 MAPE = 18.50% RMSE = 13.09% RRMSE = 18.25% and R² = 96.11%. Given these results, we deduce that short- and long-term memory recurrent neural networks (LSTM) are very well suited to predicting the electrical power consumed on the CEB electrical network.

Geothermal Heat Pump for Space Cooling and Heating in Kuwaiti Climate

Kuwait stands as one of the hottest locations globally, experiencing scorching temperatures that can soar to 50 °C during the summer months. Conversely, in the winter months of December and January, temperatures may plummet to less than 10 °C. Maintaining a comfortable temperature indoors necessitates a substantial amount of energy, particularly during the scorching summer seasons. In Kuwait, most of the electrical energy required for functions such as air conditioning and lighting is derived from fossil fuel resources, contributing to escalating air pollution and global warming. To reduce dependence on conventional energy sources for heating and cooling, this article presents a case study to explore the potential of using geothermal energy for space heating and cooling in Kuwait. The case study involves utilizing a geothermal heat pump (water-sourced heat pump) in conjunction with a vertical-borehole ground heat exchanger (VBGHE). The mentioned system is deployed to regulate the climate in a six-floor apartment block comprising a small two-bedroom apartment on each level, each with a total floor area of 57 m2. Two geothermal heat pumps, each with a cooling capacity of 2.58 kW and a heating capacity of 2.90 kW, connected to two vertical-borehole heat exchangers, were deployed for each apartment to maintain temperatures at 22 °C in summer and 26 °C in winter. The findings indicate that the estimated annual energy loads for cooling and heating for the apartment block are 42,758 kWh and 113 kWh, respectively. The corresponding electrical energy consumption amounted to 9294 kWh for space cooling and 113 kWh for space heating. The observed peak cooling load was approximately 9300 kJ/h (2.58 kW) per apartment, resulting in a power density of 45 W/m2. Moreover, the HP system achieved a 22% reduction in annual electric energy consumption compared to conventional air conditioning systems. This reduction in electric energy usage led to an annual CO2 reduction of 6.6 kg/m2.

Power consumption of electric centrifugal pumping plants under extraction of highly watered oil

Relevance. Currently, oil fields in Russia are mainly developed using the method of artificial maintenance of reservoir pressure to achieve high oil recovery. The use of artificial impact on productive formations by the method of water injection contributes to the premature irrigation of production wells. Water content of productive layers in oil fields significantly complicates the technology of oil production. With the increase in water content of the surface fluid, the power consumed by the engine increases. Minimization of energy consumption is quite relevant, as oil is produced with significant expenditures of electrical energy. It is important to investigate the dependences of the electric energy consumption on the well water cut. Aim. To study the dependence of energy consumption of submersible electric centrifugal units operated in oil production wells on water cut. Methods. Submersible electric centrifugal installations for oil production. Results. In order to study the effects of formation water cut on electrical energy specific consumption, wells with a water cut of more than 90% were selected. Using the formula, the authors have calculated the electrical energy specific consumption for oil production. The analysis of the results of the calculated data showed that in producing wells with a water cut of up to 90 %, the average specific electric power consumption for oil production is within the recommended standards. In wells with the water cut more than 90%, the average specific electric power consumption for oil production exceeds recommended specific norms of electric energy consumption during operation of oil producing wells equipped with electric centrifugal installations. Based on calculated data, the dependence of the specific electrical energy consumption for oil production on the water cut of reservoir products was plotted, and wells with a water cut of more than 90% were selected. The analysis of the graphical dependence showed that in wells with an oil water cut of more than 90%, the specific energy consumption reaches maximum values ​​(the specific energy consumption increases by 40%).

ЭНЕРГОСБЕРЕГАЮЩИЕ СИСТЕМЫ ДЛЯ ВЗВЕШИВАНИЯ БОЛЬШИХ МАСС

Energy-saving weighing systems are considered, which are an effective tool for monitoring cargo flows without significant expenditure of electrical energy. They allow you to determine the exact weight of the load, prevent overloading and optimize weight distribution. They also contribute to compliance with legal requirements in the field of traffic safety and cargo transportation, reduce losses from theft and errors during loading and unloading

Wideband Current Transducer Traceable Calibration up to 10 A and 1 MHz.

Energy efficiency is an important issue in industry, especially with the ever-increasing consumption of electrical energy. The power quality and the traceability of metering devices are essential when integrating energy metering systems for energy efficiency. This management requires an understanding of electrical current events such as pulse and transient currents. Current transducers are widely used to measure these electrical current events up to a few megahertz. Their use makes it possible to measure not only the main current flowing through the transducer, but also the bypass current that affects electrical equipment. Calibration of these sensors up to a few megahertz then becomes an essential step. Currently, most calibration methods are limited to 100 kHz frequency for a current of 10 A. This paper presents an improvement of a traceable calibration methodology for current transducers up to 10 A and 1 MHz, thus increasing, by 10 times, the current level for such high frequency applications. This calibration methodology is based on a metrological traceability chain (uninterrupted link to the International System of Units) with respect to a calculable current shunt and is currently the only traceable method for calibrating current transducers at 10 A and up to 1 MHz. The uncertainty obtained for the transimpedance ratio is less than 0.2%, which is considerably reduced with respect to the existing capabilities.

Investigation of a Building with Male and Female Residents to Achieve Equality of Thermal Sensation Associated with Rational Use of Exergy and Energy

The intensive use of air conditioning systems, primarily refrigerating, to promote thermal comfort in countries such as Brazil (a hot climate country) is changing the electrical energy consumption patterns and peak demand. Even with the increased number of appliances, we must learn to consume less energy to achieve similar outcomes with higher energy efficiency. Consequently, exergy analysis is used to evaluate the quality of these energy conversion systems. Four computational human thermal models were used to assess thermal comfort conditions: one man wearing lighter clothing, one man wearing traditional office clothing, and two women in the two types of clothing. We chose these four models since the body composition and basal metabolism rates for males and females differ. In addition, the insulation of clothes influences the temperature of the thermal environment, leading to a significantly lower percentage of people being unsatisfied, from 12.8% to values close to 5%. The outputs of these occupants are used as inputs for a computational model of the room to calculate its associated thermal loads and evaluate different temperature setpoints and their effects on thermal comfort and energy consumption. Results indicate that environmental temperatures above 24 °C and below 26 °C may lead to thermal comfort conditions, depending on the occupants’ clothing. Clothing would represent a thermal resistance of 0.7 CLO for men and women (when in the luteal phase of the menstrual cycle) and 0.8 CLO for women in the follicular phase when the environment is at 25 °C. Therefore, there is a significant reduction in the compression power of the central cooling system by about 2.2% compared to 24 °C and around 8% compared to ambient temperatures around 21 °C.

Decarbonising transportation: empirical insights into electric vehicles, carbon emissions and energy consumption

Purpose This study aims to examine the links between carbon emissions, electric vehicles, economic growth, energy use, and urbanisation in 15 countries from 2010 to 2020. Design/methodology/approach This study adopts seminal panel methods of moments quantile regression with fixed effects to trace the distributional aspect of the relationship. The reliability of methods is confirmed via fully modified ordinary least squares coefficients. Findings This study reveals that fossil fuel use, economic activity, and urbanisation negatively impact environmental quality, whereas renewable energy sources have a significant positive long-term effect on environmental quality in the selected panel of countries. Research limitations/implications The main limitation of this study is the generalisability of the findings, as the study is confined to a limited number of countries, and focuses on non-renewable and renewable energy sources. Practical implications Finally, this study proposes several policy recommendations for decision-makers and policymakers in the 15 nations to address climate change, boost sales of electric vehicles, and increase the use of renewable energy sources. Originality/value This study calls for a comprehensive transition towards green energy in the transportation sector, enhancing economic growth, fostering employment opportunities, and improving environmental quality.

Analysis of the Operating Energy Variables Involved in Mechanized Khoa Production

Significant portion of the total milk production of India has been utilized for the manufacture of the Traditional Indian Dairy Products (TIDP). Khoa based products were traditionally produced in the Indian sub-continent since ancient times. Khoa produced by the mechanized methods can overcome the demerits of traditional manufacturing methods like insufficient use of energy, poor hygiene and sanitation, non-uniform product quality, etc. In the present study, steam jacketed open type hemispherical kettle equipped with spring loaded Teflon edged scraper blade assembly was utilized for the mechanized production of khoa. A standardized mixed milk having (6.0±0.1%) fat and (9.0±0.2%) SNF, was evaporated to the different level of concentration using a batch type vacuum pan. The performance of the mechanized manufacture of khoa was evaluated at different steam pressures (P1= 98.06 kPa, P2=147.1 kPa, P3=171.61 kPa), scraper speed (R1=0.67rps), and different level of milk concentration (C1=35 %TS, C2=40 %TS, C3=45 %TS) for first stage. During the second stage, effect of operating variables on process mechanization were evaluated at different steam pressures (S1= 49.03 kPa and S2= 78.45 kPa) and scraper speed (R2=0.33rps). The primary objective of present study is to carry out thermal and electrical energy analysis associated with mechanized khoa production, which has a significant role in process optimization, scale-up of the operation and to design and fabricate the mechanized system. The overall heat transfer coefficients (U-values) of the mechanized system obtained at various processing parameters ranged from 156.87 to 234.16 W/m2K during khoa manufacturing. The treatment C1 P1 S2 gave the highest U-values for sensible and latent heating. The mean U-values of sensible heating and latent heating were increased with an increase in steam pressure during khoa making in the mechanized system. The mean value of specific steam consumption (kg steam/kg water evaporated) for the optimized operating conditions was 1.950 (kg steam/kg water evaporated) for manufacture of khoa in mechanized system. The total heat losses of mechanized system during manufacture of khoa was 17.15 to 28.02 % at different operating conditions studied. The mean values of electrical power consumption ranged from 141 to 203 Wh under different operating conditions. Considering the energy consumption data, we can narrate that existing system is energy efficient with lower electrical energy consumption and can reduce the labour cost due to mechanization.

Appraising the Feasibility of 3D Printing Construction in New Zealand Housing

The construction industry in New Zealand is significantly impacted by the importance of housing, particularly as urbanisation continues to grow in major cities. Modern construction methods, such as offsite construction and building automation, evolving into digital manufacturing and construction in the industry, have become prominent. Despite the global recognition of 3D printing technology, its adoption in the construction industry in New Zealand is still relatively limited. This study aims to examine the feasibility of 3D printing construction in response to current market challenges, innovation, and the 2050 net-zero carbon goal. Utilising Building Information Modelling (BIM) and Life Cycle Assessment (LCA) approaches, this study investigated the environmental impacts of three housing types: 3D printing (3DP), light steel framed (LSF), and timber. This study used cradle-to-cradle as the system boundary. The results indicate that the 3DP house emits 20% fewer carbon emissions than the traditional timber house and 25% less than the LSF house. Additionally, the 3DP house exhibits a 19% lower annual electric energy consumption than the timber house. Therefore, in response to the growing housing demand in New Zealand, the construction industry must innovate and embrace digital and advanced construction methods, including the adoption of 3D printing.

A Comparative Study of the Life Cycle Inventory of Thermally Sprayed WC-12Co Coatings

In this research, a life cycle inventory (LCI) is developed for tungsten carbide–cobalt (WC-Co) coatings deposited via atmospheric plasma spray (APS), high-velocity oxy-fuel (HVOF), and cold gas spray (CGS) techniques. For the APS process, a mixture of Ar/H2 was used, while the HVOF process was fueled by H2. The carrier gas for CGS was N2. This study aims to determine and quantify the inputs (consumption of inputs and materials) and outputs (emissions to air, soil, water, and waste generation) that could be used in the life cycle analysis (LCA) of these processes. The dataset produced will allow users to estimate the environmental impacts of these processes using WC-Co feedstock powder. To obtain a complete and detailed LCI, measurements of electrical energy, gas, WC-CO powder, and alumina powder consumption were performed (the use of alumina was for sandblasting). Furthermore, emissions like carbon dioxide (CO2), carbon monoxide (CO), and noise were also measured. This practice allowed us to determine the input/output process quantities. For the first time, it was possible to obtain LCI data for the APS, HVOF, and CGS deposition processes using WC-12Co as a feedstock powder, allowing access to the LCI data to a broader audience. Comparisons were made between APS, HVOF, and CGS processes in terms of consumption and emissions. It was determined that the APS process consumes more electrical energy and that its deposition efficiency is higher than the other processes, while the HVOF process consumes a large amount of H2, which makes the process costlier. CGS has comparatively low electricity consumption, high N2 consumption, and low deposition efficiency. The APS, HVOF, and CGS processes analyzed in this study do not emit CO, and CO2 emissions are negligible.

Machine learning for electric energy consumption forecasting: Application to the Paraguayan system

Abstract In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions.

An Economical Internet of Things (IoT) Driven Approach for Non-Intrusive Smart Energy Monitoring

An Economical Internet of Things (IoT) Driven Approach for Non-Intrusive Smart Energy Monitoring

Unique production strategy of Pt/C electrocatalysts via pulsed laser for hydrogen generation: Insights screening by DFT calculations

Design and optimizing the components and structure of highly-active electrocatalysts is a prevalent approach for reducing the input electrical energy consumption and concurrently H2 fuel production. An efficient and sustainable method is a water-electrolyzer for H2 generation, but still restricted by the stable electrode materials. In this study, a green chemistry approach for one-pot production of Pt/C nanospherical with controllable ratios of Pt and C was demonstrated using simple pulsed laser ablation in liquid (PLAL) process, during which Pt target ablation in diverse solvents (water, methanol, ethanol, and 1-propanol) without external carbon source and reducing/capping agents. The carbon-rich solvents are acted as both C-source and solvent, which are decomposed during the PLAL process, producing C, H, and OH ions inside the cavitation bubble which condenses as C-shells on the Pt surface. The optimal Pt/C proportions have the highest electrochemical H2-evolution in acidic media with an overpotential of 59 mV at 10 mA/cm2, Tafel slope of 41 mV/dec, and j0 of 0.686 mA/cm2. The exceptional electrocatalytic concert of optimal Pt/C was further supported by the density functional theory. Optimized C-content in Pt/C reveals good dispersion and strong interaction of C with Pt can make efficient electron transfer, structural stability, and electrochemical durability, resulting in superior electrocatalytic performance in electrolyzer devices.

THE DEVELOPMENT OF AUTOMATIC CIGARETTE SMOKE DETECTION SYSTEM USING TA12-100 AND MQ-135 SENSORS

Indonesia is ranked 3rd with the highest number of active smokers in the world after India and China. Cigarettes contain more than 4000 chemical compounds that are harmful to active smokers and passive smokers. The large number of active smokers in Indonesia also has an impact on passive smokers where passive smokers also inhale cigarette smoke. The purpose of this research is to build an automatic cigarette smoke detector to support air hygiene control, protect passive smokers, and save electricity used. The stages in designing this research include analysis of product specifications, assembly, programming, and trials. This study was designed using Arduino UNO as the microcontroller, the MQ-135 sensor as a tool to detect cigarette smoke, the TA12-100 sensor to calculate electrical power consumption, the relay as an automatic switch, and the exhaust fan as a smoke neutralizer. The results of this study are that the MQ-135 sensor can detect cigarette smoke properly with the Relay as an automatic switch that functions to turn on the exhaust fan automatically. Based on the test results on the TA12-100 sensor, the consumption of electrical energy with an automatic mechanism is more efficient than the consumption of electrical energy in a manual way, with a difference of 0.0000385 kWH and Rp. 0.05205. Future research could focus on developing a more complex system for overall air cleanliness control, rather than just focusing on cigarette smoke detection.

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

Energy Management Assessment through Case Study in Coal Handling System of Coal-Fired Power Plant

Coal Handling System (CHS) is the main system of Coal Fired Power Plant (CFPP), managing coal transportation from Vessel by Ship Unloader to coal yard through belt conveyor (BC) and Stacker/Reclaimer (S/R), then transported to Silo, known as forwarding process. CHS involves various large electric motors. The unwisdom of operating these motors would result in wasteful electrical energy consumption. To assess the energy management, United Nations Industrial Development Organization (UNIDO) provides Energy Management System (EnMS) tools based on ISO 50001 industrial sectors. This tool identifies that operational patterns and maintenance activities have a significant impact on the energy consumption. Technically using Integrated SKF Calculation, applying the scenario of direct replacement conveyor rollers aged more than 2 years by only 30%, saves 103,144.20 kWH per cycle, equal to IDR 102,807,951, rather than replacing rollers when broken only. Meanwhile, Lean Six Sigma concept in increasing cycle performance, could save a monthly 594,984 kWH, equal to IDR 593,044,189.

Carbon footprint of higher education institutions

AbstractHigher education institutions (HEIs) play a fundamental role in sustainability, promoters of innovation, science, and technology. Therefore, every day more institutions are joining the fight against global warming. One of the contributions of HEIs is the carbon footprint (CF) report, to implement policies and management systems to establish strategies to reduce polluting gas emissions from their campuses. In the present study, a systematic review was carried out for 50 reports of CF, where 94% of the studies were published from 2018 to 2022. This research compiles methodologies, scope, results, and trends in Carbon Footprint calculations and provides a procedure to evaluate CF on college campuses. This research shows that the most evaluated emission sources were the consumption of electrical energy (78%), transportation (74%), and the use of fuels (64%). In addition, the HEIs with the lowest emission factors for electricity consumption are Finland, England, and Colombia. Furthermore, establishing a specific carbon footprint guideline for universities would improve reports and allow better comparisons between HEIs.

GIS-based floating offshore wind turbine installation site selection using fuzzy analytic hierarchy process in northeast Aegean Sea

The ever-increasing consumption of electrical energy due to increasing requirements arising in parallel with the developing technology forces manufactures and users to seek new and environmentally compatible energy resources. Due to it endless energy potential and continuity, wind energy, which has been used by humanity for various purposes for centuries, continues its journey, which started with windmills that transform one form of mechanical energy into another, with offshore wind energy turbines. In this study, installation site selection, which is the first step for floating offshore wind turbine installation, is carried out. First, a Fuzzy Analytic Hierarchy Process (F-AHP) is applied to obtain the weights of criteria. Transformed alternatives and criteria in a fuzzy decision matrix through triangular fuzzy numbers are used to obtain a database via a Geographic Information System (GIS). Finally, the installation site and study area are determined. This area is located in the Northeast Aegean Sea, between Çanakkale and Gökçeada, 12 km to the northeast of Gökçeada, and 12 square kilometers. The minimum distance of the area to ship routes is 3000 m, and its minimum distance to fault lines is 2000 m. The average sea depth is 110 m, and the average wind speed is 9.26 m/s (18 knots). With this potential, enough energy can be generated for settlements on Gökçeada and the coastlines.