Pub Date : 2026-01-24DOI: 10.1016/j.meaene.2026.100088
Anshika , Vivek Goel , Sunil Kumar
Biodiesel has become more popular as a diesel substitute in recent years due to environmental concerns and the depletion of vital resources like coal and petroleum. Because of the finite supply of petroleum, concerns about global warming, and annual rises in petroleum prices, researchers are currently searching for new, affordable energy sources. The best substitute for diesel fuel is biodiesel, which is becoming more and more well-liked because of its benefits and capacity to protect the environment. The research explored the prediction of biodiesel synthesis and included transesterification experiments involving Karanja oil. In order to forecast the yield of biodiesel, an artificial neural network model was designed. Using an artificial neural network, four process parameters were evaluated during the procedure: the Oil-to-methanol molar ratio (40–50 %), proportion of the catalyst (1–2 wt.%), power (300–400 W) and duration (4–6 min). The experiment produced biodiesel with a 99.98 % yield using a catalyst (1.5 wt percent), duration (5 min), methanol (50 %), and power (300 W). An ANN was trained using the Levenberg-Marquardt algorithm. The estimations and the outcomes of the experiment were contrasted. The ANN model's R-squared value was 0.9487.
{"title":"Advanced predictive modeling of biodiesel yield using artificial neural networks for Karanja oil transesterification","authors":"Anshika , Vivek Goel , Sunil Kumar","doi":"10.1016/j.meaene.2026.100088","DOIUrl":"10.1016/j.meaene.2026.100088","url":null,"abstract":"<div><div>Biodiesel has become more popular as a diesel substitute in recent years due to environmental concerns and the depletion of vital resources like coal and petroleum. Because of the finite supply of petroleum, concerns about global warming, and annual rises in petroleum prices, researchers are currently searching for new, affordable energy sources. The best substitute for diesel fuel is biodiesel, which is becoming more and more well-liked because of its benefits and capacity to protect the environment. The research explored the prediction of biodiesel synthesis and included transesterification experiments involving Karanja oil. In order to forecast the yield of biodiesel, an artificial neural network model was designed. Using an artificial neural network, four process parameters were evaluated during the procedure: the Oil-to-methanol molar ratio (40–50 %), proportion of the catalyst (1–2 wt.%), power (300–400 W) and duration (4–6 min). The experiment produced biodiesel with a 99.98 % yield using a catalyst (1.5 wt percent), duration (5 min), methanol (50 %), and power (300 W). An ANN was trained using the Levenberg-Marquardt algorithm. The estimations and the outcomes of the experiment were contrasted. The ANN model's R-squared value was 0.9487.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial vector magnetic field sensors provide innovative tools for precision measurement, equipment diagnostics, and scientific research in the energy sector. However, determining the direction of magnetic fields remains a highly challenging task. This paper presents a compact optical fiber sensor based on a magnetic fluid-coated bare tilted fiber Bragg grating (TFBG), designed to address this challenge. The sensor offers advantages including mechanical stability, no requirement for surface metal coating, and a small footprint. Compared to traditional intensity or wavelength demodulation methods, this work employs spectral area demodulation technology, which effectively suppresses wavelength drift interference while amplifying subtle cladding mode variations. Experimental results demonstrate that the sensor can detect magnetic field intensities within the range of 0–70 Gauss, achieving an average sensitivity of in the Z-direction, and distinguish three-dimensional magnetic field orientations, with a maximum directional sensitivity of in the XOZ plane. The sensor could potentially be applied to smart grid monitoring in the power industry.
{"title":"Vector magnetic field sensing via TFBG and magnetic fluid with spectral area demodulation","authors":"Xiaobin Xue , Yuan Yang , Sijie Zhou , Qiaoling Tang , Yingbo Zhang , Changming Xia , Zhiyun Hou , Guiyao Zhou","doi":"10.1016/j.meaene.2026.100087","DOIUrl":"10.1016/j.meaene.2026.100087","url":null,"abstract":"<div><div>Spatial vector magnetic field sensors provide innovative tools for precision measurement, equipment diagnostics, and scientific research in the energy sector. However, determining the direction of magnetic fields remains a highly challenging task. This paper presents a compact optical fiber sensor based on a magnetic fluid-coated bare tilted fiber Bragg grating (TFBG), designed to address this challenge. The sensor offers advantages including mechanical stability, no requirement for surface metal coating, and a small footprint. Compared to traditional intensity or wavelength demodulation methods, this work employs spectral area demodulation technology, which effectively suppresses wavelength drift interference while amplifying subtle cladding mode variations. Experimental results demonstrate that the sensor can detect magnetic field intensities within the range of 0–70 Gauss, achieving an average sensitivity of <span><math><mrow><mn>1.528</mn><mspace></mspace><mtext>nm</mtext><mo>·</mo><mtext>dB</mtext><mo>/</mo><mtext>Gs</mtext></mrow></math></span> in the Z-direction, and distinguish three-dimensional magnetic field orientations, with a maximum directional sensitivity of <span><math><mrow><mn>1.644</mn><mspace></mspace><mtext>nm</mtext><mo>·</mo><mtext>dB</mtext><mo>/</mo><mtext>degree</mtext></mrow></math></span> in the XOZ plane. The sensor could potentially be applied to smart grid monitoring in the power industry.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Saudi Arabia's hyper-arid Madinah region, where summer temperatures exceed 40 °C, air conditioning (AC) systems consume substantial energy. This study evaluates a 3.52 kW conventional fixed-speed AC system during June–August 2024 to quantify its performance and propose energy-efficient alternatives. Using high-frequency measurements and uncertainty analysis, the system achieved a cooling capacity (Qevap) of 2003.1–2580.4 W, power consumption of 840.7–1096.3 W, and a coefficient of performance (COP) of 2.23–2.39, with uncertainties of 4.04–5.06 %. These results reveal inefficiencies compared to variable-speed systems, with a COP ranging from 3.0 to 4.5. Suboptimal user practices increased power consumption to 3.4–6.6 kW, with annual maintenance costs of SAR 1625. Advanced technologies could reduce energy use by 20–80 %, supporting Saudi Arabia's sustainability goals under Vision 2030. The novelty of this work lies in its integrative, multifaceted approach, which establishes the first high-resolution performance baseline for Madinah and provides a holistic framework—encompassing technical, economic, environmental, and behavioral factors—to guide cooling optimization policy and technology adoption in extreme climates.
{"title":"Energy efficiency and cooling performance of A/C systems in Saudi Arabia's hot climate: A case study","authors":"Emad Alrwishdi , Abdulrahman AlKassem , Saleh Al Ahmadi , Abdussamad Sadis , Azeddine Draou , Mohamed Ouzzane , Mahmoud Bady","doi":"10.1016/j.meaene.2026.100086","DOIUrl":"10.1016/j.meaene.2026.100086","url":null,"abstract":"<div><div>In Saudi Arabia's hyper-arid Madinah region, where summer temperatures exceed 40 °C, air conditioning (AC) systems consume substantial energy. This study evaluates a 3.52 kW conventional fixed-speed AC system during June–August 2024 to quantify its performance and propose energy-efficient alternatives. Using high-frequency measurements and uncertainty analysis, the system achieved a cooling capacity (Q<sub>evap</sub>) of 2003.1–2580.4 W, power consumption of 840.7–1096.3 W, and a coefficient of performance (COP) of 2.23–2.39, with uncertainties of 4.04–5.06 %. These results reveal inefficiencies compared to variable-speed systems, with a COP ranging from 3.0 to 4.5. Suboptimal user practices increased power consumption to 3.4–6.6 kW, with annual maintenance costs of SAR 1625. Advanced technologies could reduce energy use by 20–80 %, supporting Saudi Arabia's sustainability goals under Vision 2030. The novelty of this work lies in its integrative, multifaceted approach, which establishes the first high-resolution performance baseline for Madinah and provides a holistic framework—encompassing technical, economic, environmental, and behavioral factors—to guide cooling optimization policy and technology adoption in extreme climates.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.meaene.2026.100085
Nidhin Thekkedath Madhu , Martin Adendorff , Kristina Mabic , Esin Iplik , Sven Eckart , Hartmut Krause
Decarbonising high-temperature industrial furnaces requires efficient and low-emission combustion strategies. Hydrogen and oxygen enhanced combustion (OEC) are promising alternatives to conventional air-fuel systems, but their combined impact on heat transfer, efficiency, and NOx emissions under practical operating conditions remains underexplored. This study explores the combustion behaviour of a commercial 200 kW burner operating with hydrogen, natural gas, and their blend under varying oxidizer oxygen concentrations, ranging from air-fuel combustion (21 % O2) to pure oxyfuel combustion (100 % O2). Conducted at pilot-scale, the research aims to understand how fuel composition and oxygen enrichment influence NOx emissions, heat transfer, wall temperature distribution, and flue gas energy losses. The results reveal that oxygen enrichment plays a dominant role in shaping combustion performance, while the choice of fuel (whether hydrogen, natural gas, or a blend) has a comparatively minor effect. Oxygen enrichment significantly improved heat transfer and reduced flue gas losses, resulting in thermal efficiency increase from ∼45 % in air-fuel to ∼80 % in oxyfuel combustion. Burner configuration such as delayed combustion and flameless combustion strongly influenced temperature uniformity and NOx emissions, where flameless configuration resulted in enhanced mixing, reduced thermal stratification and lower NOx compared to simple delayed combustion. Under delayed and flameless oxyfuel conditions, NOx emissions dropped below 2 mg/MJ for both fuels. With this study, a reduction of ∼90 % in NOx emission while moving from air-fuel to oxyfuel condition was observed for natural gas and hydrogen. Interestingly, at flameless combustion operation, hydrogen showed lower NOx emission than natural gas. Constant-temperature studies confirmed that nitrogen availability, rather than flame temperature, dominated NOx formation under flameless conditions. These findings highlight the potential of oxyfuel and OEC to deliver cleaner and more energy-efficient operation in industrial furnaces, regardless of fuel composition. The insights gained are particularly relevant for industries transitioning toward hydrogen-based energy systems and seeking to meet decarbonisation, NOx emission and efficiency targets.
{"title":"Oxygen enrichment studies in hydrogen-natural gas burner: A pilot-scale study on emissions and thermal performance","authors":"Nidhin Thekkedath Madhu , Martin Adendorff , Kristina Mabic , Esin Iplik , Sven Eckart , Hartmut Krause","doi":"10.1016/j.meaene.2026.100085","DOIUrl":"10.1016/j.meaene.2026.100085","url":null,"abstract":"<div><div>Decarbonising high-temperature industrial furnaces requires efficient and low-emission combustion strategies. Hydrogen and oxygen enhanced combustion (OEC) are promising alternatives to conventional air-fuel systems, but their combined impact on heat transfer, efficiency, and NOx emissions under practical operating conditions remains underexplored. This study explores the combustion behaviour of a commercial 200 kW burner operating with hydrogen, natural gas, and their blend under varying oxidizer oxygen concentrations, ranging from air-fuel combustion (21 % O<sub>2</sub>) to pure oxyfuel combustion (100 % O<sub>2</sub>). Conducted at pilot-scale, the research aims to understand how fuel composition and oxygen enrichment influence NOx emissions, heat transfer, wall temperature distribution, and flue gas energy losses. The results reveal that oxygen enrichment plays a dominant role in shaping combustion performance, while the choice of fuel (whether hydrogen, natural gas, or a blend) has a comparatively minor effect. Oxygen enrichment significantly improved heat transfer and reduced flue gas losses, resulting in thermal efficiency increase from ∼45 % in air-fuel to ∼80 % in oxyfuel combustion. Burner configuration such as delayed combustion and flameless combustion strongly influenced temperature uniformity and NOx emissions, where flameless configuration resulted in enhanced mixing, reduced thermal stratification and lower NOx compared to simple delayed combustion. Under delayed and flameless oxyfuel conditions, NOx emissions dropped below 2 mg/MJ for both fuels. With this study, a reduction of ∼90 % in NOx emission while moving from air-fuel to oxyfuel condition was observed for natural gas and hydrogen. Interestingly, at flameless combustion operation, hydrogen showed lower NOx emission than natural gas. Constant-temperature studies confirmed that nitrogen availability, rather than flame temperature, dominated NOx formation under flameless conditions. These findings highlight the potential of oxyfuel and OEC to deliver cleaner and more energy-efficient operation in industrial furnaces, regardless of fuel composition. The insights gained are particularly relevant for industries transitioning toward hydrogen-based energy systems and seeking to meet decarbonisation, NOx emission and efficiency targets.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.meaene.2025.100084
Kumaran Kannaiyan
To achieve the goal of sustainable aviation, the aviation industry is considering blends of conventional and alternative jet fuels to achieve clean combustion. To this end, it is essential to understand the atomization characteristics of jet fuel blends better to gain additional insights into their ignition and combustion phenomena. Thus, this study explores the atomization characteristics of alternative-conventional jet fuel blends, i.e., blends of jet fuel derived from natural gas (Gas-To-Liquid, GTL) with Jet A-1 fuels at varying proportions (0, 50 %, 75%, and 100%) under non-reacting conditions at elevated ambient pressures (100 and 500 kPa) and ambient temperatures (300 and 350 K). Towards this goal, a simplex atomizer is used to experimentally investigate the macroscopic atomization characteristics, such as spray cone angle, sheet dynamics and breakup distance, and axial velocity variation at two pressure differentials across the atomizer (300 and 900 kPa). The results demonstrate that the influence of elevated ambient temperature has a stronger correlation with the near-nozzle atomization characteristics. For the type of nozzle studied, the far-field cone angle of jet fuel blends decreased from 70 ° to 60 ° with the increase in ambient gas pressure from 100 to 500 kPa. Under the conditions studied, the blend of 50% GTL-50%Jet A-1 exhibited an earlier onset of liquid-sheet instability, characterized using the second-order statistical moment, when compared to GTL and Jet A-1 fuels. Furthermore, the 50% GTL-50%Jet A-1 blend exhibited shorter liquid-sheet breakup distance than that of 75% GTL-25%Jet A-1 blend. The results presented here will assist in gaining additional insights into their ignition and combustion performance at relevant conditions.
{"title":"Near-nozzle atomization characteristics of GTL-Jet A-1 fuel blends at combustor relevant conditions using shadowgraph measurements","authors":"Kumaran Kannaiyan","doi":"10.1016/j.meaene.2025.100084","DOIUrl":"10.1016/j.meaene.2025.100084","url":null,"abstract":"<div><div>To achieve the goal of sustainable aviation, the aviation industry is considering blends of conventional and alternative jet fuels to achieve clean combustion. To this end, it is essential to understand the atomization characteristics of jet fuel blends better to gain additional insights into their ignition and combustion phenomena. Thus, this study explores the atomization characteristics of alternative-conventional jet fuel blends, <em>i.e.,</em> blends of jet fuel derived from natural gas (Gas-To-Liquid, GTL) with Jet A-1 fuels at varying proportions (0, 50 %, 75%, and 100%) under non-reacting conditions at elevated ambient pressures (100 and 500 kPa) and ambient temperatures (300 and 350 K). Towards this goal, a simplex atomizer is used to experimentally investigate the macroscopic atomization characteristics, such as spray cone angle, sheet dynamics and breakup distance, and axial velocity variation at two pressure differentials across the atomizer (300 and 900 kPa). The results demonstrate that the influence of elevated ambient temperature has a stronger correlation with the near-nozzle atomization characteristics. For the type of nozzle studied, the far-field cone angle of jet fuel blends decreased from 70 ° to 60 ° with the increase in ambient gas pressure from 100 to 500 kPa. Under the conditions studied, the blend of 50% GTL-50%Jet A-1 exhibited an earlier onset of liquid-sheet instability, characterized using the second-order statistical moment, when compared to GTL and Jet A-1 fuels. Furthermore, the 50% GTL-50%Jet A-1 blend exhibited shorter liquid-sheet breakup distance than that of 75% GTL-25%Jet A-1 blend. The results presented here will assist in gaining additional insights into their ignition and combustion performance at relevant conditions.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydrogen combustion is seen as a promising carbon-free alternative for generating process heat in industrial processes requiring high temperatures and large amounts of energy, such as metal melting and reheating. The transition from natural gas to hydrogen in high-temperature furnaces raises concerns about heat transfer efficiency due to the altered flue gas composition. This study examines the influence of hydrogen enrichment of natural gas on thermal performance and emissions in a semi-industrial box furnace equipped with a flameless burner operating in oxyfuel and air-fuel modes at 200 kW. The burner is primarily designed for the aluminum industry, where oxyfuel mode is used for rapid melting and air-fuel mode during holding periods. Seven different NG/H2 fuel cases, ranging from natural gas to pure hydrogen, in both burner configurations, are compared experimentally in terms of temperature and energy distribution, gas emissivity, radiative heat flux, and NOx emissions. Additional heating trials on aluminum with hydrogen and natural gas are conducted to quantify the radiative and convective contributions using a combined experimental and numerical approach. Results show that under oxyfuel conditions, hydrogen addition has minimal impact on furnace temperature, flue gas losses, and radiative heat flux. In contrast, air-fuel operation exhibited continuous temperature rise and radiative heat flux, along with reduced flue gas losses. NOx emissions remained below 5 mg/MJ for oxyfuel and 12 mg/MJ for air-fuel combustion, with pure hydrogen achieving lower values than natural gas due to the elimination of prompt NOx. Aluminum heating trials revealed a 6 % improvement in heat flux with hydrogen under oxyfuel and 19 % under air-fuel conditions. These findings confirm that hydrogen can be effectively integrated into flameless combustion without compromising combustion performance, with oxyfuel technology offering greater potential for efficiency gains and NOx mitigation.
{"title":"Heat transfer and emission characteristics of hydrogen-enriched natural gas in flameless air/oxyfuel combustion in a semi-industrial furnace","authors":"Kristina Mabic , Martin Adendorff , Nidhin Thekkedath Madhu , Esin Iplik , Tomas Ekman , Ioanna Aslanidou , Konstantinos Kyprianidis","doi":"10.1016/j.meaene.2025.100083","DOIUrl":"10.1016/j.meaene.2025.100083","url":null,"abstract":"<div><div>Hydrogen combustion is seen as a promising carbon-free alternative for generating process heat in industrial processes requiring high temperatures and large amounts of energy, such as metal melting and reheating. The transition from natural gas to hydrogen in high-temperature furnaces raises concerns about heat transfer efficiency due to the altered flue gas composition. This study examines the influence of hydrogen enrichment of natural gas on thermal performance and emissions in a semi-industrial box furnace equipped with a flameless burner operating in oxyfuel and air-fuel modes at 200 kW. The burner is primarily designed for the aluminum industry, where oxyfuel mode is used for rapid melting and air-fuel mode during holding periods. Seven different NG/H<sub>2</sub> fuel cases, ranging from natural gas to pure hydrogen, in both burner configurations, are compared experimentally in terms of temperature and energy distribution, gas emissivity, radiative heat flux, and NOx emissions. Additional heating trials on aluminum with hydrogen and natural gas are conducted to quantify the radiative and convective contributions using a combined experimental and numerical approach. Results show that under oxyfuel conditions, hydrogen addition has minimal impact on furnace temperature, flue gas losses, and radiative heat flux. In contrast, air-fuel operation exhibited continuous temperature rise and radiative heat flux, along with reduced flue gas losses. NOx emissions remained below 5 mg/MJ for oxyfuel and 12 mg/MJ for air-fuel combustion, with pure hydrogen achieving lower values than natural gas due to the elimination of prompt NOx. Aluminum heating trials revealed a 6 % improvement in heat flux with hydrogen under oxyfuel and 19 % under air-fuel conditions. These findings confirm that hydrogen can be effectively integrated into flameless combustion without compromising combustion performance, with oxyfuel technology offering greater potential for efficiency gains and NOx mitigation.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.meaene.2025.100081
Armin Ahmadi Bouzandani , Behrooz Taheri , Seyed Amir Hosseini
Due to the increasing integration of new energy sources and the critical need for power grid stability, accurate fault detection and classification in power systems has become paramount. Therefore, this paper presents a new, optimized framework for fault detection in Grid-Forming Inverter-Based Resource (GFM-IBR) power systems. This framework is based on frequency feature extraction using the Fast Fourier Transform (FFT) and a Support Vector Machine (SVM) model. First, we sample the voltage and current signals from the IBR side. Then, the angle and phase values of these signals are extracted using FFT. The average angle and phase values obtained within the 10-ms period after a fault in each phase are then designated as the main features. The distinctive aspect of this study is its dual optimization approach. This involves both adjusting the parameters of the SVM model and optimally selecting features using several evolutionary and metaheuristic algorithms, specifically HHO, PSO, GA, GWO, WOA, GOA, DO, and AEO. The method presented in this paper was tested on a power system connected to a GFM-IBR, simulated in PSCAD software. The data generated in PSCAD was then transferred to a Google Colab environment for feature extraction and SVM model training.
{"title":"Optimized SVM and feature selection for fault detection and classification in GFM-IBR system","authors":"Armin Ahmadi Bouzandani , Behrooz Taheri , Seyed Amir Hosseini","doi":"10.1016/j.meaene.2025.100081","DOIUrl":"10.1016/j.meaene.2025.100081","url":null,"abstract":"<div><div>Due to the increasing integration of new energy sources and the critical need for power grid stability, accurate fault detection and classification in power systems has become paramount. Therefore, this paper presents a new, optimized framework for fault detection in Grid-Forming Inverter-Based Resource (GFM-IBR) power systems. This framework is based on frequency feature extraction using the Fast Fourier Transform (FFT) and a Support Vector Machine (SVM) model. First, we sample the voltage and current signals from the IBR side. Then, the angle and phase values of these signals are extracted using FFT. The average angle and phase values obtained within the 10-ms period after a fault in each phase are then designated as the main features. The distinctive aspect of this study is its dual optimization approach. This involves both adjusting the parameters of the SVM model and optimally selecting features using several evolutionary and metaheuristic algorithms, specifically HHO, PSO, GA, GWO, WOA, GOA, DO, and AEO. The method presented in this paper was tested on a power system connected to a GFM-IBR, simulated in PSCAD software. The data generated in PSCAD was then transferred to a Google Colab environment for feature extraction and SVM model training.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1016/j.meaene.2025.100080
Edwin Garabitos-Lara , José Miguel Mateo-Beltré , Jesús Manuel Silva-García
In many developing countries, low-voltage autotransformers are commonly used in residential settings to compensate for persistent voltage irregularities in the electrical distribution network. Despite their widespread use, few studies have systematically evaluated their performance and efficiency under typical operating conditions. This study presents the design, construction, and experimental evaluation of a low-voltage autotransformer rated at 1580 VA. The design equations were derived from Faraday's Law, and the equivalent circuit parameters were obtained through open-circuit and short-circuit tests. The efficiency was measured using a power network analyzer under resistive and non-resistive loads and compared with values calculated analytically from the equivalent circuit. The study also incorporated uncertainty analysis in both experimental and analytical procedures. Results showed that the autotransformer reached an efficiency of 98.0 % under nominal load and exceeded 90.0 % efficiency at just 10.7 % of the demand coefficient. Furthermore, the all-day efficiency remained stable between 96.5 % and 96.8 % across real residential demand profiles, confirming consistent energy performance under variable load conditions. The differences between measured and calculated efficiencies were below 1 percentage point in all cases, and validation was confirmed using uncertainty propagation, mean absolute error (MAE), and root mean square error (RMSE). These findings reinforce the reliability of the analytical model and highlight the high efficiency of these devices as voltage-regulating elements when properly designed.
{"title":"Evaluation of the efficiency of a low voltage autotransformer","authors":"Edwin Garabitos-Lara , José Miguel Mateo-Beltré , Jesús Manuel Silva-García","doi":"10.1016/j.meaene.2025.100080","DOIUrl":"10.1016/j.meaene.2025.100080","url":null,"abstract":"<div><div>In many developing countries, low-voltage autotransformers are commonly used in residential settings to compensate for persistent voltage irregularities in the electrical distribution network. Despite their widespread use, few studies have systematically evaluated their performance and efficiency under typical operating conditions. This study presents the design, construction, and experimental evaluation of a low-voltage autotransformer rated at 1580 VA. The design equations were derived from Faraday's Law, and the equivalent circuit parameters were obtained through open-circuit and short-circuit tests. The efficiency was measured using a power network analyzer under resistive and non-resistive loads and compared with values calculated analytically from the equivalent circuit. The study also incorporated uncertainty analysis in both experimental and analytical procedures. Results showed that the autotransformer reached an efficiency of 98.0 % under nominal load and exceeded 90.0 % efficiency at just 10.7 % of the demand coefficient. Furthermore, the all-day efficiency remained stable between 96.5 % and 96.8 % across real residential demand profiles, confirming consistent energy performance under variable load conditions. The differences between measured and calculated efficiencies were below 1 percentage point in all cases, and validation was confirmed using uncertainty propagation, mean absolute error (MAE), and root mean square error (RMSE). These findings reinforce the reliability of the analytical model and highlight the high efficiency of these devices as voltage-regulating elements when properly designed.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100080"},"PeriodicalIF":0.0,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1016/j.meaene.2025.100078
Yi Duan, Guang Chen, Xiangjun Bao, Lu Zhang, Xiaojing Yang
As a key high-energy-consuming equipment in steel production, the energy efficiency optimization of steel rolling reheating furnaces (SRRF) has long been constrained by three core contradictions: "energy consumption-temperature matching, rolling rhythm-quality coordination, and billet shape-energy efficiency differentiation". In this study, a "general-subdivision" two-layer criterion system is established. The general criterion (NE) quantifies global energy consumption redundancy by means of the ratio of actual energy consumption to theoretical heat demand. The shape-corrected criterion (NE, M), meanwhile, incorporates a correction term for the width-to-thickness ratio (W/H) to distinguish between shape-induced inherent losses and operationally controllable losses. Verification using 1515 sets of industrial data demonstrates that NE can effectively assess the energy consumption-temperature matching degree for SRRF—exhibiting a negative correlation coefficient of −0.61 with temperature difference, while NE, M enables accurate identification of the heat transfer characteristics of wide billets, with a negative correlation coefficient of −0.45 with W/H, confirming that wider billets exhibit lower shape-corrected energy redundancy. Analysis of the high-gradient region reveals that although wide billets in SRRF experience higher energy consumption due to extended heat conduction paths, their thermal efficiency outperforms that of narrow billets; optimization in this regard can be achieved through regulating rolling rhythm and furnace temperature. This system serves as a quantitative tool for SRRF to transition from empirical regulation to data-driven optimization. Theoretically, it breaks through the single-factor limitation of traditional methods; practically, it provides support for energy efficiency benchmarking and dynamic regulation of SRRF.
{"title":"A dimensionless criterion system for energy efficiency evaluation in steel rolling reheating furnaces","authors":"Yi Duan, Guang Chen, Xiangjun Bao, Lu Zhang, Xiaojing Yang","doi":"10.1016/j.meaene.2025.100078","DOIUrl":"10.1016/j.meaene.2025.100078","url":null,"abstract":"<div><div>As a key high-energy-consuming equipment in steel production, the energy efficiency optimization of steel rolling reheating furnaces (SRRF) has long been constrained by three core contradictions: \"energy consumption-temperature matching, rolling rhythm-quality coordination, and billet shape-energy efficiency differentiation\". In this study, a \"general-subdivision\" two-layer criterion system is established. The general criterion (<em>N</em><sub>E</sub>) quantifies global energy consumption redundancy by means of the ratio of actual energy consumption to theoretical heat demand. The shape-corrected criterion (<em>N</em><sub>E, M</sub>), meanwhile, incorporates a correction term for the width-to-thickness ratio (<em>W</em>/<em>H</em>) to distinguish between shape-induced inherent losses and operationally controllable losses. Verification using 1515 sets of industrial data demonstrates that <em>N</em><sub>E</sub> can effectively assess the energy consumption-temperature matching degree for SRRF—exhibiting a negative correlation coefficient of −0.61 with temperature difference, while <em>N</em><sub>E, M</sub> enables accurate identification of the heat transfer characteristics of wide billets, with a negative correlation coefficient of −0.45 with <em>W</em>/<em>H</em>, confirming that wider billets exhibit lower shape-corrected energy redundancy. Analysis of the high-gradient region reveals that although wide billets in SRRF experience higher energy consumption due to extended heat conduction paths, their thermal efficiency outperforms that of narrow billets; optimization in this regard can be achieved through regulating rolling rhythm and furnace temperature. This system serves as a quantitative tool for SRRF to transition from empirical regulation to data-driven optimization. Theoretically, it breaks through the single-factor limitation of traditional methods; practically, it provides support for energy efficiency benchmarking and dynamic regulation of SRRF.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.meaene.2025.100079
Rajagopal M , Shashank R , Shreeshanth R
High-rise buildings consume significant energy due to intensive HVAC and elevator operations. This study proposes an adaptive energy management framework that captures regenerative braking heat from elevators to charge Phase-Change Thermal Batteries (PCTBs), which are subsequently used to assist HVAC operations. A hybrid co-simulation platform integrating TRNSYS, MATLAB/Simulink, and Python (TensorFlow) was developed to couple building thermal dynamics, elevator regenerative heat recovery, and AI-based HVAC scheduling. The proposed reinforcement learning (RL)–driven scheduler dynamically coordinates HVAC power modulation, PCTB charging/discharging cycles, and occupancy-based thermal demand in response to real-time weather and elevator activity. Simulation results demonstrate that the adaptive AI scheduler reduces HVAC energy consumption by up to 18 % and peak load by 12 %, while maintaining indoor comfort within ASHRAE-55 standards. Regenerative heat utilization efficiency exceeded 75 %, confirming effective capture and reuse of elevator braking energy. Extended seven-day simulations further validated system robustness and consistent performance under varying occupancy and climatic conditions. Economic assessment indicates a payback period of 5–7 years, emphasizing the financial viability of the proposed integration. By uniting regenerative energy recovery, latent thermal storage, and adaptive AI control, this research establishes a scalable framework for intelligent HVAC operation in smart buildings. The findings highlight substantial potential for achieving energy circularity, reduced carbon emissions, and enhanced sustainability in high-rise urban environments.
{"title":"ADAPTIVE AI scheduling of building HVAC to charge phase change thermal batteries with elevator regenerative braking heat","authors":"Rajagopal M , Shashank R , Shreeshanth R","doi":"10.1016/j.meaene.2025.100079","DOIUrl":"10.1016/j.meaene.2025.100079","url":null,"abstract":"<div><div>High-rise buildings consume significant energy due to intensive HVAC and elevator operations. This study proposes an adaptive energy management framework that captures regenerative braking heat from elevators to charge Phase-Change Thermal Batteries (PCTBs), which are subsequently used to assist HVAC operations. A hybrid co-simulation platform integrating TRNSYS, MATLAB/Simulink, and Python (TensorFlow) was developed to couple building thermal dynamics, elevator regenerative heat recovery, and AI-based HVAC scheduling. The proposed reinforcement learning (RL)–driven scheduler dynamically coordinates HVAC power modulation, PCTB charging/discharging cycles, and occupancy-based thermal demand in response to real-time weather and elevator activity. Simulation results demonstrate that the adaptive AI scheduler reduces HVAC energy consumption by up to 18 % and peak load by 12 %, while maintaining indoor comfort within ASHRAE-55 standards. Regenerative heat utilization efficiency exceeded 75 %, confirming effective capture and reuse of elevator braking energy. Extended seven-day simulations further validated system robustness and consistent performance under varying occupancy and climatic conditions. Economic assessment indicates a payback period of 5–7 years, emphasizing the financial viability of the proposed integration. By uniting regenerative energy recovery, latent thermal storage, and adaptive AI control, this research establishes a scalable framework for intelligent HVAC operation in smart buildings. The findings highlight substantial potential for achieving energy circularity, reduced carbon emissions, and enhanced sustainability in high-rise urban environments.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"9 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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