Pub Date : 2025-09-20DOI: 10.1016/j.meaene.2025.100067
Balakumar Palaniyappan, Vinopraba T.
To solve the issues in the electric power distribution network, oscillations in Electric Power Consumption (EPC) and Renewable Energy Generation (REG) must be considered. EPC and renewable energy resources (RES) are mostly used by prosumers integrated with smart grid. An incentive and dynamic pricing-based Demand Response (DR) can control the supply and demand balance. Uncertainty issues include supply and demand imbalances, EV charging, and natural REG fluctuations. This research study proposes an incentive and dynamic pricing-based DR technique for Distributed Generation and Demand Management (DGDM). This DGDM method considers the two uncertainties: demand and prosumer generation. The DGDM scheme, as proposed in this research article, has a dynamic incentive and penalty scheme. The policy applicability has been enhanced by the Bi-directional Long Short-Term Memory (B-LSTM) model’s predictive capabilities and ability to restrict the prosumers who participated in the DGDM program. The results demonstrate that the proposed DR policy benefits all parties involved, minimizes the electricity tariff and imbalance in supply and demand, and improves system stability while addressing prosumer issues. The proposed DR for prosumers to get a daily incentive of 89.4088 cents and 425.7844 cents reduce the daily electricity tariff.
{"title":"Grid-to-prosumer (G2P) interactions: Using bi-directional LSTM techniques to enhance the smart grid network through a demand response scheme","authors":"Balakumar Palaniyappan, Vinopraba T.","doi":"10.1016/j.meaene.2025.100067","DOIUrl":"10.1016/j.meaene.2025.100067","url":null,"abstract":"<div><div>To solve the issues in the electric power distribution network, oscillations in Electric Power Consumption (EPC) and Renewable Energy Generation (REG) must be considered. EPC and renewable energy resources (RES) are mostly used by prosumers integrated with smart grid. An incentive and dynamic pricing-based Demand Response (DR) can control the supply and demand balance. Uncertainty issues include supply and demand imbalances, EV charging, and natural REG fluctuations. This research study proposes an incentive and dynamic pricing-based DR technique for Distributed Generation and Demand Management (DGDM). This DGDM method considers the two uncertainties: demand and prosumer generation. The DGDM scheme, as proposed in this research article, has a dynamic incentive and penalty scheme. The policy applicability has been enhanced by the Bi-directional Long Short-Term Memory (B-LSTM) model’s predictive capabilities and ability to restrict the prosumers who participated in the DGDM program. The results demonstrate that the proposed DR policy benefits all parties involved, minimizes the electricity tariff and imbalance in supply and demand, and improves system stability while addressing prosumer issues. The proposed DR for prosumers to get a daily incentive of 89.4088 cents and 425.7844 cents reduce the daily electricity tariff.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"8 ","pages":"Article 100067"},"PeriodicalIF":0.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121234","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 : 2025-09-17DOI: 10.1016/j.meaene.2025.100066
Yuhan Liu, Quanbin Zhao, Daotong Chong
Predicting the vertical gas-liquid flow regime in multi-scale channels is essential for optimizing system performance and design in engineering fields such as heat dissipation, petrochemical processing, and nuclear energy. This paper provides a comprehensive review of the flow regime transition mechanisms and criteria for adiabatic co-current upward gas-liquid flow in multi-scale vertical channels. Firstly, the widely accepted definitions of scale classifications and the characteristics of flow regimes at different scales are summarized. On that basis, the transition mechanisms and criteria for multi-scale channels across different flow regimes are reviewed, including bubbly flow, slug flow, churn flow, and annular flow. Finally, the existing criteria are assessed with experimental data banks, and refined methods for predicting flow regimes in multi-scale channels are proposed, incorporating new boundaries for medium-to-large scales and accounting for flow regime transition types.
{"title":"Review of flow regime transition criteria for adiabatic co-current upward gas-liquid flow in vertical multi-scale channels","authors":"Yuhan Liu, Quanbin Zhao, Daotong Chong","doi":"10.1016/j.meaene.2025.100066","DOIUrl":"10.1016/j.meaene.2025.100066","url":null,"abstract":"<div><div>Predicting the vertical gas-liquid flow regime in multi-scale channels is essential for optimizing system performance and design in engineering fields such as heat dissipation, petrochemical processing, and nuclear energy. This paper provides a comprehensive review of the flow regime transition mechanisms and criteria for adiabatic co-current upward gas-liquid flow in multi-scale vertical channels. Firstly, the widely accepted definitions of scale classifications and the characteristics of flow regimes at different scales are summarized. On that basis, the transition mechanisms and criteria for multi-scale channels across different flow regimes are reviewed, including bubbly flow, slug flow, churn flow, and annular flow. Finally, the existing criteria are assessed with experimental data banks, and refined methods for predicting flow regimes in multi-scale channels are proposed, incorporating new boundaries for medium-to-large scales and accounting for flow regime transition types.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"8 ","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159075","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 : 2025-09-10DOI: 10.1016/j.meaene.2025.100065
Samuel Bates , Ziyin Chen , James Olden , Ward Storms , Delwar Hussain , Thomas Bacquart
Hydrogen fuel is foreseen as part of the energy transition towards green future. As part of hydrogen fuel quality, particulate mass fraction is essential to be monitored and maintained at a low level (<1 mg/kg). Particulate sampling has only been realised from the nozzle of hydrogen refueling stations (HRSs) in a limited occasion. Evaluating the presence of particulate within the fuel tank of a fuel cell electric vehicle (FCEV) provides an alternative approach to monitor particulate mass fraction and gain insights especially to link FCEV performance with particulate mass fraction. Within this activity, particulate mass determination from FCEV was compared with reference measurement of particulate from HRS. Two sampling systems, HYDAC and NPL low pressure particulate sampling system, measured particulate mass fraction in hydrogen fuel in almost repeatable condition before refueling of the FCEV and during the venting of the FCEV fuel tank. Both hydrogen fuel samples have particulate concentration below calculated limit of detection (LOD). This study provided the 1st agreement between the two strategies. Furthermore, realisation of these sampling methodologies reveals challenges for the standardization of particulate measurement.
{"title":"First comparison of particulate sampling and quantification from a hydrogen refueling station and fuel cell electric vehicle","authors":"Samuel Bates , Ziyin Chen , James Olden , Ward Storms , Delwar Hussain , Thomas Bacquart","doi":"10.1016/j.meaene.2025.100065","DOIUrl":"10.1016/j.meaene.2025.100065","url":null,"abstract":"<div><div>Hydrogen fuel is foreseen as part of the energy transition towards green future. As part of hydrogen fuel quality, particulate mass fraction is essential to be monitored and maintained at a low level (<1 mg/kg). Particulate sampling has only been realised from the nozzle of hydrogen refueling stations (HRSs) in a limited occasion. Evaluating the presence of particulate within the fuel tank of a fuel cell electric vehicle (FCEV) provides an alternative approach to monitor particulate mass fraction and gain insights especially to link FCEV performance with particulate mass fraction. Within this activity, particulate mass determination from FCEV was compared with reference measurement of particulate from HRS. Two sampling systems, HYDAC and NPL low pressure particulate sampling system, measured particulate mass fraction in hydrogen fuel in almost repeatable condition before refueling of the FCEV and during the venting of the FCEV fuel tank. Both hydrogen fuel samples have particulate concentration below calculated limit of detection (LOD). This study provided the 1st agreement between the two strategies. Furthermore, realisation of these sampling methodologies reveals challenges for the standardization of particulate measurement.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"8 ","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097346","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}
Currently, district heating systems are essential for the effective distribution of energy derived from renewable sources, such as geothermal and solar thermal energy, to extensive regions, including residential and urban communities. However, faults can affect the system's efficiency and lead to energy waste and significant economic losses. DHS's dependability and effectiveness are even more important as the shift to renewable energy sources accelerates, especially under the Net Zero Emissions by 2025 Scenario. Leakage can be one of the critical faults in the system, including the loss of energy, impact on the environment, challenging stability, and damage to the system equipment. Therefore, leak detection must be quick and precise to avoid system issues and costs. This review study provides a comprehensive review of leakage detection methods, highlighting their evolution, advantages, limitations, and prospects. Traditional model-based approaches are analyzed alongside data-driven techniques and advanced methods such as Unmanned Airborne Infrared Thermography (UAIT). The review also discusses challenges like network complexity, sensor limitations, and the trade-offs between cost, accuracy, and efficiency of different methods. While many studies demonstrate promising results, their reliance on simulated data rather than real-world validation remains a key constraint. The article recommends integrating multiple methods to improve system monitoring and predictive maintenance. It also highlights future directions involving proposed algorithms and models based on state-space nonlinear methods, which are well-suited for complex systems such as DHS. This approach will be helpful in achieving high accuracy and faster detection within system. A view is held regarding the potential for improving the monitoring and predictive maintenance system while considering the sustainable use of renewable energies for district heating and discussing the benefits and drawbacks of those various detection methodologies.
{"title":"A smart approach to maintenance of sustainable district heating systems: Techniques, challenges, and future directions","authors":"Parham Barzegaran Hosseini , Mousa Mohammadpourfard , Gülden Gökçen Akkurt , Mostafa Mohammadpourfard","doi":"10.1016/j.meaene.2025.100064","DOIUrl":"10.1016/j.meaene.2025.100064","url":null,"abstract":"<div><div>Currently, district heating systems are essential for the effective distribution of energy derived from renewable sources, such as geothermal and solar thermal energy, to extensive regions, including residential and urban communities. However, faults can affect the system's efficiency and lead to energy waste and significant economic losses. DHS's dependability and effectiveness are even more important as the shift to renewable energy sources accelerates, especially under the Net Zero Emissions by 2025 Scenario. Leakage can be one of the critical faults in the system, including the loss of energy, impact on the environment, challenging stability, and damage to the system equipment. Therefore, leak detection must be quick and precise to avoid system issues and costs. This review study provides a comprehensive review of leakage detection methods, highlighting their evolution, advantages, limitations, and prospects. Traditional model-based approaches are analyzed alongside data-driven techniques and advanced methods such as Unmanned Airborne Infrared Thermography (UAIT). The review also discusses challenges like network complexity, sensor limitations, and the trade-offs between cost, accuracy, and efficiency of different methods. While many studies demonstrate promising results, their reliance on simulated data rather than real-world validation remains a key constraint. The article recommends integrating multiple methods to improve system monitoring and predictive maintenance. It also highlights future directions involving proposed algorithms and models based on state-space nonlinear methods, which are well-suited for complex systems such as DHS. This approach will be helpful in achieving high accuracy and faster detection within system. A view is held regarding the potential for improving the monitoring and predictive maintenance system while considering the sustainable use of renewable energies for district heating and discussing the benefits and drawbacks of those various detection methodologies.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"8 ","pages":"Article 100064"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009941","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 : 2025-09-04DOI: 10.1016/j.meaene.2025.100063
M. Karimi , Abbas Helalizadeh , Behruz Mirzayi , M. Reza Adelzadeh
This study proposes a laboratory-scale overpressure measurement equation to avoid the core damage caused by traditional trial-and-error methods, filling the gap in existing methods. To estimate the overburden pressure before any damage to the core sample, the lithology table was obtained via laboratory data and petrophysical and geological information obtained from the field for determining the grain and fracture situation in the core samples. Afterwards, the fluids were injected into the core samples placed inside the apparatus under overburden pressure 15.6 to 121 by setting overburden pressure on various values (50–179 bar). The core samples utilized were mostly the two main groups of reservoir rocks, sand and dolomite/lime, or a combination of both with variable porosities (5–25 %), which whole data was obtained from the presented lithology table.
The experimental data was integrated with field data to obtain empirical equations to determine the value of overburden pressure in the cores with various porosities, considering the fluid and rock characteristics. An increase in the saturation of fluids (especially in water-bearing types), the densities of rocks and fluids, percentage of fractures (mostly in carbonate type), and the viscosity of fluids were along with an increment in the total overburden pressure exerted on the sample. Subsequently, this increment caused a reduction in permeability and damage to the core samples. Conversely, the increase of initial pore pressure (mostly in sandstone type) was along with a decline in overburden pressure, resulting in a significant decrease in permeability and more damage to the mostly-fragile under-pressure cores. The equations introduced here incorporated the effects of these variables and represented the behavior of porous media to raise the speed and accuracy of predicting overburden pressure in the laboratory.
{"title":"Equations of overburden pressure at laboratory: an overburden pressure measurement method in core samples","authors":"M. Karimi , Abbas Helalizadeh , Behruz Mirzayi , M. Reza Adelzadeh","doi":"10.1016/j.meaene.2025.100063","DOIUrl":"10.1016/j.meaene.2025.100063","url":null,"abstract":"<div><div>This study proposes a laboratory-scale overpressure measurement equation to avoid the core damage caused by traditional trial-and-error methods, filling the gap in existing methods. To estimate the overburden pressure before any damage to the core sample, the lithology table was obtained via laboratory data and petrophysical and geological information obtained from the field for determining the grain and fracture situation in the core samples. Afterwards, the fluids were injected into the core samples placed inside the apparatus under overburden pressure 15.6 to 121 <span><math><mrow><mo>°C</mo></mrow></math></span> by setting overburden pressure on various values (50–179 bar). The core samples utilized were mostly the two main groups of reservoir rocks, sand and dolomite/lime, or a combination of both with variable porosities (5–25 %), which whole data was obtained from the presented lithology table.</div><div>The experimental data was integrated with field data to obtain empirical equations to determine the value of overburden pressure in the cores with various porosities, considering the fluid and rock characteristics. An increase in the saturation of fluids (especially in water-bearing types), the densities of rocks and fluids, percentage of fractures (mostly in carbonate type), and the viscosity of fluids were along with an increment in the total overburden pressure exerted on the sample. Subsequently, this increment caused a reduction in permeability and damage to the core samples. Conversely, the increase of initial pore pressure (mostly in sandstone type) was along with a decline in overburden pressure, resulting in a significant decrease in permeability and more damage to the mostly-fragile under-pressure cores. The equations introduced here incorporated the effects of these variables and represented the behavior of porous media to raise the speed and accuracy of predicting overburden pressure in the laboratory.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"8 ","pages":"Article 100063"},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050612","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 : 2025-08-04DOI: 10.1016/j.meaene.2025.100060
Victor O. Hammed , Elizabeth W. Salako , Daniel Edet , Jefferson Ederhion , Babatunde Ibrahim Keshinro , Ifeanyi Augustine Uwaoma , Olaoluwa John Adeleke , Akinrotimi Odetoran , Oluyinka Joseph Adedokun , Peter F. Makinde , Yakubu Adekunle Alli
The rapid electrification of transportation has intensified the demand for high-performance lithium-ion batteries (LIBs), making advancements in materials, AI-driven optimization, and circular economy strategies crucial for the next generation of EV batteries. This review explores cutting-edge developments in LIB technology, focusing on advanced cathode and anode materials, solid-state electrolytes, and innovative battery architectures that enhance energy density, charging efficiency, and lifespan. Additionally, the integration of artificial intelligence (AI) in battery design, predictive maintenance, and manufacturing optimization is discussed, highlighting its role in improving battery performance and reliability. Furthermore, circular economy strategies, including advanced recycling technologies, second-life applications, and sustainable raw material sourcing, are examined as essential pathways toward reducing environmental impact and ensuring resource efficiency. Looking ahead, emerging trends such as solid-state batteries, AI-powered lifecycle management, and the integration of EV batteries with renewable energy systems are poised to revolutionize the energy storage landscape. This review underscores the necessity of interdisciplinary collaboration among researchers, industry leaders, and policymakers to drive sustainable innovations and achieve the next generation of LIBs for EVs.
{"title":"Next-generation lithium-ion batteries for electric vehicles: Advanced materials, AI driven performance optimization, and circular economy strategies","authors":"Victor O. Hammed , Elizabeth W. Salako , Daniel Edet , Jefferson Ederhion , Babatunde Ibrahim Keshinro , Ifeanyi Augustine Uwaoma , Olaoluwa John Adeleke , Akinrotimi Odetoran , Oluyinka Joseph Adedokun , Peter F. Makinde , Yakubu Adekunle Alli","doi":"10.1016/j.meaene.2025.100060","DOIUrl":"10.1016/j.meaene.2025.100060","url":null,"abstract":"<div><div>The rapid electrification of transportation has intensified the demand for high-performance lithium-ion batteries (LIBs), making advancements in materials, AI-driven optimization, and circular economy strategies crucial for the next generation of EV batteries. This review explores cutting-edge developments in LIB technology, focusing on advanced cathode and anode materials, solid-state electrolytes, and innovative battery architectures that enhance energy density, charging efficiency, and lifespan. Additionally, the integration of artificial intelligence (AI) in battery design, predictive maintenance, and manufacturing optimization is discussed, highlighting its role in improving battery performance and reliability. Furthermore, circular economy strategies, including advanced recycling technologies, second-life applications, and sustainable raw material sourcing, are examined as essential pathways toward reducing environmental impact and ensuring resource efficiency. Looking ahead, emerging trends such as solid-state batteries, AI-powered lifecycle management, and the integration of EV batteries with renewable energy systems are poised to revolutionize the energy storage landscape. This review underscores the necessity of interdisciplinary collaboration among researchers, industry leaders, and policymakers to drive sustainable innovations and achieve the next generation of LIBs for EVs.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767180","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 : 2025-07-31DOI: 10.1016/j.meaene.2025.100061
Qi Li , Kun Han , Shifa Cui , Yaru Shi
Temperature modeling plays an important role in the wave rotor refrigeration process control and optimization. However, considering data-driven nonlinear and time-delay modeling, how to determine the structure of the model is a challenging problem. To solve this problem, a novel sparrow optimization gated recurrent convolutional network (SGRC) deep learning method is proposed. Firstly, to exploit the advantages of convolutional neural network (CNN), the sample data is converted into grids along the time axis similar to the image input, which contains model structure and dynamic time-delay information. The multivariate and dynamic time-delay information is input into the CNN to extract the multivariate model structure features of the data. Then, after flattening the data into one-dimensional time series, input it into gated recurrent unit (GRU) layers to learn the temporal dependencies of the wave rotor refrigeration. The hyperparameters of the SGRC network are optimized using the sparrow search algorithm (SSA). Finally, simulation results based on wave rotor refrigeration industry data show that the proposed SGRC method achieves superior performance compared to traditional machine learning and other deep learning approaches, exhibiting lower RMSE and MAE values while attaining a higher R2 score. This enhancement significantly improves the generalization capability of the temperature model in the wave rotor refrigeration process.
{"title":"Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process","authors":"Qi Li , Kun Han , Shifa Cui , Yaru Shi","doi":"10.1016/j.meaene.2025.100061","DOIUrl":"10.1016/j.meaene.2025.100061","url":null,"abstract":"<div><div>Temperature modeling plays an important role in the wave rotor refrigeration process control and optimization. However, considering data-driven nonlinear and time-delay modeling, how to determine the structure of the model is a challenging problem. To solve this problem, a novel sparrow optimization gated recurrent convolutional network (SGRC) deep learning method is proposed. Firstly, to exploit the advantages of convolutional neural network (CNN), the sample data is converted into grids along the time axis similar to the image input, which contains model structure and dynamic time-delay information. The multivariate and dynamic time-delay information is input into the CNN to extract the multivariate model structure features of the data. Then, after flattening the data into one-dimensional time series, input it into gated recurrent unit (GRU) layers to learn the temporal dependencies of the wave rotor refrigeration. The hyperparameters of the SGRC network are optimized using the sparrow search algorithm (SSA). Finally, simulation results based on wave rotor refrigeration industry data show that the proposed SGRC method achieves superior performance compared to traditional machine learning and other deep learning approaches, exhibiting lower RMSE and MAE values while attaining a higher R<sup>2</sup> score. This enhancement significantly improves the generalization capability of the temperature model in the wave rotor refrigeration process.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100061"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757118","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 : 2025-07-19DOI: 10.1016/j.meaene.2025.100059
Tejas V. Kadam , Shrawan Y. Pakhare , Akshay B. Godse
Solar thermal collectors (STCs) are central to the transition toward sustainable energy systems, enabling the conversion of solar radiation into useable heat for residential, commercial, and industrial applications. This review presents a critical analysis of the performance, classification, and recent advancements in STCs, including flat-plate collectors, evacuated tube collectors, concentrating systems, and hybrid photovoltaic/thermal configurations. Emphasis is placed on the evaluation of key performance metrics—thermal efficiency, optical properties, and fluid dynamics—under standardized protocols such as ASHRAE 93–2003 and ISO 9806. The review further explores state-of-the-art enhancements involving nanofluids, selective coatings, and phase change materials (PCMs), highlighting their effectiveness and integration challenges. Special attention is given to instrumentation techniques, measurement uncertainties, and real-time monitoring systems used in performance assessment. Finally, emerging research directions, including intelligent control strategies, hybridization, and climate-adaptive designs, are discussed to guide future development of more efficient, durable, and scalable solar thermal technologies.
{"title":"“Performance analysis of solar thermal collectors: A comprehensive review”","authors":"Tejas V. Kadam , Shrawan Y. Pakhare , Akshay B. Godse","doi":"10.1016/j.meaene.2025.100059","DOIUrl":"10.1016/j.meaene.2025.100059","url":null,"abstract":"<div><div>Solar thermal collectors (STCs) are central to the transition toward sustainable energy systems, enabling the conversion of solar radiation into useable heat for residential, commercial, and industrial applications. This review presents a critical analysis of the performance, classification, and recent advancements in STCs, including flat-plate collectors, evacuated tube collectors, concentrating systems, and hybrid photovoltaic/thermal configurations. Emphasis is placed on the evaluation of key performance metrics—thermal efficiency, optical properties, and fluid dynamics—under standardized protocols such as ASHRAE 93–2003 and ISO 9806. The review further explores state-of-the-art enhancements involving nanofluids, selective coatings, and phase change materials (PCMs), highlighting their effectiveness and integration challenges. Special attention is given to instrumentation techniques, measurement uncertainties, and real-time monitoring systems used in performance assessment. Finally, emerging research directions, including intelligent control strategies, hybridization, and climate-adaptive designs, are discussed to guide future development of more efficient, durable, and scalable solar thermal technologies.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702414","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 : 2025-06-16DOI: 10.1016/j.meaene.2025.100057
Abdulhalim Musa Abubakar , Naeema Nazar , Abdulghaffaar Assayyidi Yusuf , Enyomeji Ademu Idama , Moses NyoTonglo Arowo , Aisha Maina Ma'aji , Irnis Azura Zakarya
Laboratory effluents containing dyes, particularly methylene blue (MB) impact human health negatively and the need to eliminate it from wastewaters using low-cost adsorbent such as coconut shell activated carbon (CS-AC) is paramount. Sodium hydroxide (NaOH) was used to activate the conversion of CS to AC, which was subsequently employed to adsorb MB from laboratory effluent. Preliminary optimization study led to the selection of 0.2g CS-AC dosage, pH of 6, 303K temperature, 45 min contact time, 100 mg/L initial MB concentration and a 0.5 L solution, which gives a maximum adsorption capacity () of 153.765 mg/g and 62.285 % removal efficiency (RE). A suite of isotherm (Langmuir, Freundlich, Temkin, Halsey, and Dubinin–Radushkevich) and kinetic (Pseudo-First Order, Pseudo-Second Order, Elovich, Intraparticle Diffusion, Boyd, Bhattacharya-Venkobachar, and Natarajan–Khalaf) models were applied using both graphical (GT) and nonlinear regression techniques (NRT) using LAB Fit V7.2.50 software. The Langmuir model, using NRT, gave a valid separation factor ( = 0.215) and a high maximum capacity ( = 172.12 mg/g), confirming favorable monolayer adsorption. Freundlich model yielded a realistic adsorption intensity (n = 2.7) with = 56.48 L/g, suggesting heterogeneous adsorption, although GT estimates were physically inconsistent. Temkin and Halsey models also showed good physical reliability under NRT with adsorption energies of 1.485 kJ/mol and exponent = 3.06, respectively. Dubinin–Radushkevich model supported a physisorption mechanism with a low energy (E = 0.047 kJ/mol) from GT and a consistent of 161.03 mg/g from NRT. Pseudo-Second Order is the best kinetic model fit – reflecting chemisorption at high and moderate ‘h’. FTIR analysis confirmed the presence of oxygen-containing functional groups and the absence of nitrogen functionalities, which may explain the moderate adsorption performance. SEM images revealed significant morphological changes post-adsorption, including pore blockage and surface smoothing. A general assessment show that NRT estimates proved more reliable, indicating that CS-AC exhibits a predominantly physical, favorable, and heterogeneous adsorption behavior toward MB. This study confirms CS-AC's potential as a cost-effective adsorbent for dye-contaminated laboratory wastewater and recommends further surface engineering to enhance adsorption performance. Water is in constant use in the laboratory, whose recycle or treated dis
{"title":"Development of low-cost adsorbents from coconut shell for energy-efficient dye removal from laboratory effluent discharge","authors":"Abdulhalim Musa Abubakar , Naeema Nazar , Abdulghaffaar Assayyidi Yusuf , Enyomeji Ademu Idama , Moses NyoTonglo Arowo , Aisha Maina Ma'aji , Irnis Azura Zakarya","doi":"10.1016/j.meaene.2025.100057","DOIUrl":"10.1016/j.meaene.2025.100057","url":null,"abstract":"<div><div>Laboratory effluents containing dyes, particularly methylene blue (MB) impact human health negatively and the need to eliminate it from wastewaters using low-cost adsorbent such as coconut shell activated carbon (CS-AC) is paramount. Sodium hydroxide (NaOH) was used to activate the conversion of CS to AC, which was subsequently employed to adsorb MB from laboratory effluent. Preliminary optimization study led to the selection of 0.2g CS-AC dosage, pH of 6, 303K temperature, 45 min contact time, 100 mg/L initial MB concentration and a 0.5 L solution, which gives a maximum adsorption capacity (<span><math><mrow><msub><mi>q</mi><mi>max</mi></msub></mrow></math></span>) of 153.765 mg/g and 62.285 % removal efficiency (RE). A suite of isotherm (Langmuir, Freundlich, Temkin, Halsey, and Dubinin–Radushkevich) and kinetic (Pseudo-First Order, Pseudo-Second Order, Elovich, Intraparticle Diffusion, Boyd, Bhattacharya-Venkobachar, and Natarajan–Khalaf) models were applied using both graphical (GT) and nonlinear regression techniques (NRT) using LAB Fit V7.2.50 software. The Langmuir model, using NRT, gave a valid separation factor (<span><math><mrow><msub><mi>R</mi><mi>L</mi></msub></mrow></math></span> = 0.215) and a high maximum capacity (<span><math><mrow><msub><mi>q</mi><mi>e</mi></msub></mrow></math></span> = 172.12 mg/g), confirming favorable monolayer adsorption. Freundlich model yielded a realistic adsorption intensity (n = 2.7) with <span><math><mrow><msub><mi>K</mi><mi>f</mi></msub></mrow></math></span> = 56.48 L/g, suggesting heterogeneous adsorption, although GT estimates were physically inconsistent. Temkin and Halsey models also showed good physical reliability under NRT with adsorption energies of 1.485 kJ/mol and exponent <span><math><mrow><msub><mi>n</mi><mi>h</mi></msub></mrow></math></span> = 3.06, respectively. Dubinin–Radushkevich model supported a physisorption mechanism with a low energy (E = 0.047 kJ/mol) from GT and a consistent <span><math><mrow><msub><mi>q</mi><mi>e</mi></msub></mrow></math></span> of 161.03 mg/g from NRT. Pseudo-Second Order is the best kinetic model fit – reflecting chemisorption at high <span><math><mrow><msub><mi>q</mi><mi>e</mi></msub></mrow></math></span> and moderate ‘h’. FTIR analysis confirmed the presence of oxygen-containing functional groups and the absence of nitrogen functionalities, which may explain the moderate adsorption performance. SEM images revealed significant morphological changes post-adsorption, including pore blockage and surface smoothing. A general assessment show that NRT estimates proved more reliable, indicating that CS-AC exhibits a predominantly physical, favorable, and heterogeneous adsorption behavior toward MB. This study confirms CS-AC's potential as a cost-effective adsorbent for dye-contaminated laboratory wastewater and recommends further surface engineering to enhance adsorption performance. Water is in constant use in the laboratory, whose recycle or treated dis","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338496","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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