Mingjie Cai, Mingmin He, Leichuan Tan, Dan Mao, Jinchao Xiao
The safety problem of large-size drilling tools in large-size boreholes has become increasingly prominent with the exploration and development of deep and ultradeep wells. This study analyzes the causes of large-size drilling tool failures from the engineering point of view via statistical analysis, experimental material test, and vibration and bending analyses. Results show that the violent downhole vibration changes the drilling tool's mechanical properties. These changes results in an uneven distribution of hardness and reduced impact work, finally leading to the initiation of fatigue cracks at stress concentration points. Drilling tool bending is closely related to drilling parameters and BHA configuration. Unreasonable BHA configuration and drilling parameters increase BHA bending and accelerate fatigue failure. Once a crack is generated, the corrosive ions in water-based drilling fluids invade the microcrack, causing the corrosion of the drilling tool material. As a result, the strength is reduced, and the fracture is aggravated. Therefore, measures for preventing the failure of large-size drilling tools are proposed. We hope that the results of this work can provide useful guidance for drilling engineers.
{"title":"Failure analysis of large-size drilling tools in the oil and gas industry","authors":"Mingjie Cai, Mingmin He, Leichuan Tan, Dan Mao, Jinchao Xiao","doi":"10.1115/1.4065250","DOIUrl":"https://doi.org/10.1115/1.4065250","url":null,"abstract":"\u0000 The safety problem of large-size drilling tools in large-size boreholes has become increasingly prominent with the exploration and development of deep and ultradeep wells. This study analyzes the causes of large-size drilling tool failures from the engineering point of view via statistical analysis, experimental material test, and vibration and bending analyses. Results show that the violent downhole vibration changes the drilling tool's mechanical properties. These changes results in an uneven distribution of hardness and reduced impact work, finally leading to the initiation of fatigue cracks at stress concentration points. Drilling tool bending is closely related to drilling parameters and BHA configuration. Unreasonable BHA configuration and drilling parameters increase BHA bending and accelerate fatigue failure. Once a crack is generated, the corrosive ions in water-based drilling fluids invade the microcrack, causing the corrosion of the drilling tool material. As a result, the strength is reduced, and the fracture is aggravated. Therefore, measures for preventing the failure of large-size drilling tools are proposed. We hope that the results of this work can provide useful guidance for drilling engineers.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"16 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747779","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}
M. Nemitallah, M. Aliyu, Mohammed Hamdy, Mohamed A. Habib
The effects of hydrogen fraction (HF: volumetric fraction of H2 in the fuel mixture of CH4+H2) from 0 to 100% - by vol, on thermal and environmental performance of a 207-MW industrial water tube boiler are investigated numerically at fixed excess air factor, λ=1.15. This study aims to determine the hardware modifications required for boilers to be retrofitted for pure hydrogen operation and investigates how NOx emissions are affected by hydrogen enrichment. The results showed insignificant increases in maximum combustion temperature with increasing the HF, through the distributions of temperature profiles are distinct. In reference to the basic methane combustion, H2 flames resulted in positive temperature rise in the vicinity of the burner. Increasing the HF from 0% to 2% resulted in higher average thermal NOx emissions at the boiler exit section from 37 up to 1284 ppm, then it decreased to 1136 ppm at HF=30%, and later it leveled up to 1474 ppm at HF=100%. The spots for higher differences in NO formation compared to the reference case are shifted downstream at higher HFs. The effect of hydrogen enrichment on CO2 and H2O as radiation sources, as well as the volumetric absorption radiation of the furnace wall and the heat flux at furnace surfaces, have all been presented in relation to the effect of hydrogen addition on boiler performance.
{"title":"RETROFITING NATURAL-GAS FIRED BOILER FOR HYDROGEN COMBUSTION: OPERATIONAL PERFORMANCE AND NOX EMISSIONS","authors":"M. Nemitallah, M. Aliyu, Mohammed Hamdy, Mohamed A. Habib","doi":"10.1115/1.4065205","DOIUrl":"https://doi.org/10.1115/1.4065205","url":null,"abstract":"\u0000 The effects of hydrogen fraction (HF: volumetric fraction of H2 in the fuel mixture of CH4+H2) from 0 to 100% - by vol, on thermal and environmental performance of a 207-MW industrial water tube boiler are investigated numerically at fixed excess air factor, λ=1.15. This study aims to determine the hardware modifications required for boilers to be retrofitted for pure hydrogen operation and investigates how NOx emissions are affected by hydrogen enrichment. The results showed insignificant increases in maximum combustion temperature with increasing the HF, through the distributions of temperature profiles are distinct. In reference to the basic methane combustion, H2 flames resulted in positive temperature rise in the vicinity of the burner. Increasing the HF from 0% to 2% resulted in higher average thermal NOx emissions at the boiler exit section from 37 up to 1284 ppm, then it decreased to 1136 ppm at HF=30%, and later it leveled up to 1474 ppm at HF=100%. The spots for higher differences in NO formation compared to the reference case are shifted downstream at higher HFs. The effect of hydrogen enrichment on CO2 and H2O as radiation sources, as well as the volumetric absorption radiation of the furnace wall and the heat flux at furnace surfaces, have all been presented in relation to the effect of hydrogen addition on boiler performance.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"90 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371115","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}
Yuchao Yan, Tansu Shang, Lingmin Li, Zhen-tao Liu, Jinlong Liu
In the quest for decarbonizing internal combustion engines, ammonia (NH3) is recognized as a viable alternative fuel due to its zero-carbon emission profile, positioning it as a potential substitute for conventional petroleum fuels. However, the suboptimal combustion characteristics of ammonia pose challenges for its direct application in engines. The introduction of hydrogen (H2) as a combustion enhancer shows promise in improving ammonia viability for engine use. While previous studies have confirmed the benefits of hydrogen addition to ammonia for enhanced engine performance, comprehensive analysis on the precise ammonia-to-hydrogen ratio for optimal efficacy remains scarce. This research aims to bridge this gap by evaluating hydrogen-ammonia mixtures for achieving methane-equivalent laminar flame speeds under typical engine conditions, with a focus on the kernel inception process primarily driven by laminar flames. The findings indicate that a minimum of 20% hydrogen mixed with ammonia is necessary to facilitate a rapid spark inception, although it does not reach the laminar flame speed of methane. Additionally, employing a high compression ratio and operating near stoichiometry could lower the required hydrogen-ammonia ratio. Considering the challenges in generating ample hydrogen with NH3 dissociators and the need for operational conditions like full-load and low-speed to lessen hydrogen demand, ammonia-hydrogen fuel blends are deemed most suitable for stationary engine applications in the near term.
{"title":"Assessing Hydrogen-Ammonia Ratios to Achieve Rapid Kernel Inception in Spark Ignition Engines","authors":"Yuchao Yan, Tansu Shang, Lingmin Li, Zhen-tao Liu, Jinlong Liu","doi":"10.1115/1.4065198","DOIUrl":"https://doi.org/10.1115/1.4065198","url":null,"abstract":"\u0000 In the quest for decarbonizing internal combustion engines, ammonia (NH3) is recognized as a viable alternative fuel due to its zero-carbon emission profile, positioning it as a potential substitute for conventional petroleum fuels. However, the suboptimal combustion characteristics of ammonia pose challenges for its direct application in engines. The introduction of hydrogen (H2) as a combustion enhancer shows promise in improving ammonia viability for engine use. While previous studies have confirmed the benefits of hydrogen addition to ammonia for enhanced engine performance, comprehensive analysis on the precise ammonia-to-hydrogen ratio for optimal efficacy remains scarce. This research aims to bridge this gap by evaluating hydrogen-ammonia mixtures for achieving methane-equivalent laminar flame speeds under typical engine conditions, with a focus on the kernel inception process primarily driven by laminar flames. The findings indicate that a minimum of 20% hydrogen mixed with ammonia is necessary to facilitate a rapid spark inception, although it does not reach the laminar flame speed of methane. Additionally, employing a high compression ratio and operating near stoichiometry could lower the required hydrogen-ammonia ratio. Considering the challenges in generating ample hydrogen with NH3 dissociators and the need for operational conditions like full-load and low-speed to lessen hydrogen demand, ammonia-hydrogen fuel blends are deemed most suitable for stationary engine applications in the near term.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"104 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370776","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}
Kazi Ekramul Hoque, Tahiya Hossain, Abm Mominul Haque, Md. Abdul Karim Miah, MD Azazul Haque
The reduction of NOx emissions is a paramount endeavor in contemporary engineering and energy production, as these emissions are closely linked to adverse environmental and health impacts. The prediction of NOx emission from gas turbines through several integrated data-driven machine learning methods have been evaluated in study. The study also assesses the performance of ensemble machine learning models in comparison to conventional methods, with results indicating the superior accuracy of ensemble models. Specifically, the Random Forest model achieved an accuracy rate of 91.68%, XGBoost yielded an accuracy of 91.54%, and CATBoost exhibited the highest accuracy at 92.76%. These findings highlight the capability of data-driven machine learning techniques to enhance NOx emission predictions in gas turbines. This enhancement aids in the development and implementation of more effective control and mitigation strategies in practical applications. Through the application these advanced machine learning approaches, the gas turbine industry can play a pivotal role in minimizing its environmental impact while optimizing operational efficiency. This study also provides valuable insights into the effectiveness of ensemble machine learning models, advancing our understanding of their capabilities in addressing the critical issue of NOx emissions from gas turbines.
{"title":"NOx Emission Predictions in Gas Turbines through Integrated Data-Driven Machine Learning Approaches","authors":"Kazi Ekramul Hoque, Tahiya Hossain, Abm Mominul Haque, Md. Abdul Karim Miah, MD Azazul Haque","doi":"10.1115/1.4065200","DOIUrl":"https://doi.org/10.1115/1.4065200","url":null,"abstract":"\u0000 The reduction of NOx emissions is a paramount endeavor in contemporary engineering and energy production, as these emissions are closely linked to adverse environmental and health impacts. The prediction of NOx emission from gas turbines through several integrated data-driven machine learning methods have been evaluated in study. The study also assesses the performance of ensemble machine learning models in comparison to conventional methods, with results indicating the superior accuracy of ensemble models. Specifically, the Random Forest model achieved an accuracy rate of 91.68%, XGBoost yielded an accuracy of 91.54%, and CATBoost exhibited the highest accuracy at 92.76%. These findings highlight the capability of data-driven machine learning techniques to enhance NOx emission predictions in gas turbines. This enhancement aids in the development and implementation of more effective control and mitigation strategies in practical applications. Through the application these advanced machine learning approaches, the gas turbine industry can play a pivotal role in minimizing its environmental impact while optimizing operational efficiency. This study also provides valuable insights into the effectiveness of ensemble machine learning models, advancing our understanding of their capabilities in addressing the critical issue of NOx emissions from gas turbines.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"133 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369831","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}
Herringbone well is effective in improving productivity for bottom-water reservoir, however, the main problem faced in the exploitation of bottom water reservoir is the ridge and cone of bottom water during the process of waterflooding, which leads to the decline of oil production. Therefore, predicting the breakthrough time and location of herringbone wells in bottom water reservoirs and then adjust the water injection measures are of great significance for improving production and development. In this paper, by establishing a three-dimensional coning model of bottom water to study the dynamic performance of bottom water rises, and the sequence of breakthrough position is determined by studying the breakthrough time along the wellbore. Based on the reservoir numerical simulation, carry out the comprehensive adjustment of water injection mechanism, develops the water injection scheme under the combination arrangement of vertical wells and herringbone well. The results show that the bottom water breakthrough position of the branch well is mainly near the heel of the main branch or near the middle subsidence, and the recovery rate is the highest when the branch Angle is 45°. The longer the shut-in time, the higher the recovery. The study is of great significance to optimize the layout and spatial structure, determine a reasonable working system, delay water channeling, and increase the cumulative production of herringbone wells.
{"title":"Dynamics of water sighting and injection mechanisms in fishbone branch wells in bottomwater reservoirs","authors":"Guoqing Zhang, Chunxue Cui, Zhijun Zhou, Juan Wang, Jian Zhang, Guifeng Hou","doi":"10.1115/1.4065199","DOIUrl":"https://doi.org/10.1115/1.4065199","url":null,"abstract":"\u0000 Herringbone well is effective in improving productivity for bottom-water reservoir, however, the main problem faced in the exploitation of bottom water reservoir is the ridge and cone of bottom water during the process of waterflooding, which leads to the decline of oil production. Therefore, predicting the breakthrough time and location of herringbone wells in bottom water reservoirs and then adjust the water injection measures are of great significance for improving production and development. In this paper, by establishing a three-dimensional coning model of bottom water to study the dynamic performance of bottom water rises, and the sequence of breakthrough position is determined by studying the breakthrough time along the wellbore. Based on the reservoir numerical simulation, carry out the comprehensive adjustment of water injection mechanism, develops the water injection scheme under the combination arrangement of vertical wells and herringbone well. The results show that the bottom water breakthrough position of the branch well is mainly near the heel of the main branch or near the middle subsidence, and the recovery rate is the highest when the branch Angle is 45°. The longer the shut-in time, the higher the recovery. The study is of great significance to optimize the layout and spatial structure, determine a reasonable working system, delay water channeling, and increase the cumulative production of herringbone wells.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"74 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371305","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}
Thermal energy storage (TES) plays a pivotal role in a wide array of energy systems, offering a highly effective means to harness renewable energy sources, trim energy consumption and costs, reduce environmental impact, and bolster the adaptability and dependability of power grids. Concurrently, artificial intelligence (AI) has risen in prominence for optimizing and fine-tuning TES systems. Various AI techniques, such as particle swarm optimization, artificial neural networks, support vector machines, and adaptive neuro-fuzzy inference systems, have been extensively explored in the realm of energy storage. This study provides a comprehensive overview of how AI, across diverse applications, categorizes, and optimizes energy systems. The study critically evaluates the effectiveness of these AI technologies, highlighting their impressive accuracy in achieving a range of objectives. Through a thorough analysis, the paper also offers valuable recommendations and outlines future research directions, aiming to inspire innovative concepts and advancements in leveraging AI for TESS. By bridging the gap between TES and AI techniques, this study contributes significantly to the progress of energy systems, enhancing their efficiency, reliability, and sustainability. The insights gleaned from this research will be invaluable for researchers, engineers, and policymakers, aiding them in making well-informed decisions regarding the design, operation, and management of energy systems integrated with TES.
{"title":"Artificial Intelligence for thermal energy storage enhancement: A Comprehensive Review","authors":"T. Chekifi, M. Boukraa, Amine Benmoussa","doi":"10.1115/1.4065197","DOIUrl":"https://doi.org/10.1115/1.4065197","url":null,"abstract":"\u0000 Thermal energy storage (TES) plays a pivotal role in a wide array of energy systems, offering a highly effective means to harness renewable energy sources, trim energy consumption and costs, reduce environmental impact, and bolster the adaptability and dependability of power grids. Concurrently, artificial intelligence (AI) has risen in prominence for optimizing and fine-tuning TES systems. Various AI techniques, such as particle swarm optimization, artificial neural networks, support vector machines, and adaptive neuro-fuzzy inference systems, have been extensively explored in the realm of energy storage. This study provides a comprehensive overview of how AI, across diverse applications, categorizes, and optimizes energy systems. The study critically evaluates the effectiveness of these AI technologies, highlighting their impressive accuracy in achieving a range of objectives. Through a thorough analysis, the paper also offers valuable recommendations and outlines future research directions, aiming to inspire innovative concepts and advancements in leveraging AI for TESS. By bridging the gap between TES and AI techniques, this study contributes significantly to the progress of energy systems, enhancing their efficiency, reliability, and sustainability. The insights gleaned from this research will be invaluable for researchers, engineers, and policymakers, aiding them in making well-informed decisions regarding the design, operation, and management of energy systems integrated with TES.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"130 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369754","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}
The increasing concentration of greenhouse gasses in Earth's atmosphere is a critical concern, of which 75% of carbon dioxide (CO2) emissions are from the combustion of fossil fuels. This rapid increase in emissions led to irredeemable damages to ecosystems such as climate change, acid rain, etc. As a result, industries and academia have focused on developing innovative and cost-effective technologies for CO2 capture and storage (CCS). Physical/chemical absorption using amine and membrane-based technologies are generally used in CCS systems. However, the inherent technical and cost-effective limitations of these techniques directed their attention toward applying nanotechnologies for CCS systems. Here, the researchers have focused on infusing nanoparticles (NPs) into existing CCS technologies. The NPs could either be suspended in a base fluid to create nanofluids (NFs) or infused with membrane base materials to create nanocomposite membranes for enhanced carbon capture capabilities. This review paper investigates the manufacturing methods, characterization techniques and various mechanisms to analyze the impact of nanoparticles-infused nanofluids and nanocomposite membranes for CO2 capture. Finally, the paper summarises the factors associated with the two technologies and then outlines the drawbacks and benefits of incorporating NPs for CCS applications.
{"title":"Nanofluids and nanocomposite membranes for enhanced CO2 capture: A Comprehensive Review","authors":"Dirar Aletan, E. Shirif, SD Jacob Muthu","doi":"10.1115/1.4065147","DOIUrl":"https://doi.org/10.1115/1.4065147","url":null,"abstract":"\u0000 The increasing concentration of greenhouse gasses in Earth's atmosphere is a critical concern, of which 75% of carbon dioxide (CO2) emissions are from the combustion of fossil fuels. This rapid increase in emissions led to irredeemable damages to ecosystems such as climate change, acid rain, etc. As a result, industries and academia have focused on developing innovative and cost-effective technologies for CO2 capture and storage (CCS). Physical/chemical absorption using amine and membrane-based technologies are generally used in CCS systems. However, the inherent technical and cost-effective limitations of these techniques directed their attention toward applying nanotechnologies for CCS systems. Here, the researchers have focused on infusing nanoparticles (NPs) into existing CCS technologies. The NPs could either be suspended in a base fluid to create nanofluids (NFs) or infused with membrane base materials to create nanocomposite membranes for enhanced carbon capture capabilities. This review paper investigates the manufacturing methods, characterization techniques and various mechanisms to analyze the impact of nanoparticles-infused nanofluids and nanocomposite membranes for CO2 capture. Finally, the paper summarises the factors associated with the two technologies and then outlines the drawbacks and benefits of incorporating NPs for CCS applications.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140218320","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}
Electric vehicles (EVs) have emerged as an environmentally friendly alternative to conventional fuel vehicles. Lithium-ion batteries are the major energy source for EVs, but they degrade under dynamic operating conditions. Accurate estimation of battery state of health (SOH) is important for sustainability as it quantifies battery condition, influences reuse possibilities, and helps alleviate capacity degradation, which finally impacts battery lifespan and energy efficiency. In this paper, a self-attention graph neural network combined with long short-term memory (LSTM) is introduced by focusing on using temporal and spatial dependencies in battery data. The LSTM layer utilizes a sliding window to extract temporal dependencies in the battery health factors. Two different approaches to the graph construction layer are subsequently developed: health factor-based and window-based graph. Each approach emphasizes the interconnections between individual health factors and exploits temporal features in a deeper way, respectively. The self-attention mechanism is used to compute the adjacent weight matrix, which measures the strength of interactions between nodes in the graph. The impact of the two graph structures on the model performance is discussed. The model accuracy and computational cost of the proposed model are compared with the individual LSTM and GRU models.
{"title":"State of Health Estimation for Sustainable Electric Vehicle Batteries Using Temporal-Enhanced Self-Attention Graph Neural Networks","authors":"Yixin Zhao, Sara Behdad","doi":"10.1115/1.4065146","DOIUrl":"https://doi.org/10.1115/1.4065146","url":null,"abstract":"\u0000 Electric vehicles (EVs) have emerged as an environmentally friendly alternative to conventional fuel vehicles. Lithium-ion batteries are the major energy source for EVs, but they degrade under dynamic operating conditions. Accurate estimation of battery state of health (SOH) is important for sustainability as it quantifies battery condition, influences reuse possibilities, and helps alleviate capacity degradation, which finally impacts battery lifespan and energy efficiency. In this paper, a self-attention graph neural network combined with long short-term memory (LSTM) is introduced by focusing on using temporal and spatial dependencies in battery data. The LSTM layer utilizes a sliding window to extract temporal dependencies in the battery health factors. Two different approaches to the graph construction layer are subsequently developed: health factor-based and window-based graph. Each approach emphasizes the interconnections between individual health factors and exploits temporal features in a deeper way, respectively. The self-attention mechanism is used to compute the adjacent weight matrix, which measures the strength of interactions between nodes in the graph. The impact of the two graph structures on the model performance is discussed. The model accuracy and computational cost of the proposed model are compared with the individual LSTM and GRU models.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140215421","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 order to reduce energy consumption and related CO2 emissions, waste heat recovery is considered a viable opportunity in several economic sectors, with particular attention on industry and transportation. Among different proposed technologies, thermodynamic cycles using suitable organic working fluids seem to be promising options, and the possibility of combining two different cycles improves the final recovered energy. In this paper, a combination of Brayton and Rankine cycles is proposed: the upper cycle has carbon dioxide as the working fluid, in supercritical phase (sCO2), while the bottomed Rankine section is performed by an organic fluid (ORC). This combined unit is applied to recover the exhaust energy of the flue gases of an internal combustion engine (ICE) for the transportation sector. The sCO2 Brayton cycle is directly facing the exhaust gases, and it should dispose a certain amount of energy at lower pressure, which can be furtherly recovered by the ORC-unit. A specific mathematical model has been developed, which makes use of experimental data of the engine to assess a realistic final recoverable energy. The model is able to evaluate the performance of each subsection of the recovery, highlighting the interactions and possible trade-offs between them. Hence, the combined system can be optimized from a global point-of-view, identifying the most influencing operating parameters and also considering a regeneration stage in the ORC unit.
{"title":"Combined supercritical CO2 Brayton cycle and Organic Rankine Cycle for exhaust heat recovery","authors":"R. Carapellucci, Davide Di Battista","doi":"10.1115/1.4065080","DOIUrl":"https://doi.org/10.1115/1.4065080","url":null,"abstract":"\u0000 In order to reduce energy consumption and related CO2 emissions, waste heat recovery is considered a viable opportunity in several economic sectors, with particular attention on industry and transportation. Among different proposed technologies, thermodynamic cycles using suitable organic working fluids seem to be promising options, and the possibility of combining two different cycles improves the final recovered energy. In this paper, a combination of Brayton and Rankine cycles is proposed: the upper cycle has carbon dioxide as the working fluid, in supercritical phase (sCO2), while the bottomed Rankine section is performed by an organic fluid (ORC). This combined unit is applied to recover the exhaust energy of the flue gases of an internal combustion engine (ICE) for the transportation sector. The sCO2 Brayton cycle is directly facing the exhaust gases, and it should dispose a certain amount of energy at lower pressure, which can be furtherly recovered by the ORC-unit. A specific mathematical model has been developed, which makes use of experimental data of the engine to assess a realistic final recoverable energy. The model is able to evaluate the performance of each subsection of the recovery, highlighting the interactions and possible trade-offs between them. Hence, the combined system can be optimized from a global point-of-view, identifying the most influencing operating parameters and also considering a regeneration stage in the ORC unit.","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242850","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}
{"title":"Erratum: “Potential of Integrating Solar Energy into Systems of Thermal Power Generation, Cooling-Refrigeration, Hydrogen Production, and Carbon Capture” [Journal of Energy Resources Technology, 2023, 145 (11), P. 110801; https://doi.org/10.1115/1.4062381]","authors":"Mohamed A. Habib","doi":"10.1115/1.4065081","DOIUrl":"https://doi.org/10.1115/1.4065081","url":null,"abstract":"","PeriodicalId":509700,"journal":{"name":"Journal of Energy Resources Technology","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243721","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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