As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
Climate change is the greatest environmental threat to humans and the planet in the 21st century.Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate eve...Climate change is the greatest environmental threat to humans and the planet in the 21st century.Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate events.Cumulative carbon dioxide(CO_(2))emissions showed a linear relationship with cumulative temperature rise since the pre-industrial stage,and this accounts for approximately 80%of the total anthropogenic greenhouse gases.Therefore,accurate and reliable carbon emission data are the foundation and scientific basis for most emission reduction policymaking and target setting.Currently,China has made clear the ambitious goal of achieving the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060.The development of a finer-grained spatiotemporal carbon emission database is urgently needed to achieve more accurate carbon emission monitoring for continuous implementation and the iterative improvement of emission reduction policies.Near-real-time carbon emission monitoring is not only a major national demand but also a scientific question at the frontier of this discipline.This article reviews existing annual-based carbon accounting methods,with a focus on the newly developed real-time carbon emission technology and its current application trends.We also present a framework for the latest near-real-time carbon emission accounting technology that can be widely used.The development of relevant data and methods will provide strong database support to the policymaking for China’s“carbon neutrality”strategy.Finally,this article provides an outlook on the future of real-time carbon emission monitoring technology.展开更多
Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensi...Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensive OCCUS project. A potential high-resolution method for the aforementioned purpose lies in the full-waveform inversion(FWI) of time-lapse seismic data. However, practical applications of FWI are severely restricted by the well-known cycle-skipping problem. A new time-lapse FWI method using cross-correlation-based dynamic time warping(CDTW) is proposed to detect changes in the subsurface property due to carbon dioxide(CO_(2)) injection and address the aforementioned issue. The proposed method, namely CDTW, which combines the advantages of cross-correlation and dynamic time warping, is employed in the automatic estimation of the discrepancy between the seismic signals simulated using the baseline/initial model and those acquired. The proposed FWI method can then back-project the estimated discrepancy to the subsurface space domain, thereby facilitating retrieval of the induced subsurface property change by taking the difference between the inverted baseline and monitor models. Numerical results on pairs of signals prove that CDTW can obtain reliable shifts under amplitude modulation and noise contamination conditions. The performance of CDTW substantially outperforms that of the conventional dynamic time warping method. The proposed time-lapse fullwaveform inversion(FWI) method is applied to the Frio-2 CO_(2) storage model. The baseline and monitor models are inverted from the corresponding time-lapse seismic data. The changes in velocity due to CO_(2) injection are reconstructed by the difference between the baseline and the monitor models.展开更多
The process industry plays a crucial role in national economic development and national defense construction.However,as a typical energy-intensive industry,it is at the forefront of efforts to reduce carbon emissions....The process industry plays a crucial role in national economic development and national defense construction.However,as a typical energy-intensive industry,it is at the forefront of efforts to reduce carbon emissions.Information and communications technology(ICT)is an important means of achieving low-carbon operation in the process industry by strengthening the regulation of carbon flow in the production process.This paper first introduces the relevant research on low-carbon operation of industrial processes,and analyzes and summarizes the current research status and bottleneck.Then,the challenging problems faced by ICT in achieving low-carbon operation in the process industry are analyzed from four aspects:carbon emission sensing,carbon transfer modeling,carbon migration control,as well as low-carbon operation optimization throughout the entire process.Finally,the overall framework and vision for implementing low-carbon operation in the process industry through ICT are presented,and future research directions are proposed in conjunction with industrial artificial intelligence.展开更多
In order to mitigate global warming,the international communities actively explore low-carbon and green development methods.According to the Paris Agreement that came into effect in 2016,there will be a global stockta...In order to mitigate global warming,the international communities actively explore low-carbon and green development methods.According to the Paris Agreement that came into effect in 2016,there will be a global stocktaking plan to carry out every 5 years from 2023 onwards.In September 2020,China proposed a"double carbon"target of carbon peaking before 2030 and carbon neutrality before 2060.Achieving carbon peaking and carbon neutrality goals requires accurate carbon emissions and carbon absorptions.China’s existing carbon monitoring methods have insufficient detection accuracy,low spatial resolution,and narrow swath,which are difficult to meet the monitoring requirement of carbon sources and sinks monitoring.In order to meet the needs of carbon stocktaking and support the monitoring and supervision of carbon sources and sinks,it is recommended to make full use of the foundation of the existing satellites,improve the detection technical specifications of carbon sources and sinks monitoring measures,and build a multi-means and comprehensive,LEO-GEO orbit carbon monitoring satellite system to achieve higher precision,higher resolution and multi-dimensional carbon monitoring.On this basis,it is recommended to strengthen international cooperation,improve data sharing policy,actively participate in the development of carbon retrieval algorithm and the setting of international carbon monitoring standards,establish an independent and controllable global carbon monitoring and evaluation system,and contribute China’s strength to the global realization of carbon peaking and carbon neutrality.展开更多
The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. ...The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO2 variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO2 data was applied to predict the daily atmospheric CO2. Further, the recurrent nonlinear autoregressive with exogenous input(NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO2 storage and transportation technologies.展开更多
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
基金financially supported by the National Natural Science Foundation of China (71874097 and 41921005)Beijing Natural Science Foundation (JQ19032)+1 种基金the Qiu Shi Science & Technologies Foundationthe Shenzhen Municipal Science and Technology Commission College Stability Support Project (WDZC20200819173345002)
文摘Climate change is the greatest environmental threat to humans and the planet in the 21st century.Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate events.Cumulative carbon dioxide(CO_(2))emissions showed a linear relationship with cumulative temperature rise since the pre-industrial stage,and this accounts for approximately 80%of the total anthropogenic greenhouse gases.Therefore,accurate and reliable carbon emission data are the foundation and scientific basis for most emission reduction policymaking and target setting.Currently,China has made clear the ambitious goal of achieving the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060.The development of a finer-grained spatiotemporal carbon emission database is urgently needed to achieve more accurate carbon emission monitoring for continuous implementation and the iterative improvement of emission reduction policies.Near-real-time carbon emission monitoring is not only a major national demand but also a scientific question at the frontier of this discipline.This article reviews existing annual-based carbon accounting methods,with a focus on the newly developed real-time carbon emission technology and its current application trends.We also present a framework for the latest near-real-time carbon emission accounting technology that can be widely used.The development of relevant data and methods will provide strong database support to the policymaking for China’s“carbon neutrality”strategy.Finally,this article provides an outlook on the future of real-time carbon emission monitoring technology.
文摘Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensive OCCUS project. A potential high-resolution method for the aforementioned purpose lies in the full-waveform inversion(FWI) of time-lapse seismic data. However, practical applications of FWI are severely restricted by the well-known cycle-skipping problem. A new time-lapse FWI method using cross-correlation-based dynamic time warping(CDTW) is proposed to detect changes in the subsurface property due to carbon dioxide(CO_(2)) injection and address the aforementioned issue. The proposed method, namely CDTW, which combines the advantages of cross-correlation and dynamic time warping, is employed in the automatic estimation of the discrepancy between the seismic signals simulated using the baseline/initial model and those acquired. The proposed FWI method can then back-project the estimated discrepancy to the subsurface space domain, thereby facilitating retrieval of the induced subsurface property change by taking the difference between the inverted baseline and monitor models. Numerical results on pairs of signals prove that CDTW can obtain reliable shifts under amplitude modulation and noise contamination conditions. The performance of CDTW substantially outperforms that of the conventional dynamic time warping method. The proposed time-lapse fullwaveform inversion(FWI) method is applied to the Frio-2 CO_(2) storage model. The baseline and monitor models are inverted from the corresponding time-lapse seismic data. The changes in velocity due to CO_(2) injection are reconstructed by the difference between the baseline and the monitor models.
基金supported in part by the National Key R&D Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(62394340,62073340,61860206014)+1 种基金in part by China Engineering Science and Technology Development Strategy Jiangxi Research Institute Consulting Research Project(2023-02JXZD-02)in part by the 111 Project,China(B17048).
文摘The process industry plays a crucial role in national economic development and national defense construction.However,as a typical energy-intensive industry,it is at the forefront of efforts to reduce carbon emissions.Information and communications technology(ICT)is an important means of achieving low-carbon operation in the process industry by strengthening the regulation of carbon flow in the production process.This paper first introduces the relevant research on low-carbon operation of industrial processes,and analyzes and summarizes the current research status and bottleneck.Then,the challenging problems faced by ICT in achieving low-carbon operation in the process industry are analyzed from four aspects:carbon emission sensing,carbon transfer modeling,carbon migration control,as well as low-carbon operation optimization throughout the entire process.Finally,the overall framework and vision for implementing low-carbon operation in the process industry through ICT are presented,and future research directions are proposed in conjunction with industrial artificial intelligence.
文摘In order to mitigate global warming,the international communities actively explore low-carbon and green development methods.According to the Paris Agreement that came into effect in 2016,there will be a global stocktaking plan to carry out every 5 years from 2023 onwards.In September 2020,China proposed a"double carbon"target of carbon peaking before 2030 and carbon neutrality before 2060.Achieving carbon peaking and carbon neutrality goals requires accurate carbon emissions and carbon absorptions.China’s existing carbon monitoring methods have insufficient detection accuracy,low spatial resolution,and narrow swath,which are difficult to meet the monitoring requirement of carbon sources and sinks monitoring.In order to meet the needs of carbon stocktaking and support the monitoring and supervision of carbon sources and sinks,it is recommended to make full use of the foundation of the existing satellites,improve the detection technical specifications of carbon sources and sinks monitoring measures,and build a multi-means and comprehensive,LEO-GEO orbit carbon monitoring satellite system to achieve higher precision,higher resolution and multi-dimensional carbon monitoring.On this basis,it is recommended to strengthen international cooperation,improve data sharing policy,actively participate in the development of carbon retrieval algorithm and the setting of international carbon monitoring standards,establish an independent and controllable global carbon monitoring and evaluation system,and contribute China’s strength to the global realization of carbon peaking and carbon neutrality.
基金the National Natural Science Foundation of China(21808181)China Postdoctoral Science Foundation(2019M653651)+1 种基金Shaanxi Provincial Science and Technology Department(2017ZDXM-GY-115)Basic Research Project of Natural Science in Shaanxi Province(2020JM-021)。
文摘The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO2 variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO2 data was applied to predict the daily atmospheric CO2. Further, the recurrent nonlinear autoregressive with exogenous input(NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO2 storage and transportation technologies.
