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< Page ,Total 27 >
Gas recognition method based on the deep learning model of sensor array response map EI SCIE
期刊论文 | 2021 , 330 | Sensors and Actuators, B: Chemical
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Abstract :

It is important to detect and recognize the unknown gases or VOCs (Volatile Organic Compounds) in industrial safety issues. Electronic nose is a novel and portable method to detect the VOCs with high accuracy combined with sensor array and artificial intelligence algorithm. The results indicated that the multidimensional dynamic response signals of the sensor array can be viewed as the image form. Thus, a new method coupled dynamic response map with deep learning model (DLM) was proposed to improve the accuracy of the sensor array. The error-correcting output codes (ECOC) model with support vector machine (SVM) learners was applied to discriminate different VOCs. The results showed that the model with the data from the sensor array classified the VOCs more accurately than that with just single sensor. Further, a simple DLM network was trained to classify the VOCs with the accuracy of 92 %. Then the transferred VGG-19 model was further adapted to improve the generalization property of DLM with the accuracy of 90 %. Moreover, all sensors’ responses at certain time were normalized before building the model, which enhanced the prediction accuracy to 96 % for simple DLM and 94 % for transferred VGG-19. Finally, the concentrations of different substances were predicted with SVM and DLM. The results showed that the prediction error of SVM and DLM with multidimensional response map is lower that with the data from single sensor. Therefore, it is a feasible tool to detect VOCs with just one sensor module using the response map-DLM method proposed in this research. © 2020 Elsevier B.V.

Keyword :

Dynamic response Support vector machines Volatile organic compounds Risk management Accident prevention Lagrange multipliers Electronic nose Deep learning Learning systems

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GB/T 7714 Ma, Denglong , Gao, Jianmin , Zhang, Zaoxiao et al. Gas recognition method based on the deep learning model of sensor array response map [J]. | Sensors and Actuators, B: Chemical , 2021 , 330 .
MLA Ma, Denglong et al. "Gas recognition method based on the deep learning model of sensor array response map" . | Sensors and Actuators, B: Chemical 330 (2021) .
APA Ma, Denglong , Gao, Jianmin , Zhang, Zaoxiao , Zhao, Hong . Gas recognition method based on the deep learning model of sensor array response map . | Sensors and Actuators, B: Chemical , 2021 , 330 .
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Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades EI SCIE
期刊论文 | 2021 , 2021 | SHOCK AND VIBRATION
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Abstract :

Aiming at the problem of low resources utilization of rotating blades in the selection process of aeroengine fan rotor blades, this paper takes the first-order bending dispersion, first-order torque dispersion, and gravitational moment difference of rotor blades as the selection criteria and takes the minimum remaining blades as the optimization goal. An intelligent selection algorithm of blades based on the collocation degree of blades is proposed and achieves the efficient selection and full utilization of rotating blades. Aiming at the problem of multiple installations and multiple adjustments and low assembly success rate of fan rotor blades, this paper takes the gravity moment difference of the two blades at the diagonal position of 180 degrees as the constraint and takes the minimum residual unbalance as the optimization objective, adopts the improved simulated annealing algorithm to optimize the assembly sequence of rotating blades, and greatly reduces the residual unbalance of blades, which is beneficial to reduce the number of assembly adjustments of blades. The optimizing selection and optimizing matching methods of rotating blades realize the full utilization and efficient assembly of blades and lays a foundation for the reliability and robustness of the assembly quality and service performance of blades.

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GB/T 7714 Li, Lili , Chen, Kun , Gao, Jianmin et al. Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades [J]. | SHOCK AND VIBRATION , 2021 , 2021 .
MLA Li, Lili et al. "Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades" . | SHOCK AND VIBRATION 2021 (2021) .
APA Li, Lili , Chen, Kun , Gao, Jianmin , Gao, Zhiyong , Liu, Junkong . Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades . | SHOCK AND VIBRATION , 2021 , 2021 .
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Form Recognition Based on Lightweight U-Net and Tesseract after Multi-level Retraining EI
会议论文 | 2021 , 243-248 | 2021 International Conference of Optical Imaging and Measurement, ICOIM 2021
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Abstract :

With the rapid development of Internet information technology and the advancement of enterprise digitization, the digitization of paper forms has also received extensive attention. The automatic conversion of paper form documents into electronic form documents mainly faces three problems. The first is that the format of the form file is diverse and the structure is complex. This article uses the XML file of the form to accurately analyze the structure of the file, which is more accurate than the current semantic segmentation method. The second problem is table area detection, this paper uses traditional algorithms to find the contours of the candidate table area, and screens according to the characteristics of the table area to complete the detection and extraction of the table area. The third is that the recognition of the table text is more difficult, not only the interference information such as the table frame will also affect the accuracy of text recognition, and the type of text information in the table is complex, including Chinese, English, numbers, symbols and mixed types, which bring huge challenges to text recognition. This paper uses the lightweight U-Net network model to segment the text area at pixel level, eliminating the interference information of text recognition. The neural network of Tesseract was retrained in a multiple, multi-level manner, and successfully realized the recognition of complex types of text information with an accuracy of about 96%. Based on deep learning and XML table structure analysis algorithm, this paper realizes the recognition of paper version of the form file and the reconstruction of the electronic version of the file. © 2021 IEEE.

Keyword :

Character recognition XML Semantic Segmentation Complex networks Deep learning Semantics

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GB/T 7714 Li, Gang , Huang, Junhui , Wang, Zhao et al. Form Recognition Based on Lightweight U-Net and Tesseract after Multi-level Retraining [C] . 2021 : 243-248 .
MLA Li, Gang et al. "Form Recognition Based on Lightweight U-Net and Tesseract after Multi-level Retraining" . (2021) : 243-248 .
APA Li, Gang , Huang, Junhui , Wang, Zhao , Gao, Jianmin , Chen, Kun . Form Recognition Based on Lightweight U-Net and Tesseract after Multi-level Retraining . (2021) : 243-248 .
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Optimization Design of Lattice Structures in Internal Cooling Channel with Variable Aspect Ratio of Gas Turbine Blade SCIE
期刊论文 | 2021 , 14 (13) | ENERGIES
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Abstract :

Traditional cooling structures in gas turbines greatly improve the high temperature resistance of turbine blades; however, few cooling structures concern both heat transfer and mechanical performances. A lattice structure (LS) can solve this issue because of its advantages of being lightweight and having high porosity and strength. Although the topology of LS is complex, it can be manufactured with metal 3D printing technology in the future. In this study, an integral optimization model concerning both heat transfer and mechanical performances was presented to design the LS cooling channel with a variable aspect ratio in gas turbine blades. Firstly, some internal cooling channels with the thin walls were built up and a simple raw of five LS cores was taken as an insert or a turbulator in these cooling channels. Secondly, relations between geometric variables (height (H), diameter (D) and inclination angle(omega)) and objectives/functions of this research, including the first-order natural frequency (freq1), equivalent elastic modulus (E), relative density ((rho) over bar) and Nusselt number (Nu), were established for a pyramid-type lattice structure (PLS) and Kagome-type lattice structure (KLS). Finally, the ISIGHT platform was introduced to construct the frame of the integral optimization model. Two selected optimization problems (Op-I and Op-II) were solved based on the third-order response model with an accuracy of more than 0.97, and optimization results were analyzed. The results showed that the change of Nu and freq1 had the highest overall sensitivity Op-I and Op-II, respectively, and the change of D and H had the highest single sensitivity for Nu and freq1, respectively. Compared to the initial LS, the LS of Op-I increased Nu and E by 24.1% and 29.8%, respectively, and decreased (rho) over bar by 71%; the LS of Op-II increased Nu and E by 30.8% and 45.2%, respectively, and slightly increased (rho) over bar; the LS of both Op-I and Op-II decreased freq1 by 27.9% and 19.3%, respectively. These results suggested that the heat transfer, load bearing and lightweight performances of the LS were greatly improved by the optimization model (except for the lightweight performance for the optimal LS of Op-II, which became slightly worse), while it failed to improve vibration performance of the optimal LS.

Keyword :

integral optimization model internal cooling channel lattice structure heat transfer mechanical performances

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GB/T 7714 Xu, Liang , Ruan, Qicheng , Shen, Qingyun et al. Optimization Design of Lattice Structures in Internal Cooling Channel with Variable Aspect Ratio of Gas Turbine Blade [J]. | ENERGIES , 2021 , 14 (13) .
MLA Xu, Liang et al. "Optimization Design of Lattice Structures in Internal Cooling Channel with Variable Aspect Ratio of Gas Turbine Blade" . | ENERGIES 14 . 13 (2021) .
APA Xu, Liang , Ruan, Qicheng , Shen, Qingyun , Xi, Lei , Gao, Jianmin , Li, Yunlong . Optimization Design of Lattice Structures in Internal Cooling Channel with Variable Aspect Ratio of Gas Turbine Blade . | ENERGIES , 2021 , 14 (13) .
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Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade SCIE
期刊论文 | 2021 , 11 (13) | APPLIED SCIENCES-BASEL
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Recently, the inlet temperatures in gas turbine units have been drastically increased, which extremely affects the lifespan of gas turbine blades. Traditional cooling structures greatly improve the high temperature resistance of the blade; however, these structures scarcely concern both heat transfer and mechanical performances. Lattice structure (LS) can realize these requirements because of its characteristics of light weight, high strength, and porosity. Although the topology of LS is complex, it can be manufactured with the 3D metal printing technology. In this study, an integral optimization method of lattice cooling structure, used at the trailing edge of turbine blades, concerned with heat transfer and mechanical performance, was presented. Firstly, functions between the first-order natural frequency (freq1), elasticity modulus (E), relative density ((rho) over bar), and Nusselt number (Nu), and the geometric variables of pyramid type LS (PLS) and X-type LS (XLS) were established, and the reliability of these functions was verified. Then, a mathematical optimization model was developed based on these functions which contained two selected optimization problems. Finally, relations among objectives were analyzed; influence law of geometric variables to objectives were discussed, and the accuracy of the optimal LS was proved by experiment and numerical simulation. The optimization results suggest that, compared to the initial LS, Nu increases by 24.1% and p decreases by 31% in the optimal LS of the first selected problem, and the Nu increases by 28.8% while freq1 and (rho) over bar are almost unchanged in the optimal LS of the second selected problem compared to the initial LS. This study may provide a guidance for functions integration design of lattice cooling structures used at turbine blades based on 3D printing.

Keyword :

integral optimization method lattice cooling structure heat transfer and mechanical performances functions integration design

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GB/T 7714 Xu, Liang , Shen, Qingyun , Ruan, Qicheng et al. Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade [J]. | APPLIED SCIENCES-BASEL , 2021 , 11 (13) .
MLA Xu, Liang et al. "Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade" . | APPLIED SCIENCES-BASEL 11 . 13 (2021) .
APA Xu, Liang , Shen, Qingyun , Ruan, Qicheng , Xi, Lei , Gao, Jianmin , Li, Yunlong . Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade . | APPLIED SCIENCES-BASEL , 2021 , 11 (13) .
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Prediction of thermally induced failure for electronic equipment based on an artificial olfactory system EI SCIE Scopus
期刊论文 | 2021 , 32 (3) | MEASUREMENT SCIENCE AND TECHNOLOGY
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The failure of electronic equipment causes serious consequences and even catastrophic fires. Abnormal thermal signals are one of the main characteristics of the failure of electronic equipment. Thus, a new method for recognizing and predicting the thermally induced failure states of electronic equipment was proposed, based on an artificial olfactory system (AOS). The AOS recognizes the state of the volatile components released during the early stages of thermally induced failure and uses it to predict the state of health of the electronic equipment. Some typical electronic devices, such as microcomputer units, electronic rectifiers, transformers, and battery modules, were tested with the AOS to recognize the failures indicated by abnormal thermal accumulation. Compared with infrared thermal imagers and gas analyzers, the PEN3 electronic nose was utilized to monitor the status of the devices under different thermal failure scenarios. It was found that infrared thermal imaging was only able to monitor the local surface temperature, and the air temperature in the device chamber changed slowly with the surface temperature of the electronic modules. However, the AOS was able to detect the abnormal change in the whole chamber. Linear discriminant analysis (LDA) and principal component analysis (PCA) were then adopted to investigate the features of thermally induced failure for different thermal states. The results showed that the models obtained both from LDA and PCA were able to distinguish the different states of the electronic devices. Furthermore, a support vector machine model was built, based on the AOS data, to recognize and predict the thermally induced failure processes. All the failure states of the electronic devices caused by thermal simulations were recognized successfully and the prediction accuracy was above 95%. Hence, the experimental results of this research proved that using the AOS, it is feasible to predict the thermally induced failure states of electronic equipment, and the failure of electronic devices can be forecast in advance, before the obvious temperature rise and smoke release. Moreover, the method proposed in this research can also be applied to the prediction of, and warning about, electrical fires, indoor fires, and other thermally induced accidents.

Keyword :

AOS electronic nose electronic equipment linear discriminant analysis failure principal component analysis

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GB/T 7714 Ma, Denglong , Liu, Yuan , Zheng, Liangtian et al. Prediction of thermally induced failure for electronic equipment based on an artificial olfactory system [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (3) .
MLA Ma, Denglong et al. "Prediction of thermally induced failure for electronic equipment based on an artificial olfactory system" . | MEASUREMENT SCIENCE AND TECHNOLOGY 32 . 3 (2021) .
APA Ma, Denglong , Liu, Yuan , Zheng, Liangtian , Gao, Jianmin , Gao, Zhiyong , Zhang, Zaoxiao . Prediction of thermally induced failure for electronic equipment based on an artificial olfactory system . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2021 , 32 (3) .
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Large-Eddy Simulation Study of Flow and Heat Transfer in Swirling and Non-Swirling Impinging Jets on a Semi-Cylinder Concave Target SCIE
期刊论文 | 2021 , 11 (15) | APPLIED SCIENCES-BASEL
WoS CC Cited Count: 1
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Swirling impinging jet (SIJ) is considered as an effective means to achieve uniform cooling at high heat transfer rates, and the complex flow structure and its mechanism of enhancing heat transfer have attracted much attention in recent years. The large eddy simulation (LES) technique is employed to analyze the flow fields of swirling and non-swirling impinging jet emanating from a hole with four spiral and straight grooves, respectively, at a relatively high Reynolds number (Re) of 16,000 and a small jet spacing of H/D = 2 on a concave surface with uniform heat flux. Firstly, this work analyzes two different sub-grid stress models, and LES with the wall-adapting local eddy-viscosity model (WALEM) is established for accurately predicting flow and heat transfer performance of SIJ on a flat surface. The complex flow field structures, spectral characteristics, time-averaged flow characteristics and heat transfer on the target surface for the swirling and non-swirling impinging jets are compared in detail using the established method. The results show that small-scale recirculation vortices near the wall change the nearby flow into an unstable microwave state, resulting in small-scale fluctuation of the local Nusselt number (Nu) of the wall. There is a stable recirculation vortex at the stagnation point of the target surface, and the axial and radial fluctuating speeds are consistent with the fluctuating wall temperature. With the increase in the radial radius away from the stagnation point, the main frequency of the fluctuation of wall temperature coincides with the main frequency of the fluctuation of radial fluctuating velocity at x/D = 0.5. Compared with 0 degrees straight hole, 45 degrees spiral hole has a larger fluctuating speed because of speed deflection, resulting in a larger turbulence intensity and a stronger air transport capacity. The heat transfer intensity of the 45 degrees spiral hole on the target surface is slightly improved within 5-10%.

Keyword :

flow structures large eddy simulation swirling impinging jet heat transfer

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GB/T 7714 Xu, Liang , Zhao, Xu , Xi, Lei et al. Large-Eddy Simulation Study of Flow and Heat Transfer in Swirling and Non-Swirling Impinging Jets on a Semi-Cylinder Concave Target [J]. | APPLIED SCIENCES-BASEL , 2021 , 11 (15) .
MLA Xu, Liang et al. "Large-Eddy Simulation Study of Flow and Heat Transfer in Swirling and Non-Swirling Impinging Jets on a Semi-Cylinder Concave Target" . | APPLIED SCIENCES-BASEL 11 . 15 (2021) .
APA Xu, Liang , Zhao, Xu , Xi, Lei , Ma, Yonghao , Gao, Jianmin , Li, Yunlong . Large-Eddy Simulation Study of Flow and Heat Transfer in Swirling and Non-Swirling Impinging Jets on a Semi-Cylinder Concave Target . | APPLIED SCIENCES-BASEL , 2021 , 11 (15) .
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Identifying atmospheric pollutant sources using a machine learning dispersion model and Markov chain Monte Carlo methods EI SCIE
期刊论文 | 2021 , 35 (2) , 271-286 | Stochastic Environmental Research and Risk Assessment
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Estimating the sources of contaminant or hazard emissions is important for pollution control and safety management. Markov chain Monte Carlo (MCMC), combined with Bayesian inference, was used to identify the source terms of pollutants. However, the efficiency and accuracy of the forward dispersion model greatly impacted the performance of the estimation method. Therefore, a machine learning algorithm (MLA) model with high prediction accuracy and efficiency was proposed and coupled with MCMC method to estimate the source terms. A previously proposed MLA model was used to obtain the expected concentrations in Bayesian estimation. The Delayed Rejection Adaptive Metropolis (DRAM) method was applied to sample particles in order to form Markov chains. To evaluate the performance of the MCMC–MLA method, a Gaussian dispersion model was selected as the forward model. The performances of MCMC–MLA and MCMC–Gaussian models were then compared with release cases in Prairie Grass experiment and the results showed that the MCMC–MLA method converged more rapidly than the MCMC–Gaussian model. Nevertheless, release cases in the Round Hill experiment were also used to test the generalisability of the MCMC–MLA. The results indicated that the performance of MCMC–MLA was better than that of the MCMC–Gaussian model for estimating source terms in estimation accuracy. Hence, the MCMC–MLA method proposed here is potentially a useful tool for identifying emissions source parameters with high accuracy and efficiency, as well as reasonable probability estimates. Graphic abstract: [Figure not available: see fulltext.]. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Keyword :

Pollution control Bayesian networks Atmospheric movements Machine learning Turing machines Learning algorithms Monte Carlo methods Efficiency Inference engines Markov chains Gaussian distribution

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GB/T 7714 Ma, Denglong , Gao, Jianmin , Zhang, Zaoxiao et al. Identifying atmospheric pollutant sources using a machine learning dispersion model and Markov chain Monte Carlo methods [J]. | Stochastic Environmental Research and Risk Assessment , 2021 , 35 (2) : 271-286 .
MLA Ma, Denglong et al. "Identifying atmospheric pollutant sources using a machine learning dispersion model and Markov chain Monte Carlo methods" . | Stochastic Environmental Research and Risk Assessment 35 . 2 (2021) : 271-286 .
APA Ma, Denglong , Gao, Jianmin , Zhang, Zaoxiao , Zhao, Hong . Identifying atmospheric pollutant sources using a machine learning dispersion model and Markov chain Monte Carlo methods . | Stochastic Environmental Research and Risk Assessment , 2021 , 35 (2) , 271-286 .
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Locating the gas leakage source in the atmosphere using the dispersion wave method EI SCIE Scopus
期刊论文 | 2020 , 63 | JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
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A new and simple method for locating emission source was proposed in this work based on gas dynamic dispersion information. The simulation of the unsteady state dispersion of leakage gas emission from the geo-sequestration project showed that the transportation process of emission gases in the atmosphere is similar to wave propagation, and the time parameter of the dispersion wave is linearly related to the downwind distance. Therefore, monitoring the dispersion wave at different downwind positions can be used to estimate the leakage source position. An estimation formula for locating emission sources was derived. First, an estimation formula for locating emission sources was derived under some initial assumptions. Then, the deviation of the location formula was investigated using a computational fluid dynamics (CFD) model and analytic solution to get the offset distance under different conditions. The results showed that the average distance is stable for a certain atmosphere and terrestrial conditions. This method needs no more than 3 sensors' dynamic information to locate the emission source, and hence it is highly useful for conditions with limited sensors. A numerical test demonstrated that the absolute error of the source estimation is within the range of 1-30 m. Finally, experimental tests were conducted to verify the feasibility of the source location with dispersion waves. Therefore, the dispersion wave monitor is a potentially simple and feasible way to estimate the source location for gas emission event management with limited sensors in the process industries.

Keyword :

Source location Hazard emission Gas dispersion Emission source

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GB/T 7714 Ma, Denglong , Gao, Jianmin , Zhang, Zaoxiao et al. Locating the gas leakage source in the atmosphere using the dispersion wave method [J]. | JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES , 2020 , 63 .
MLA Ma, Denglong et al. "Locating the gas leakage source in the atmosphere using the dispersion wave method" . | JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 63 (2020) .
APA Ma, Denglong , Gao, Jianmin , Zhang, Zaoxiao , Zhao, Hong , Wang, Qingsheng . Locating the gas leakage source in the atmosphere using the dispersion wave method . | JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES , 2020 , 63 .
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Gas leakage recognition for CO2 geological sequestration based on the time series neural network EI SCIE Scopus CSCD
期刊论文 | 2020 , 28 (9) , 2343-2357 | Chinese Journal of Chemical Engineering | IF: 3.171
WoS CC Cited Count: 1
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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. © 2020 Elsevier B.V.

Keyword :

Weather forecasting Digital storage Predictive analytics Recurrent neural networks Geology Carbon dioxide Time series

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GB/T 7714 Ma, Denglong , Gao, Jianmin , Gao, Zhiyong et al. Gas leakage recognition for CO2 geological sequestration based on the time series neural network [J]. | Chinese Journal of Chemical Engineering , 2020 , 28 (9) : 2343-2357 .
MLA Ma, Denglong et al. "Gas leakage recognition for CO2 geological sequestration based on the time series neural network" . | Chinese Journal of Chemical Engineering 28 . 9 (2020) : 2343-2357 .
APA Ma, Denglong , Gao, Jianmin , Gao, Zhiyong , Jiang, Hongquan , Zhang, Zaoxiao , Xie, Juntai . Gas leakage recognition for CO2 geological sequestration based on the time series neural network . | Chinese Journal of Chemical Engineering , 2020 , 28 (9) , 2343-2357 .
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