Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep...Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.展开更多
As the dual task of question answering,question generation(QG)is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph.The QG task is of great significance to quest...As the dual task of question answering,question generation(QG)is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph.The QG task is of great significance to question answering systems,conversational systems,and machine reading comprehension systems.Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks.However,the task of Tibetan QG is rarely mentioned.The key factor impeding its development is the lack of a public Tibetan QG dataset.Faced with this challenge,the present paper first collects 425 articles from the Tibetan Wikipedia website and constructs 7,234 question–answer pairs through crowdsourcing.Next,we propose a Tibetan QG model based on the sequence to sequence framework to generate Tibetan questions from given paragraphs.Secondly,in order to generate answer-aware questions,we introduce an attention mechanism that can capture the key semantic information related to the answer.Meanwhile,we adopt a copy mechanism to copy some words in the paragraph to avoid generating unknown or rare words in the question.Finally,experiments show that our model achieves higher performance than baseline models.We also further explore the attention and copy mechanisms,and prove their effectiveness through experiments.展开更多
The V_2C compound,belonging to the group of two-dimensional transition metal carbonitrides,or MXenes,has demonstrated a promising electrochemical performance in capacitor applications in acidic electrolytes;however,th...The V_2C compound,belonging to the group of two-dimensional transition metal carbonitrides,or MXenes,has demonstrated a promising electrochemical performance in capacitor applications in acidic electrolytes;however,there is evidence to suggest that V_2C is unstable in an acidic environment.On the other hand,the performance of V_2C in neutral aqueous electrolytes is still moderate,and has not yet been systematically studied.The charge storage mechanism in a V_2C electrode,employed in neutral aqueous electrolytes,is investigated via cyclic voltammetry testing and in situ x-ray diffraction(XRD).Good specific capacitances are achieved,specifically208 F/g in 0.5 M Li_2SO_4,225 F/g in 1 M MgSO_4,120 F/g in 1 M Na_2 SO_4,and 104 F/g in 0.5 M K_2SO_4.Using in situ XRD,we observe that,during the charge and discharge process,the c-lattice parameter shrinks or expands by up to 0.25 A in MgSO_4,and 0.29 A in Li_2SO_4 which demonstrates the intercalation/de-intercalation of cations into the d-V_2C layer.展开更多
文摘Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.
基金This work is supported by the National Nature Science Foundation(No.61972436).
文摘As the dual task of question answering,question generation(QG)is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph.The QG task is of great significance to question answering systems,conversational systems,and machine reading comprehension systems.Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks.However,the task of Tibetan QG is rarely mentioned.The key factor impeding its development is the lack of a public Tibetan QG dataset.Faced with this challenge,the present paper first collects 425 articles from the Tibetan Wikipedia website and constructs 7,234 question–answer pairs through crowdsourcing.Next,we propose a Tibetan QG model based on the sequence to sequence framework to generate Tibetan questions from given paragraphs.Secondly,in order to generate answer-aware questions,we introduce an attention mechanism that can capture the key semantic information related to the answer.Meanwhile,we adopt a copy mechanism to copy some words in the paragraph to avoid generating unknown or rare words in the question.Finally,experiments show that our model achieves higher performance than baseline models.We also further explore the attention and copy mechanisms,and prove their effectiveness through experiments.
基金Supported by the Science&Technology Department of Jilin Province (Grant Nos.20180101199JC and 20180101204JC)Jilin Province/Jilin University Co-construction Project-Funds for New Materials (SXGJSF2017-3)。
文摘The V_2C compound,belonging to the group of two-dimensional transition metal carbonitrides,or MXenes,has demonstrated a promising electrochemical performance in capacitor applications in acidic electrolytes;however,there is evidence to suggest that V_2C is unstable in an acidic environment.On the other hand,the performance of V_2C in neutral aqueous electrolytes is still moderate,and has not yet been systematically studied.The charge storage mechanism in a V_2C electrode,employed in neutral aqueous electrolytes,is investigated via cyclic voltammetry testing and in situ x-ray diffraction(XRD).Good specific capacitances are achieved,specifically208 F/g in 0.5 M Li_2SO_4,225 F/g in 1 M MgSO_4,120 F/g in 1 M Na_2 SO_4,and 104 F/g in 0.5 M K_2SO_4.Using in situ XRD,we observe that,during the charge and discharge process,the c-lattice parameter shrinks or expands by up to 0.25 A in MgSO_4,and 0.29 A in Li_2SO_4 which demonstrates the intercalation/de-intercalation of cations into the d-V_2C layer.
