The unfrozenwater content(UWC)is a crucial parameter that affects the strength and thermal properties of rocks in relation to engineering construction and geological disasters in cold regions.In this study,three diffe...The unfrozenwater content(UWC)is a crucial parameter that affects the strength and thermal properties of rocks in relation to engineering construction and geological disasters in cold regions.In this study,three different methods were employed to test and estimate the UWC of saturated sandstones,including nuclear magnetic resonance(NMR),mercury intrusion porosimetry(MIP),and ultrasonic methods.The NMR method enabled the direct measurement of the UWC of sandstones using the free induction decay(FID).The MIP method was used to analyze the pore structures of sandstones,with the UWC subsequently calculated based on pore ice crystallization.Therefore,the MIP test constituted an indirect measurement method.Furthermore,a correlation was established between the P-wave velocity and the UWC of these sandstones based on the mixture theory,which could be employed to estimate the UWC as an empirical method.All methods demonstrated that the UWC initially exhibited a rapid decrease from 0C to5C and then generally became constant beyond20C.However,these test methods had different characteristics.The NMR method was used to directly and accurately calculate the UWC in the laboratory.However,the cost and complexity of NMR equipment have precluded its use in the field.The UWC can be effectively estimated by the MIP test,but the estimation accuracy is influenced by the ice crystallization process and the pore size distribution.The P-wave velocity has been demonstrated to be a straightforward and practical empirical parameter and was utilized to estimate the UWC based on the mixture theory.This method may be more suitable in the field.All methods confirmed the existence of a hysteresis phenomenon in the freezing-thawing process.The average hysteresis coefficient was approximately 0.538,thus validating the GibbseThomson equation.This study not only presents alternative methodologies for estimating the UWC of saturated sandstones but also contribute to our understanding of the freezing-thawing process of pore water.展开更多
Based on data from a petrochemical company’s MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation c...Based on data from a petrochemical company’s MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables,which included properties of feedstock oil and spent catalyst,operational variables,and material flows.The distillation range variables were reduced using factor analysis,and the feedstock oils were clustered into three types using the K-means++algorithm.Each feedstock oil type was then used as an input variable for modeling.An XGBoost model and a back propagation(BP)neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene,as well as the coke yield.In the test set,the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48%and 0.738,respectively,compared to the XGBoost model.It was therefore chosen for further optimization work.The genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke yield.Seven commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke yield.These results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42377191)Hubei Provincial Natural Science Foundation of China(Grant No.2021CFA094)“The 14th Five Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(Grant No.2023A0303)。
文摘The unfrozenwater content(UWC)is a crucial parameter that affects the strength and thermal properties of rocks in relation to engineering construction and geological disasters in cold regions.In this study,three different methods were employed to test and estimate the UWC of saturated sandstones,including nuclear magnetic resonance(NMR),mercury intrusion porosimetry(MIP),and ultrasonic methods.The NMR method enabled the direct measurement of the UWC of sandstones using the free induction decay(FID).The MIP method was used to analyze the pore structures of sandstones,with the UWC subsequently calculated based on pore ice crystallization.Therefore,the MIP test constituted an indirect measurement method.Furthermore,a correlation was established between the P-wave velocity and the UWC of these sandstones based on the mixture theory,which could be employed to estimate the UWC as an empirical method.All methods demonstrated that the UWC initially exhibited a rapid decrease from 0C to5C and then generally became constant beyond20C.However,these test methods had different characteristics.The NMR method was used to directly and accurately calculate the UWC in the laboratory.However,the cost and complexity of NMR equipment have precluded its use in the field.The UWC can be effectively estimated by the MIP test,but the estimation accuracy is influenced by the ice crystallization process and the pore size distribution.The P-wave velocity has been demonstrated to be a straightforward and practical empirical parameter and was utilized to estimate the UWC based on the mixture theory.This method may be more suitable in the field.All methods confirmed the existence of a hysteresis phenomenon in the freezing-thawing process.The average hysteresis coefficient was approximately 0.538,thus validating the GibbseThomson equation.This study not only presents alternative methodologies for estimating the UWC of saturated sandstones but also contribute to our understanding of the freezing-thawing process of pore water.
基金the National Natural Science Foundation of China(No.U22B20141)the SINOPEC funded project(No.31900000-21-ZC0607-0009).
文摘Based on data from a petrochemical company’s MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables,which included properties of feedstock oil and spent catalyst,operational variables,and material flows.The distillation range variables were reduced using factor analysis,and the feedstock oils were clustered into three types using the K-means++algorithm.Each feedstock oil type was then used as an input variable for modeling.An XGBoost model and a back propagation(BP)neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene,as well as the coke yield.In the test set,the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48%and 0.738,respectively,compared to the XGBoost model.It was therefore chosen for further optimization work.The genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke yield.Seven commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke yield.These results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.
