Sodium silicate（NazSiO3） was used to improve the elution of super heavy oil from weathered soil on an ultrasound-enhanced elution system by the solution containing 0-6000 mg/L surfactant Triton X-100. The removal ex...Sodium silicate（NazSiO3） was used to improve the elution of super heavy oil from weathered soil on an ultrasound-enhanced elution system by the solution containing 0-6000 mg/L surfactant Triton X-100. The removal extent of three markers[C26-34 17a 25-norhopanes, C26-28triaromatic steroids（TAS）, and C27-29methyl triaromatic steroids（MTAS）] was monitored. The average elution percentages of C26-34 norhopanes, C26-28 TAS, and C27-29 MTAS by Triton X-100/Na2SiO3 solutions were increased by 11%-13%, 9%-11% and 8%-13% with increasing Triton X-100 concentrations from 150 mg/L to 6000 mg/L. All the concentrations of Triton X-100 improved the elu- tion of TAS homologs containing fewer carbon atoms, whereas high concentrations improved the elution of larger 17a 25-norhopane and MTAS species. Addition of Na2SiO3 produced a noticeable increase in elution, particularly for lower-weight species. Scanning electron microscope（SEM） images and energy spectroscopy data reveal that surfac- rant solution of 6000 mg/L Triton X-100 and 4000 mg/L Na2SiO3 produced the greatest improvement in the elution of super heavy oil aggregates encapsulating the soil surface and the emulsification of particle dispersions. That is to say mixed solutions of Triton X-100 and Na2SiO3 in combination with ultrasound are a potential means of removing super heavy oil from weathered soils.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.51179001 and 40772146)the Fok Ying Tung Education Foundation,China(No.122041)
文摘Sodium silicate（NazSiO3） was used to improve the elution of super heavy oil from weathered soil on an ultrasound-enhanced elution system by the solution containing 0-6000 mg/L surfactant Triton X-100. The removal extent of three markers[C26-34 17a 25-norhopanes, C26-28triaromatic steroids（TAS）, and C27-29methyl triaromatic steroids（MTAS）] was monitored. The average elution percentages of C26-34 norhopanes, C26-28 TAS, and C27-29 MTAS by Triton X-100/Na2SiO3 solutions were increased by 11%-13%, 9%-11% and 8%-13% with increasing Triton X-100 concentrations from 150 mg/L to 6000 mg/L. All the concentrations of Triton X-100 improved the elu- tion of TAS homologs containing fewer carbon atoms, whereas high concentrations improved the elution of larger 17a 25-norhopane and MTAS species. Addition of Na2SiO3 produced a noticeable increase in elution, particularly for lower-weight species. Scanning electron microscope（SEM） images and energy spectroscopy data reveal that surfac- rant solution of 6000 mg/L Triton X-100 and 4000 mg/L Na2SiO3 produced the greatest improvement in the elution of super heavy oil aggregates encapsulating the soil surface and the emulsification of particle dispersions. That is to say mixed solutions of Triton X-100 and Na2SiO3 in combination with ultrasound are a potential means of removing super heavy oil from weathered soils.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
