Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project...Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.展开更多
This study focuses on identifying the factors under which mixed microbial seeds assist bio-chemical denitrification when Scrap Iron Filings(SIF)are used as electron donors and adsorbents in low C/N ratio waters.Batch ...This study focuses on identifying the factors under which mixed microbial seeds assist bio-chemical denitrification when Scrap Iron Filings(SIF)are used as electron donors and adsorbents in low C/N ratio waters.Batch studies were conducted in abiotic and biotic reactors containing fresh and aged SIF under different dissolved oxygen concentrations with NO_(3)^(-)−N and/or PO_(4)^(3−)influent(s)and their nitrate/phosphate removal and by-product formations were studied.Batch reactors were seeded with a homogenized mixed microbial inoculum procured from natural sludges which were enriched over 6 months under denitrifying conditions in the presence of SIF.Results indicated that when influent containing 40 mg/L of NO_(3)^(-)−N was treated with 5 g SIF,79.9%nitrate reduction was observed in 8 days abiotically and 100%removal was accomplished in 20 days when the reactor was seeded.Both abiotic and seeded reactors removed more than 92%PO_(4)^(3−)under high DO conditions in 12 days.Abiotic and biochemical removal of NO_(3)^(-)−N and abiotic removal of PO_(4)^(3−)were higher under independent NO_(3)^(-)−N/PO_(4)^(3−)loading,while 99%PO_(4)^(3−)was removed biochemically under combined NO_(3)^(-)−N and PO_(4)^(3−)loading.This study furthers the understandings of nitrate and phosphate removal in Zero Valent Iron(ZVI)assisted mixed microbial systems to encourage the application of SIF-supported bio-chemical processes in the simultaneous removals of these pollutants.展开更多
基金State Key Laboratory of Hydroscience and Hydraulic Engineering of Tsinghua University,Grant/Award Number:2019-KY-03Key Technology of Intelligent Construction of Urban Underground Space of North China University of Technology,Grant/Award Number:110051360022XN108-19+3 种基金Research Start-up Fund Project of North China University of Technology,Grant/Award Number:110051360002Yujie Project of North China University of Technology,Grant/Award Number:216051360020XN199/006National Natural Science Foundation of China,Grant/Award Numbers:51522903,51774184National Key R&D Program of China,Grant/Award Numbers:2018YFC1504801,2018YFC1504902。
文摘Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.
基金We are grateful for the project grants supported by the Major Science and Technology Programs for Water Pollution Control and Management of China(Nos.2012ZX07205-001 and 2017ZX7103-007).
文摘This study focuses on identifying the factors under which mixed microbial seeds assist bio-chemical denitrification when Scrap Iron Filings(SIF)are used as electron donors and adsorbents in low C/N ratio waters.Batch studies were conducted in abiotic and biotic reactors containing fresh and aged SIF under different dissolved oxygen concentrations with NO_(3)^(-)−N and/or PO_(4)^(3−)influent(s)and their nitrate/phosphate removal and by-product formations were studied.Batch reactors were seeded with a homogenized mixed microbial inoculum procured from natural sludges which were enriched over 6 months under denitrifying conditions in the presence of SIF.Results indicated that when influent containing 40 mg/L of NO_(3)^(-)−N was treated with 5 g SIF,79.9%nitrate reduction was observed in 8 days abiotically and 100%removal was accomplished in 20 days when the reactor was seeded.Both abiotic and seeded reactors removed more than 92%PO_(4)^(3−)under high DO conditions in 12 days.Abiotic and biochemical removal of NO_(3)^(-)−N and abiotic removal of PO_(4)^(3−)were higher under independent NO_(3)^(-)−N/PO_(4)^(3−)loading,while 99%PO_(4)^(3−)was removed biochemically under combined NO_(3)^(-)−N and PO_(4)^(3−)loading.This study furthers the understandings of nitrate and phosphate removal in Zero Valent Iron(ZVI)assisted mixed microbial systems to encourage the application of SIF-supported bio-chemical processes in the simultaneous removals of these pollutants.
