In order to acquire the flow pattern and investigate the settling behavior of the red mud in the separation thickener,computational fluid dynamics(CFD),custom subroutines and agglomerates settling theory were employed...In order to acquire the flow pattern and investigate the settling behavior of the red mud in the separation thickener,computational fluid dynamics(CFD),custom subroutines and agglomerates settling theory were employed to simulate the three-dimensional flow field in an industrial scale thickener with the introduction of a self-dilute feed system.The simulation results show good agreement with the measurement onsite and the flow patterns of the thickener are presented and discussed on both velocity and concentration field.Optimization experiments on feed well and self-dilute system were also carried out,and indicate that the optimal thickener system can dilute the solid concentration in feed well from 110 g/L to 86 g/L which would help the agglomerates' formation and improve the red mud settling speed.Furthermore,the additional power of recirculation pump can be saved and flocculants dosage was reduced from 105g/t to 85g/t in the operation.展开更多
Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a ...Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.展开更多
基金Project(50876116)supported by the National Natural Science Foundation of China
文摘In order to acquire the flow pattern and investigate the settling behavior of the red mud in the separation thickener,computational fluid dynamics(CFD),custom subroutines and agglomerates settling theory were employed to simulate the three-dimensional flow field in an industrial scale thickener with the introduction of a self-dilute feed system.The simulation results show good agreement with the measurement onsite and the flow patterns of the thickener are presented and discussed on both velocity and concentration field.Optimization experiments on feed well and self-dilute system were also carried out,and indicate that the optimal thickener system can dilute the solid concentration in feed well from 110 g/L to 86 g/L which would help the agglomerates' formation and improve the red mud settling speed.Furthermore,the additional power of recirculation pump can be saved and flocculants dosage was reduced from 105g/t to 85g/t in the operation.
基金Projects(2016YFE0200100,2018YFC1505300-5.3)supported by the National Key Research&Development Plan of ChinaProject(51639002)supported by the Key Program of National Natural Science Foundation of China
文摘Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.
