Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will im...Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will impact on the flow characteristics, and subsequently affect the electrostatic type of flow measurements. This study investigates this phenomenon using both experimental and numerical meth- ods. Particle degradation was induced experimentally by recursively conveying the fillite material within a pneumatic pipeline. The associated particle size reduction was monitored. Three electrostatic sensors were embedded along the pipeline to monitor the flow. The results indicated a decreasing trend in the electrostatic sensor outputs with decreasing particle size, which suggested the attenuation of the flow velocity fluctuation. This trend was more apparent at higher conveying velocities, which suggested that more severe particle degradation occurred under these conditions. Coupled computational fluid dynamics and discrete element methods （CFD-DEM） analysis was used to qualitatively validate these experimental results. The numerical results suggested that smaller particles exhibited lower flow velocity fluctua- tions, which was consistent with the observed experimental results. These findings provide important information for the accurate aoolication of electrostatic measurement devices in oneumatic conveyors.展开更多
In this paper,modeling and machine learning with experimental data and a novel calibration function for a gas-solid flow sensor fusion are presented.Sensor fusion is the use of software that intelligently combines dat...In this paper,modeling and machine learning with experimental data and a novel calibration function for a gas-solid flow sensor fusion are presented.Sensor fusion is the use of software that intelligently combines data from multiple sensors in order to improve overall system performance.This technique can be applied to measurement of mass flow rate of solids in a pipeline with non-intrusive electrostatic techniques.Data fusion from multiple heterogeneous/homogenous sensors can overcome the limitations of an individual sensor and measured variable.It is shown that the output voltage of a ring-shaped electrode is predominantly a function of solids mass flow rate and velocity for a flow of bulk solids in a pipeline,when particle size,properties and ambient conditions remain constant.By incorporating solids flow velocity in a proposed mathematical model(obtained via machine learning),meter output voltage could be predicted with superior accuracy,for wide range of different flow pa-rameters from numerous experiments with a pneumatic conveying system.Transposing the model yields a new calibration function which,when deployed in signal processing software,enables accurate mass flow measure-ment with velocity compensation.The described method also de-necessitates determination of air solids ratio or solids volumetric concentration,thereby enabling simplification of the overall measurement system whilst yielding higher accuracy than calibration methods from previous studies.Accurate flow measurement facilitates enhanced monitoring and controllability of blast furnaces,power stations,chemical reactors,process plants etc.where there are bulk solids flows in pipelines.Optimization of such highly materially consumptive and energy intensive processes can yield significant reductions in waste and emissions(CO 2,NOx)and increased efficiencies in global production of energy and materials.展开更多
文摘Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will impact on the flow characteristics, and subsequently affect the electrostatic type of flow measurements. This study investigates this phenomenon using both experimental and numerical meth- ods. Particle degradation was induced experimentally by recursively conveying the fillite material within a pneumatic pipeline. The associated particle size reduction was monitored. Three electrostatic sensors were embedded along the pipeline to monitor the flow. The results indicated a decreasing trend in the electrostatic sensor outputs with decreasing particle size, which suggested the attenuation of the flow velocity fluctuation. This trend was more apparent at higher conveying velocities, which suggested that more severe particle degradation occurred under these conditions. Coupled computational fluid dynamics and discrete element methods （CFD-DEM） analysis was used to qualitatively validate these experimental results. The numerical results suggested that smaller particles exhibited lower flow velocity fluctua- tions, which was consistent with the observed experimental results. These findings provide important information for the accurate aoolication of electrostatic measurement devices in oneumatic conveyors.
文摘In this paper,modeling and machine learning with experimental data and a novel calibration function for a gas-solid flow sensor fusion are presented.Sensor fusion is the use of software that intelligently combines data from multiple sensors in order to improve overall system performance.This technique can be applied to measurement of mass flow rate of solids in a pipeline with non-intrusive electrostatic techniques.Data fusion from multiple heterogeneous/homogenous sensors can overcome the limitations of an individual sensor and measured variable.It is shown that the output voltage of a ring-shaped electrode is predominantly a function of solids mass flow rate and velocity for a flow of bulk solids in a pipeline,when particle size,properties and ambient conditions remain constant.By incorporating solids flow velocity in a proposed mathematical model(obtained via machine learning),meter output voltage could be predicted with superior accuracy,for wide range of different flow pa-rameters from numerous experiments with a pneumatic conveying system.Transposing the model yields a new calibration function which,when deployed in signal processing software,enables accurate mass flow measure-ment with velocity compensation.The described method also de-necessitates determination of air solids ratio or solids volumetric concentration,thereby enabling simplification of the overall measurement system whilst yielding higher accuracy than calibration methods from previous studies.Accurate flow measurement facilitates enhanced monitoring and controllability of blast furnaces,power stations,chemical reactors,process plants etc.where there are bulk solids flows in pipelines.Optimization of such highly materially consumptive and energy intensive processes can yield significant reductions in waste and emissions(CO 2,NOx)and increased efficiencies in global production of energy and materials.
