This paper presents a methodology for calibrating discrete element method input parameters for simulating cohesive materials.The Plackett-Burman method was initially employed to identify the sig-nificant input paramet...This paper presents a methodology for calibrating discrete element method input parameters for simulating cohesive materials.The Plackett-Burman method was initially employed to identify the sig-nificant input parameters.Subsequently,the performances of response surface methodology(RSM),artificial neural networks(ANN),and random forest(RF)models for calibration were compared.The results demonstrated that the random forest model outperformed the two other models,achieving an RMSE of 1.89,an R-squared of 94%,and an MAE of 1.63.The ANN model followed closely,with an RMSE of 3.12,an R-squared of 89%,and an MAE of 2.18,while the RSM model exhibited lower performance with an RMSE of 6.84,an R-squared of 86%,and an MAE of 5.41.This study presents a framework for enhancing the accuracy of DEM simulations.Finally,the robustness and adaptability of the calibration approach were demonstrated by applying calibrated parameters from one particle size to another.展开更多
A double paddle blender's flow patterns and mixing mechanisms were analyzed using discrete element method(DEM)and experiments.The mixing performance of this type of the blender containing bi-disperse particles has...A double paddle blender's flow patterns and mixing mechanisms were analyzed using discrete element method(DEM)and experiments.The mixing performance of this type of the blender containing bi-disperse particles has been rarely studied in the literature.Plackett-Burman design of experiments(DoE)methodology was used to calibrate the DEM input parameters.Subsequently,the impact of the particle number ratio,vessel fill level,and paddle rotational speed on mixing performance was investigated using the calibrated DEM model.The mixing performance was assessed using relative standard deviation and segregation intensity.Mixing performance was significantly affected by the paddle rotational speed and particle number ratio.Moreover,the Peclet number and diffusivity coefficient were used to evaluate the mixing mechanism in the blender.Results revealed that the diffusion was the predominant mixing mechanism,and the best mixing performance was observed when the diffusivity coefficients of 3 mm and 5 mm particles were almost equal.展开更多
Industrial mixers for powders and granular materials operate with no effective control of mixture quality and lack scientific design. The last twenty years have seen growth in understanding of mixing and mixers. Howev...Industrial mixers for powders and granular materials operate with no effective control of mixture quality and lack scientific design. The last twenty years have seen growth in understanding of mixing and mixers. However, research falls far short of what is needed for on-line characterisation of mixture quality. Secondly, although theoretical descriptions of a few mixer types have been reported, these fall far short of what is needed for equipment design. Two thrusts could revolutionise this situation. One is a scientific characterisation of mixer structure applicable to industrial scale as well as laboratory scale equipment; this is now within our grasp using digital imaging. The other is the development of ideas to overcome the restricted number of particles that can be used in the Distinct Element Method （DEM） for mixers. The goal should be to take the designer through a sequence of steps to the most appropriate mixer size, configuration and operating conditions for a given process duty.展开更多
This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation a...This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEM simulation.The main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the process.The investigation also illustrates the significant influence of the mixing time on the mixing quality.To gain a deeper understanding of the DEM simulation findings,an analytical tool called multivariate polynomial regression in machine learning is employed.This method uncovers significant connections between the DEM results and the operational parameters,providing a more comprehensive insight into their interrelationships.The multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual values.The model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.展开更多
The mixing performance of a multi-bladed baffle inserted into a traditional Gallay tote blender is explored by graphic processing unit-based discrete element method software. The mixing patterns and rates are investig...The mixing performance of a multi-bladed baffle inserted into a traditional Gallay tote blender is explored by graphic processing unit-based discrete element method software. The mixing patterns and rates are investigated for a binary mixture, represented by two different colors, under several loading profiles. The baffle effectively enhances the convective mixing both in the axial and radial directions, because of the disturbance it causes to the initial flowing layer and solid-body zone, compared with a blender without a baffle. The axial mixing rate is affected by the gap between the baffle and the wall on the left and right sides, and an optimal blade length corresponds to the maximum mixing rate. However, the radial mixing rate increases with the blade length almost monotonically.展开更多
基金support of the Natural Sciences and Engineering Research Council of Canada(NSERC)is gratefully acknowledged.
文摘This paper presents a methodology for calibrating discrete element method input parameters for simulating cohesive materials.The Plackett-Burman method was initially employed to identify the sig-nificant input parameters.Subsequently,the performances of response surface methodology(RSM),artificial neural networks(ANN),and random forest(RF)models for calibration were compared.The results demonstrated that the random forest model outperformed the two other models,achieving an RMSE of 1.89,an R-squared of 94%,and an MAE of 1.63.The ANN model followed closely,with an RMSE of 3.12,an R-squared of 89%,and an MAE of 2.18,while the RSM model exhibited lower performance with an RMSE of 6.84,an R-squared of 86%,and an MAE of 5.41.This study presents a framework for enhancing the accuracy of DEM simulations.Finally,the robustness and adaptability of the calibration approach were demonstrated by applying calibrated parameters from one particle size to another.
文摘A double paddle blender's flow patterns and mixing mechanisms were analyzed using discrete element method(DEM)and experiments.The mixing performance of this type of the blender containing bi-disperse particles has been rarely studied in the literature.Plackett-Burman design of experiments(DoE)methodology was used to calibrate the DEM input parameters.Subsequently,the impact of the particle number ratio,vessel fill level,and paddle rotational speed on mixing performance was investigated using the calibrated DEM model.The mixing performance was assessed using relative standard deviation and segregation intensity.Mixing performance was significantly affected by the paddle rotational speed and particle number ratio.Moreover,the Peclet number and diffusivity coefficient were used to evaluate the mixing mechanism in the blender.Results revealed that the diffusion was the predominant mixing mechanism,and the best mixing performance was observed when the diffusivity coefficients of 3 mm and 5 mm particles were almost equal.
文摘Industrial mixers for powders and granular materials operate with no effective control of mixture quality and lack scientific design. The last twenty years have seen growth in understanding of mixing and mixers. However, research falls far short of what is needed for on-line characterisation of mixture quality. Secondly, although theoretical descriptions of a few mixer types have been reported, these fall far short of what is needed for equipment design. Two thrusts could revolutionise this situation. One is a scientific characterisation of mixer structure applicable to industrial scale as well as laboratory scale equipment; this is now within our grasp using digital imaging. The other is the development of ideas to overcome the restricted number of particles that can be used in the Distinct Element Method （DEM） for mixers. The goal should be to take the designer through a sequence of steps to the most appropriate mixer size, configuration and operating conditions for a given process duty.
基金the Natural Sciences and Engineering Research Council of Canada(grant No.RGPIN-2019-04644)is gratefully acknowledged.
文摘This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEM simulation.The main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the process.The investigation also illustrates the significant influence of the mixing time on the mixing quality.To gain a deeper understanding of the DEM simulation findings,an analytical tool called multivariate polynomial regression in machine learning is employed.This method uncovers significant connections between the DEM results and the operational parameters,providing a more comprehensive insight into their interrelationships.The multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual values.The model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.
基金This work was supported by the National Key Basic Research Program of China under Grant No. 2015CB251402, the National Natural Science Foundation of China under Grant Nos. 21206167, 21225628, 91434201, and 91334204, the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA07080203, and CAS Interdisciplinary Innovation Team.
文摘The mixing performance of a multi-bladed baffle inserted into a traditional Gallay tote blender is explored by graphic processing unit-based discrete element method software. The mixing patterns and rates are investigated for a binary mixture, represented by two different colors, under several loading profiles. The baffle effectively enhances the convective mixing both in the axial and radial directions, because of the disturbance it causes to the initial flowing layer and solid-body zone, compared with a blender without a baffle. The axial mixing rate is affected by the gap between the baffle and the wall on the left and right sides, and an optimal blade length corresponds to the maximum mixing rate. However, the radial mixing rate increases with the blade length almost monotonically.
