Mass flow rate prediction of screw conveyor using artificial neural network method

Screw conveyors are widely used in granular transportation to provide an efficient and steady flow rate. DEM is a numerical method used to predict flow behaviors of granular material effectively. However, this method is computationally intensive. In this work, an artificial network model was trained...

Full description

Saved in:
Bibliographic Details
Published in:Powder technology 2022-08, Vol.408, p.117757, Article 117757
Main Authors: Kalay, Eren, Boğoçlu, Muharrem Erdem, Bolat, Berna
Format: Article
Language:English
Subjects:
Artificial neural network
Design of experiments
Discrete element method
Mass flow rate
Multi-sphere
Screw conveyor
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Screw conveyors are widely used in granular transportation to provide an efficient and steady flow rate. DEM is a numerical method used to predict flow behaviors of granular material effectively. However, this method is computationally intensive. In this work, an artificial network model was trained using DEM simulation results to reduce computational cost while keeping the estimation accuracy. The main drawback of this technique is that it requires large number of data which is time consuming when a series of DEM simulation results with varying parameters are used to train the network mainly for screw conveyor applications. To get beyond this limitation, the DOE approach was used to optimize ANN parameters by performing significantly fewer virtual experiments. Moreover, the effect of particle shape on mass flow rate was also considered using single and clumped spheres. The trained artificial neural network was able to predict mass flow rate accurately and highly efficiently by taking into account both screw conveyor related parameters and granular particle related parameters as inputs. For validation of ANN, experimental tests were performed using polypropylene granular material. The findings showed that the proposed model by ANN was also in a good agreement with the experimental data for horizontal screw conveyor. [Display omitted] •Proposing an ANN structure to calculate mass flow rate.•Optimization of ANN parameters by performing DOE approach.•Considering conveyor and particle relater parameters in ANN model.•Validation of network using polypropylene granular.
ISSN:0032-5910
1873-328X
DOI:10.1016/j.powtec.2022.117757