# Experimental study on DEM parameters calibration for organic fertilizer by the particle swarm optimization − backpropagation neural networks

## TL;DR

This study uses a combination of simulations and machine learning to accurately predict the repose angle of organic fertilizer particles.

## Contribution

A novel PSO-BP neural network approach is proposed for calibrating DEM parameters of organic fertilizer.

## Key findings

- The PSO-BP algorithm achieved better fitting and higher accuracy in predicting the repose angle.
- Optimal parameter combinations were identified, including CORO−p 0.35 and COSO−O 0.49.
- PSO-BP outperformed GA-BP and RSM regression models in terms of R2MAE and RMSE.

## Abstract

In order to calibrate the properties of the organic fertilizer particles, this work employs an integrated strategy that combines simulations, machine vision techniques, and physical experiments. Through physical testing, the fundamental physical characteristics of the organic fertilizer particles were identified. The initial analysis was through the Plackett-Burman test. The parameters that greatly influence the angle of repose are established. The previously identified important variables were optimized by the Central Composite Design test. The regression fitting models of the BP neural network have been developed from the data set derived from the Central Composite Design test results. Genetic algorithms (GA) and particle swarm optimization algorithms (PSO) were used to optimize the BP neural network. The R2MAE and RMSE of the BP, GA − BP, PSO − BP and RSM regression models were compared and analyzed. The results showed that PSO − BP algorithm could achieve better fitting effect, and could construct a prediction model with higher accuracy and less error to analyze the repose angle of the organic fertilizer particles. The PSO − BP algorithm was used to iterate until the individual with the closest fitness was obtained. CORO−p was 0.35, COSO−O was 0.49, COSO−p was 0.29 and CODO−O was 0.38 were the optimal parameter combination.