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

Fandi Zeng, Limin Liu, Yinzeng Liu, Hongbin Bai, Chunxiao Li, Zhihuan Zhao

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Fandi ZengCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan, 250100, China.

Limin LiuCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan, 250100, China.

Yinzeng LiuCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan, 250100, China.

Hongbin BaiCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China.

Chunxiao LiCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan, 250100, China.

Zhihuan ZhaoCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan, 250100, China. zzh2023129@126.com.

Funding

Shandong Agriculture and Engineering University Start-Up Fund for Talented Scholars 2024GCCZR-05the Innovative Research Team in SDAEU sgykycxtd2020-03the integrated pilot project of agricultural machinery Research and Development, Maufacturing and Application in Shandong Province NJYTHSD-202311

6 · The paper itself

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 R

Indexed as

Organic fertilizer particlesPhysical propertyRepose angleThe discrete element modelThe PSO − BP algorithm

Identifiers

PMID40665085

DOI10.1038/s41598-025-11827-9

PMCPMC12263875

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.