Experimental study on DEM parameters calibration for organic fertilizer by the particle swarm optimization − backpropagation neural networks
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 we...
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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 25629 - 15 |
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| Jazyk: | angličtina |
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Nature Publishing Group UK
15.07.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| 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
2
MAE 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. COR
O−p
was 0.35, COS
O−O
was 0.49, COS
O−p
was 0.29 and COD
O−O
was 0.38 were the optimal parameter combination. |
|---|---|
| AbstractList | 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. 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 2 MAE 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. COR O−p was 0.35, COS O−O was 0.49, COS O−p was 0.29 and COD O−O was 0.38 were the optimal parameter combination. 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. 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 2 MAE 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. COR O−p was 0.35, COS O−O was 0.49, COS O−p was 0.29 and COD O−O was 0.38 were the optimal parameter combination. 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.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. 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 MAE 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. COR was 0.35, COS was 0.49, COS was 0.29 and COD was 0.38 were the optimal parameter combination. |
| ArticleNumber | 25629 |
| Author | Zhao, Zhihuan Liu, Yinzeng Bai, Hongbin Li, Chunxiao Zeng, Fandi Liu, Limin |
| Author_xml | – sequence: 1 givenname: Fandi surname: Zeng fullname: Zeng, Fandi organization: College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University – sequence: 2 givenname: Limin surname: Liu fullname: Liu, Limin organization: College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University – sequence: 3 givenname: Yinzeng surname: Liu fullname: Liu, Yinzeng organization: College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University – sequence: 4 givenname: Hongbin surname: Bai fullname: Bai, Hongbin organization: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University – sequence: 5 givenname: Chunxiao surname: Li fullname: Li, Chunxiao organization: College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University – sequence: 6 givenname: Zhihuan surname: Zhao fullname: Zhao, Zhihuan email: zzh2023129@126.com organization: College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40665085$$D View this record in MEDLINE/PubMed |
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| Keywords | Physical property Organic fertilizer particles The PSO − BP algorithm Repose angle The discrete element model |
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| Snippet | In order to calibrate the properties of the organic fertilizer particles, this work employs an integrated strategy that combines simulations, machine vision... Abstract In order to calibrate the properties of the organic fertilizer particles, this work employs an integrated strategy that combines simulations, machine... |
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| SubjectTerms | 639/166/988 639/705 Algorithms Calibration Fertilizers Friction Genetic algorithms Horticulture Humanities and Social Sciences Manures Methods Moisture content multidisciplinary Neural networks Optimization techniques Organic fertilizer particles Organic fertilizers Physical characteristics Physical property Prediction models Regression analysis Repose angle Science Science (multidisciplinary) Sheep Simulation Software The discrete element model The PSO − BP algorithm |
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| Title | Experimental study on DEM parameters calibration for organic fertilizer by the particle swarm optimization − backpropagation neural networks |
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