Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network
Background: The relationships between the density of the biomass pellet and the related variables are very complicated and highly nonlinear, which make developing a single, general, and accurate mathematical model almost impossible. One of the most appropriate methods to solve these problems is the...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
OICC Press
2024-02-01
|
| Series: | International Journal of Recycling of Organic Waste in Agriculture |
| Subjects: | |
| Online Access: | https://oiccpress.com/ijrowa/article/view/3072 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832569140789903360 |
|---|---|
| author | Abedin Zafari Mohammad Hossein Kianmehr Rahman Abdolahzadeh |
| author_facet | Abedin Zafari Mohammad Hossein Kianmehr Rahman Abdolahzadeh |
| author_sort | Abedin Zafari |
| collection | DOAJ |
| description | Background: The relationships between the density of the biomass pellet and the related variables are very complicated and highly nonlinear, which make developing a single, general, and accurate mathematical model almost impossible. One of the most appropriate methods to solve these problems is the intelligent method. Shankar and Bandyopadhyay and Shankar et al. successfully used genetic algorithms and artificial neural networks to understand and optimize an extrusion process. Results: The results showed that a four-layer perceptron network with training algorithm of back propagation, hyperbolic tangential activation function, and Delta training rule with ten neurons in the first hidden layer and four neurons in the second hidden layer had the best performance for the prediction of pellet density. The minimum root mean square error and coefficient of determination for the multilayer perceptron network were 0.01732 and 0.972, respectively. Also, the results of statistical analysis indicate that moisture content, speed of piston, and particle size significantly affected (P < 0.01) the density of pellets while the influence of die length was negligible (P > 0.05). Conclusions: The results indicate that a properly trained neural network can be used to predict effect of input variable on pellet density. The ANN model was found to have higher predictive capability than the statistical model. |
| format | Article |
| id | doaj-art-9e11eb5c3e0a4bbabdb93ee46dbbc02b |
| institution | Kabale University |
| issn | 2195-3228 2251-7715 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | OICC Press |
| record_format | Article |
| series | International Journal of Recycling of Organic Waste in Agriculture |
| spelling | doaj-art-9e11eb5c3e0a4bbabdb93ee46dbbc02b2025-02-02T23:06:32ZengOICC PressInternational Journal of Recycling of Organic Waste in Agriculture2195-32282251-77152024-02-012110.1186/2251-7715-2-9Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural networkAbedin Zafari0Mohammad Hossein Kianmehr1Rahman Abdolahzadeh2University of TehranUniversity of TehranUniversity of TehranBackground: The relationships between the density of the biomass pellet and the related variables are very complicated and highly nonlinear, which make developing a single, general, and accurate mathematical model almost impossible. One of the most appropriate methods to solve these problems is the intelligent method. Shankar and Bandyopadhyay and Shankar et al. successfully used genetic algorithms and artificial neural networks to understand and optimize an extrusion process. Results: The results showed that a four-layer perceptron network with training algorithm of back propagation, hyperbolic tangential activation function, and Delta training rule with ten neurons in the first hidden layer and four neurons in the second hidden layer had the best performance for the prediction of pellet density. The minimum root mean square error and coefficient of determination for the multilayer perceptron network were 0.01732 and 0.972, respectively. Also, the results of statistical analysis indicate that moisture content, speed of piston, and particle size significantly affected (P < 0.01) the density of pellets while the influence of die length was negligible (P > 0.05). Conclusions: The results indicate that a properly trained neural network can be used to predict effect of input variable on pellet density. The ANN model was found to have higher predictive capability than the statistical model.https://oiccpress.com/ijrowa/article/view/3072Extrusion parameters. Biomass pellet. Density. Artificial neural network, , , , , , , , , , |
| spellingShingle | Abedin Zafari Mohammad Hossein Kianmehr Rahman Abdolahzadeh Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network International Journal of Recycling of Organic Waste in Agriculture Extrusion parameters. Biomass pellet. Density. Artificial neural network, , , , , , , , , , |
| title | Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network |
| title_full | Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network |
| title_fullStr | Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network |
| title_full_unstemmed | Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network |
| title_short | Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network |
| title_sort | modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network |
| topic | Extrusion parameters. Biomass pellet. Density. Artificial neural network, , , , , , , , , , |
| url | https://oiccpress.com/ijrowa/article/view/3072 |
| work_keys_str_mv | AT abedinzafari modelingtheeffectofextrusionparametersondensityofbiomasspelletusingartificialneuralnetwork AT mohammadhosseinkianmehr modelingtheeffectofextrusionparametersondensityofbiomasspelletusingartificialneuralnetwork AT rahmanabdolahzadeh modelingtheeffectofextrusionparametersondensityofbiomasspelletusingartificialneuralnetwork |