Artificial neural network model for the flow regime recognition in the drying of guava pieces in the spouted bed

Veloso, Yago Matheus da Silva; Almeida, Marcello Maia de; Alsina, Odelsia Leonor Sánchez de; Leite, Manuela Souza

doi:10.1080/00986445.2019.1608192

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…This showed that PSO-ANN had a better prediction ability in comparison to the standalone ANN, whereas GA-ANN outperformed both neural models in prediction capability. GA-ANN outperformed ANN in modeling the drying process of guava pieces [26], whereas PSO-ANN was shown to outperform ANN in the recognition of citrus fruits [41].…”

Section: Development Of Pso-annmentioning

confidence: 99%

“…When increasing the number of particles from 20 to 100, the cost function value rose, albeit at a slower rate. The use of a large number of particles in PSO optimization has resulted in comparably low MSE values, such as in the modeling study of guava drying using PSO and ANN [26]. Using a large number of particles did not improve the accuracy of the PSO based on the optimization of anaerobic wastewater treatment [38].…”

Section: Effect Of Pso Parameters On Fitness Value and Optimization Processmentioning

confidence: 99%

“…PSO has been used in food-related optimization; for example, in modelling the mastication of white rice [25]. The integration of PSO and ANN has been utilized in the drying of guava pieces [26] as the use of optimization techniques led to a significant improvement in the ANN. The study showed that the optimized neural network employing PSO obtained almost 99% recognition accuracy in terms of R 2 values, whereas the ANN models achieved 86%.…”

Section: Introductionmentioning

confidence: 99%

“…10, x FOR PEER REVIEW 11 of 14guava pieces[26], whereas PSO-ANN was shown to outperform ANN in the recognition of citrus fruits[41].…”

mentioning

confidence: 99%

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Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk

Ming

Anuar

How

et al. 2021

Foods

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Spray drying techniques are one of the methods to preserve and extend the shelf-life of coconut milk. The objective of this research was to create a particle swarm optimization–enhanced artificial neural network (PSO–ANN) that could predict the coconut milk spray drying process. The parameters for PSO tuning were selected as the number of particles and acceleration constant, respectively, for both global and personal best using a 2k factorial design. The optimal PSO settings were recorded as global best, C1 = 4.0; personal best, C2 = 0; and number of particles = 100. When comparing different types of spray drying models, PSO–ANN had an MSE value of 0.077, GA–ANN had an MSE of 0.033, while ANN had an MSE of 0.082. Sensitivity analysis was conducted on all three models to evaluate the significance level of each parameter on the model, and it was discovered that inlet temperature had the most significant influence on the model performance. In conclusion, the PSO–ANN was found to be more effective than ANN but less effective than GA–ANN in predicting the quality of coconut milk powder.

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Section: Development Of Pso-annmentioning

confidence: 99%

Section: Effect Of Pso Parameters On Fitness Value and Optimization Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…10, x FOR PEER REVIEW 11 of 14guava pieces[26], whereas PSO-ANN was shown to outperform ANN in the recognition of citrus fruits[41].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk

Ming

Anuar

How

et al. 2021

Foods

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“…Artificial neural networks (ANN's), one of the dominant techniques in the field of machine learning, is an important branch of artificial intelligence, presenting characteristics of selfadaptation, self-organization and self-learning. (Han et al, 2018, Viana et al, 2018, Veloso et al, 2019, Veloso et al, 2020. Application of ANN-based models for monitoring oil or derivatives may require large numbers of sensors due to the complexity of oil analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied as an in-situ monitoring technique for the water content in oil recovered by means of NIR spectroscopy

Costa¹,

Goncalves²,

Neta³

et al. 2021

S. F. J. of Dev.

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The present work proposes a study on the combination of artificial neural network based model and multivariate calibration techniques, as principais component analysis (PCA) and partial least squares (PLS) combined with near infrared spectrophotometer (NIR) to formulate online water content monitoring models for recovered fuel oils after effluent treatment of a Brazilian thermoelectric plant. The database for adjustments of these models was built using oil samples supplied by the SUAPE II thermoelectric plant, where they were characterized in laboratories and analyzed via NIR. 450 spectra were used to construct the PLS model for predictive model calibration using the PLS technique and 118 spectra for the model based on artificiais neurais networks. In the obtained PLS model, it was possible to obtain correlations around 0.97, cross-validation error (RMSECV) of 0.0906 and test prediction error (RMSEP) of 0.0651. The RNA model presented coefficient of determination R² of 0.99 for training and R² of 0.98 for test with prediction error of 0.062

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Mixing evolution behavior of raw and gasified biomass pellets in a fluidized bed reactor

Wang

Song

Jarolin

et al. 2022

Chemical Engineering Science

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Artificial neural network model for the flow regime recognition in the drying of guava pieces in the spouted bed

Cited by 7 publications

References 29 publications

Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk

Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk

Machine learning applied as an in-situ monitoring technique for the water content in oil recovered by means of NIR spectroscopy

Mixing evolution behavior of raw and gasified biomass pellets in a fluidized bed reactor

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