Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming
The application of a versatile approach for modeling and prediction the moisture content of dried peels was evaluated using both empirical and semi-empirical equations (Lewis, Page, Henderson and Pabis, Modified Page, Logarithmic, and Modified Logistic) as well as machine learning models (K-nearest...
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| Format: | Article |
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Elsevier
2025-01-01
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| Series: | South African Journal of Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1026918524001203 |
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| author | Maressa O. Camilo Romero F. Carvalho Ariany B.S. Costa Esly F.C. Junior Andréa O.S. Costa Robson C. Sousa |
| author_facet | Maressa O. Camilo Romero F. Carvalho Ariany B.S. Costa Esly F.C. Junior Andréa O.S. Costa Robson C. Sousa |
| author_sort | Maressa O. Camilo |
| collection | DOAJ |
| description | The application of a versatile approach for modeling and prediction the moisture content of dried peels was evaluated using both empirical and semi-empirical equations (Lewis, Page, Henderson and Pabis, Modified Page, Logarithmic, and Modified Logistic) as well as machine learning models (K-nearest neighbor | KNN, Decision Tree | DT, Artificial Neural Network | ANN and Support Vector Regression | SVR). Heuristic optimization methods, including genetic algorithms (GA) and nonlinear programming (NLP), were employed to identify the best empirical and semi-empirical models for estimating moisture content during the drying process of lemon peel layers. The parameters of the drying kinetics models were optimized using GA to achieve the best results. It was found that as the number of model parameters increases, particularly in models such as the logarithmic one, the optimization problem becomes more complex. Consequently, accurate initial guesses become increasingly important, emphasizing the need for heuristic methods like genetic algorithms. This optimization approach provided excellent performance metrics (R2 > 0.9715, SSR 〈 0.0625 and MSE < 0.0026 for endocarp and R2 〉 0.9678, SSR < 0.0755 and MSE < 0.0030 for epicarp). The models proposed in this study achieved the best results with the modified logistic equation (R2 > 0.9923, MSE 〈 0.0001 and SSR < 0.0013 for endocarp and R2 〉 0.9905, MSE < 0.0001 and SSR < 0.0013 for epicarp). In particular, the multilayer perceptron neural network of the machine learning proved to be the optimal choice as it best accounts for the complexity of the drying kinetics of lemons. This neural network model outperformed traditional empirical and semi-empirical models, demonstrating superior performance metrics (R2 > 0.9979, MSE 〈 0.0002 and SSR < 0.0012 for endocarp and R2 〉 0.9989, MSE < 0.0001 and SSR < 0.0008 for epicarp) when tested against validation data. |
| format | Article |
| id | doaj-art-9229a602c03d46f5b1f34a984ddb0b1c |
| institution | Kabale University |
| issn | 1026-9185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | South African Journal of Chemical Engineering |
| spelling | doaj-art-9229a602c03d46f5b1f34a984ddb0b1c2025-01-19T06:24:14ZengElsevierSouth African Journal of Chemical Engineering1026-91852025-01-0151136152Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programmingMaressa O. Camilo0Romero F. Carvalho1Ariany B.S. Costa2Esly F.C. Junior3Andréa O.S. Costa4Robson C. Sousa5Laboratory of Transport Phenomena and Unit Operations (LaFTOP), Center of Agricultural Sciences and Engineering, Federal University of Espírito Santo, Alto Universitário, 29500-000, Guararema, Alegre, ES, BrazilLaboratory of Transport Phenomena and Unit Operations (LaFTOP), Center of Agricultural Sciences and Engineering, Federal University of Espírito Santo, Alto Universitário, 29500-000, Guararema, Alegre, ES, BrazilLaboratory of Transport Phenomena and Unit Operations (LaFTOP), Center of Agricultural Sciences and Engineering, Federal University of Espírito Santo, Alto Universitário, 29500-000, Guararema, Alegre, ES, BrazilChemical Engineering Program, Federal University of Minas Gerais, Av. Presidente Antônio Carlos, 6627, 31270-901, Belo Horizonte, Minas Gerais, MG, BrazilChemical Engineering Program, Federal University of Minas Gerais, Av. Presidente Antônio Carlos, 6627, 31270-901, Belo Horizonte, Minas Gerais, MG, BrazilLaboratory of Transport Phenomena and Unit Operations (LaFTOP), Center of Agricultural Sciences and Engineering, Federal University of Espírito Santo, Alto Universitário, 29500-000, Guararema, Alegre, ES, Brazil; Corresponding author..The application of a versatile approach for modeling and prediction the moisture content of dried peels was evaluated using both empirical and semi-empirical equations (Lewis, Page, Henderson and Pabis, Modified Page, Logarithmic, and Modified Logistic) as well as machine learning models (K-nearest neighbor | KNN, Decision Tree | DT, Artificial Neural Network | ANN and Support Vector Regression | SVR). Heuristic optimization methods, including genetic algorithms (GA) and nonlinear programming (NLP), were employed to identify the best empirical and semi-empirical models for estimating moisture content during the drying process of lemon peel layers. The parameters of the drying kinetics models were optimized using GA to achieve the best results. It was found that as the number of model parameters increases, particularly in models such as the logarithmic one, the optimization problem becomes more complex. Consequently, accurate initial guesses become increasingly important, emphasizing the need for heuristic methods like genetic algorithms. This optimization approach provided excellent performance metrics (R2 > 0.9715, SSR 〈 0.0625 and MSE < 0.0026 for endocarp and R2 〉 0.9678, SSR < 0.0755 and MSE < 0.0030 for epicarp). The models proposed in this study achieved the best results with the modified logistic equation (R2 > 0.9923, MSE 〈 0.0001 and SSR < 0.0013 for endocarp and R2 〉 0.9905, MSE < 0.0001 and SSR < 0.0013 for epicarp). In particular, the multilayer perceptron neural network of the machine learning proved to be the optimal choice as it best accounts for the complexity of the drying kinetics of lemons. This neural network model outperformed traditional empirical and semi-empirical models, demonstrating superior performance metrics (R2 > 0.9979, MSE 〈 0.0002 and SSR < 0.0012 for endocarp and R2 〉 0.9989, MSE < 0.0001 and SSR < 0.0008 for epicarp) when tested against validation data.http://www.sciencedirect.com/science/article/pii/S1026918524001203DryingTahiti lemonEmpirical and semi-empirical modelsMachine learningGenetic algorithms |
| spellingShingle | Maressa O. Camilo Romero F. Carvalho Ariany B.S. Costa Esly F.C. Junior Andréa O.S. Costa Robson C. Sousa Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming South African Journal of Chemical Engineering Drying Tahiti lemon Empirical and semi-empirical models Machine learning Genetic algorithms |
| title | Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming |
| title_full | Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming |
| title_fullStr | Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming |
| title_full_unstemmed | Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming |
| title_short | Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming |
| title_sort | drying kinetic for moisture content prediction of peels tahiti lemon citrus latifolia approach by machine learning and optimization genetic algorithms and nonlinear programming |
| topic | Drying Tahiti lemon Empirical and semi-empirical models Machine learning Genetic algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S1026918524001203 |
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