Modeling the Chemical Hydrolysis of Mesquite (Prosopis laevigata) Seed Husk Using Response Surface Methodology and Artificial Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Obtaining the Sample
2.2. Chemical Hydrolysis
2.3. Enzymatic Hydrolysis
2.4. Data Adjustment Techniques
2.4.1. Data Adjustment via Response Surface Methodology
2.4.2. Data Adjustment via Artificial Neural Network
2.5. Comparison of RSM and ANN Models
3. Results
3.1. Experimental Values
3.1.1. Effect of Particle Size, Acid, and Reaction Time on Obtaining TRS
3.1.2. Effect of Chemical and Enzymatic Hydrolysis on Mesquite Husk
3.2. Response Surface Methodology
3.3. Artificial Neural Network
3.4. Model Comparison
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No. | Particle Size (A) | Acid (N) (B) | Time (min) (C) | TRS (g/L) Experimental |
---|---|---|---|---|
1 | 30 (0) | 0.25 (0) | 20 (0) | 9.75 |
2 | 16 (−1) | 0 (−1) | 20 (0) | 1.79 |
3 | 50 (+1) | 0 (−1) | 20 (0) | 2.72 |
4 | 16 (−1) | 0.5 (+1) | 20 (0) | 7.72 |
5 | 50 (+1) | 0.5 (+1) | 20 (0) | 16.41 |
6 | 16 (−1) | 0.25 (0) | 10 (−1) | 1.78 |
7 | 50 (+1) | 0.25 (0) | 10 (−1) | 2.29 |
8 | 30 (0) | 0.25 (0) | 20 (0) | 9.91 |
9 | 16 (−1) | 0.25 (0) | 30 (+1) | 5.43 |
10 | 50 (+1) | 0.25 (0) | 30 (+1) | 6.24 |
11 | 30 (0) | 0 (−1) | 10 (−1) | 1.90 |
12 | 30 (0) | 0.5 (+1) | 10 (−1) | 5.92 |
13 | 30 (0) | 0 (−1) | 30 (+1) | 1.72 |
14 | 30 (0) | 0.5 (+1) | 30 (+1) | 15.06 |
15 | 30 (0) | 0.25 (0) | 20 (0) | 9.65 |
Coefficient of Determination r2 | Root Mean Square Error (RMSE) | Mean Absolute Percentage Error (MAPE) | |||
---|---|---|---|---|---|
RSM | ANN | RSM | ANN | RSM | ANN |
0.93 | 0.99 | 1.48 | 0.44 | 26.22% | 3.65% |
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Pérez-Cadena, R.; Vázquez-Maldonado, S.; Téllez-Jurado, A.; Serna-Diaz, M.G.; Medina-Marin, J. Modeling the Chemical Hydrolysis of Mesquite (Prosopis laevigata) Seed Husk Using Response Surface Methodology and Artificial Neural Networks. Appl. Sci. 2025, 15, 1419. https://doi.org/10.3390/app15031419
Pérez-Cadena R, Vázquez-Maldonado S, Téllez-Jurado A, Serna-Diaz MG, Medina-Marin J. Modeling the Chemical Hydrolysis of Mesquite (Prosopis laevigata) Seed Husk Using Response Surface Methodology and Artificial Neural Networks. Applied Sciences. 2025; 15(3):1419. https://doi.org/10.3390/app15031419
Chicago/Turabian StylePérez-Cadena, Rogelio, Silvana Vázquez-Maldonado, Alejandro Téllez-Jurado, Maria Guadalupe Serna-Diaz, and Joselito Medina-Marin. 2025. "Modeling the Chemical Hydrolysis of Mesquite (Prosopis laevigata) Seed Husk Using Response Surface Methodology and Artificial Neural Networks" Applied Sciences 15, no. 3: 1419. https://doi.org/10.3390/app15031419
APA StylePérez-Cadena, R., Vázquez-Maldonado, S., Téllez-Jurado, A., Serna-Diaz, M. G., & Medina-Marin, J. (2025). Modeling the Chemical Hydrolysis of Mesquite (Prosopis laevigata) Seed Husk Using Response Surface Methodology and Artificial Neural Networks. Applied Sciences, 15(3), 1419. https://doi.org/10.3390/app15031419