Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment
Lignocellulosic biomass, particularly softwoods such as pine, poses a significant challenge to enzymatic hydrolysis due to its high lignin content and complex structural rigidity. Although the application of steam explosion and alkaline pretreatment has gained widespread popularity for enhancing dig...
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North Carolina State University
2025-08-01
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| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24822 |
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| author | Hyeon Cheol Kim Si Young Ha Jae-Kyung Yang |
| author_facet | Hyeon Cheol Kim Si Young Ha Jae-Kyung Yang |
| author_sort | Hyeon Cheol Kim |
| collection | DOAJ |
| description | Lignocellulosic biomass, particularly softwoods such as pine, poses a significant challenge to enzymatic hydrolysis due to its high lignin content and complex structural rigidity. Although the application of steam explosion and alkaline pretreatment has gained widespread popularity for enhancing digestibility, the optimization of process parameters remains a formidable challenge due to the nonlinear interactions among variables. Machine learning is emerging as a promising solution to address these challenges, offering a viable alternative for predictive modeling and process control. In this study, an artificial neural network (ANN) model was developed to predict the enzymatic hydrolysis rate of steam-exploded pine wood subjected to mild alkaline (NaOH) pretreatment. The artificial neural network (ANN) was trained on experimental data encompassing three primary process variables: steam explosion time (1 to 5 min), NaOH concentration (0.5 to 2.0%), and chemical pretreatment time (12 to 24 h). The artificial neural network (ANN) model demonstrated the highest level of accuracy among the models evaluated, including random forest, support vector machine, and extreme gradient boosting. It attained a coefficient of determination (R²) of 0.9805. In conditions that were not optimized (1% NaOH, 24-hour treatment, 5 min steam explosion, without bark), a maximum hydrolysis of 93.9% was obtained. |
| format | Article |
| id | doaj-art-f3fbe7af955e4f7795e8fe292f1e7011 |
| institution | Matheson Library |
| issn | 1930-2126 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | North Carolina State University |
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| series | BioResources |
| spelling | doaj-art-f3fbe7af955e4f7795e8fe292f1e70112025-08-04T19:59:23ZengNorth Carolina State UniversityBioResources1930-21262025-08-01204840084193170Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline PretreatmentHyeon Cheol Kim0https://orcid.org/0000-0003-0997-6926Si Young Ha1https://orcid.org/0000-0002-2832-5830Jae-Kyung Yang2https://orcid.org/0000-0002-9482-1584Department of Environmental Materials Science/Institute of Agriculture and Life Science, Gyeongsang National University, Jinju, 52828, Republic of KoreaDepartment of Environmental Materials Science/Institute of Agriculture and Life Science, Gyeongsang National University, Jinju, 52828, Republic of KoreaDepartment of Environmental Materials Science/Institute of Agriculture and Life Science, Gyeongsang National University, Jinju, 52828, Republic of KoreaLignocellulosic biomass, particularly softwoods such as pine, poses a significant challenge to enzymatic hydrolysis due to its high lignin content and complex structural rigidity. Although the application of steam explosion and alkaline pretreatment has gained widespread popularity for enhancing digestibility, the optimization of process parameters remains a formidable challenge due to the nonlinear interactions among variables. Machine learning is emerging as a promising solution to address these challenges, offering a viable alternative for predictive modeling and process control. In this study, an artificial neural network (ANN) model was developed to predict the enzymatic hydrolysis rate of steam-exploded pine wood subjected to mild alkaline (NaOH) pretreatment. The artificial neural network (ANN) was trained on experimental data encompassing three primary process variables: steam explosion time (1 to 5 min), NaOH concentration (0.5 to 2.0%), and chemical pretreatment time (12 to 24 h). The artificial neural network (ANN) model demonstrated the highest level of accuracy among the models evaluated, including random forest, support vector machine, and extreme gradient boosting. It attained a coefficient of determination (R²) of 0.9805. In conditions that were not optimized (1% NaOH, 24-hour treatment, 5 min steam explosion, without bark), a maximum hydrolysis of 93.9% was obtained.https://ojs.bioresources.com/index.php/BRJ/article/view/24822artificial neural networkalkaline pretreatmentenzymatic hydrolysispine woodsteam explosion |
| spellingShingle | Hyeon Cheol Kim Si Young Ha Jae-Kyung Yang Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment BioResources artificial neural network alkaline pretreatment enzymatic hydrolysis pine wood steam explosion |
| title | Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment |
| title_full | Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment |
| title_fullStr | Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment |
| title_full_unstemmed | Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment |
| title_short | Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment |
| title_sort | artificial neural network approach for predicting enzymatic hydrolysis of steam exploded pine wood chip in mild alkaline pretreatment |
| topic | artificial neural network alkaline pretreatment enzymatic hydrolysis pine wood steam explosion |
| url | https://ojs.bioresources.com/index.php/BRJ/article/view/24822 |
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