Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making
The current study examined the tensile properties of epoxy biocomposites reinforced with untreated and NaOH-treated Washingtonia robusta waste (WRW) and biochar considering different fiber weight fractions (10%, 20%, 30%), NaOH concentrations (2%, 2.5%, 3%), and treatment durations (4, 12, 24 h). Th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Journal of Natural Fibers |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2025.2540475 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849229056087687168 |
|---|---|
| author | Messaouda Boumaaza Ahmed Belaadi Hassan Alshahrani Ibrahim M. H. Alshaikh Djamel Ghernaout |
| author_facet | Messaouda Boumaaza Ahmed Belaadi Hassan Alshahrani Ibrahim M. H. Alshaikh Djamel Ghernaout |
| author_sort | Messaouda Boumaaza |
| collection | DOAJ |
| description | The current study examined the tensile properties of epoxy biocomposites reinforced with untreated and NaOH-treated Washingtonia robusta waste (WRW) and biochar considering different fiber weight fractions (10%, 20%, 30%), NaOH concentrations (2%, 2.5%, 3%), and treatment durations (4, 12, 24 h). The potential of WRW/biochar composites for sustainable applications, particularly in the automotive sector, was highlighted. The maximum tensile strength (35.69 MPa) and Young’s modulus (7.67 GPa) were achieved at 30% WRW treated for 4 h with 3% NaOH. These improvements are attributed to better interfacial bonding and fiber-matrix adhesion. To model and optimize the mechanical behavior, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Multi-Criteria Decision-Making (MCDM) method based on TOPSIS were applied. ANN provided higher predictive accuracy (R2 = 0.9993 for tensile strength, 0.9819 for Young’s modulus) compared to RSM. Optimization results indicated ideal conditions of 29.35–29.41% WRW, 11.06–11.24 h treatment time, and 2.99–3% NaOH, based on desirability function RSM and genetic algorithm ANN optimization. The integration of ANN, RSM, and TOPSIS-MCDM provided a comprehensive optimization framework, confirming the potential of WRW/biochar composites for eco-efficient engineering applications, such as in the automotive sector. |
| format | Article |
| id | doaj-art-e1d90768c9f54f919bca3ad0b50ab904 |
| institution | Kabale University |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-e1d90768c9f54f919bca3ad0b50ab9042025-08-22T08:27:16ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2025-12-0122110.1080/15440478.2025.2540475Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-MakingMessaouda Boumaaza0Ahmed Belaadi1Hassan Alshahrani2Ibrahim M. H. Alshaikh3Djamel Ghernaout4LGCH Laboratory, University 8 Mai 1945, Guelma, AlgeriaDepartment of mechanical engineering, Faculty of Technology, University of 20 Août 1955 Skikda, Skikda, AlgeriaDepartment of Mechanical Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, University of Science and Technology, Faculty of Engineering, Sana’a, YemenChemical Engineering Department, College of Engineering, University of Ha’il, Ha’il, Saudi ArabiaThe current study examined the tensile properties of epoxy biocomposites reinforced with untreated and NaOH-treated Washingtonia robusta waste (WRW) and biochar considering different fiber weight fractions (10%, 20%, 30%), NaOH concentrations (2%, 2.5%, 3%), and treatment durations (4, 12, 24 h). The potential of WRW/biochar composites for sustainable applications, particularly in the automotive sector, was highlighted. The maximum tensile strength (35.69 MPa) and Young’s modulus (7.67 GPa) were achieved at 30% WRW treated for 4 h with 3% NaOH. These improvements are attributed to better interfacial bonding and fiber-matrix adhesion. To model and optimize the mechanical behavior, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Multi-Criteria Decision-Making (MCDM) method based on TOPSIS were applied. ANN provided higher predictive accuracy (R2 = 0.9993 for tensile strength, 0.9819 for Young’s modulus) compared to RSM. Optimization results indicated ideal conditions of 29.35–29.41% WRW, 11.06–11.24 h treatment time, and 2.99–3% NaOH, based on desirability function RSM and genetic algorithm ANN optimization. The integration of ANN, RSM, and TOPSIS-MCDM provided a comprehensive optimization framework, confirming the potential of WRW/biochar composites for eco-efficient engineering applications, such as in the automotive sector.https://www.tandfonline.com/doi/10.1080/15440478.2025.2540475Hybrid biocompositestructural compositetensile propertiesexperimental designdesirability function/RSMgenetic algorithm/ANN |
| spellingShingle | Messaouda Boumaaza Ahmed Belaadi Hassan Alshahrani Ibrahim M. H. Alshaikh Djamel Ghernaout Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making Journal of Natural Fibers Hybrid biocomposite structural composite tensile properties experimental design desirability function/RSM genetic algorithm/ANN |
| title | Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making |
| title_full | Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making |
| title_fullStr | Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making |
| title_full_unstemmed | Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making |
| title_short | Modeling and Optimization of Tensile Properties of Epoxy Biocomposites Reinforced with Washingtonia robusta Waste and Biochar Using Response Surface Methodology, Artificial Neural Networks, and Multi-Criteria Decision-Making |
| title_sort | modeling and optimization of tensile properties of epoxy biocomposites reinforced with washingtonia robusta waste and biochar using response surface methodology artificial neural networks and multi criteria decision making |
| topic | Hybrid biocomposite structural composite tensile properties experimental design desirability function/RSM genetic algorithm/ANN |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2025.2540475 |
| work_keys_str_mv | AT messaoudaboumaaza modelingandoptimizationoftensilepropertiesofepoxybiocompositesreinforcedwithwashingtoniarobustawasteandbiocharusingresponsesurfacemethodologyartificialneuralnetworksandmulticriteriadecisionmaking AT ahmedbelaadi modelingandoptimizationoftensilepropertiesofepoxybiocompositesreinforcedwithwashingtoniarobustawasteandbiocharusingresponsesurfacemethodologyartificialneuralnetworksandmulticriteriadecisionmaking AT hassanalshahrani modelingandoptimizationoftensilepropertiesofepoxybiocompositesreinforcedwithwashingtoniarobustawasteandbiocharusingresponsesurfacemethodologyartificialneuralnetworksandmulticriteriadecisionmaking AT ibrahimmhalshaikh modelingandoptimizationoftensilepropertiesofepoxybiocompositesreinforcedwithwashingtoniarobustawasteandbiocharusingresponsesurfacemethodologyartificialneuralnetworksandmulticriteriadecisionmaking AT djamelghernaout modelingandoptimizationoftensilepropertiesofepoxybiocompositesreinforcedwithwashingtoniarobustawasteandbiocharusingresponsesurfacemethodologyartificialneuralnetworksandmulticriteriadecisionmaking |