Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization
This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperatu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media Deutschland GmbH
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113285/ http://psasir.upm.edu.my/id/eprint/113285/1/113285.pdf |
| _version_ | 1848867731257950208 |
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| author | Thivaly, Diffa Althafania Setyawan, Hendrix Yulis Mohd Yusoff, Mohd Zulkhairi Mohamed, Mohd Shamzi Ahmad Farid, Mohammed Abdillah |
| author_facet | Thivaly, Diffa Althafania Setyawan, Hendrix Yulis Mohd Yusoff, Mohd Zulkhairi Mohamed, Mohd Shamzi Ahmad Farid, Mohammed Abdillah |
| author_sort | Thivaly, Diffa Althafania |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperature, and concentrations of the activating agent, KOH, the outcomes offer relevant insights that could be leveraged to maximize biochar production for tailored applications. This study stands out for its innovative use of Artificial Neural Network (ANN) approaches for predictive modeling. Fifty datasets, supplemented with secondary data obtained from the literature and experiments, were utilized for the purposes of training, testing, and validating the neural network model. Here, the datasets were processed utilizing the Deep Neural Network (DNN) framework, which was designed and implemented with the minimal loss function framework feasible. The architectural configuration comprises the following; an input layer, four hidden layers (128-neuron dense layer, batch normalization, and 64-neuron dense layer, batch normalization), a dropout layer, and an output layer. With an R2 of 0.8238 for biochar yield and 0.7324 for iodine number, the trained DNN model showed a relatively high degree of accuracy in making predictions. |
| first_indexed | 2025-11-15T14:16:31Z |
| format | Article |
| id | upm-113285 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:41:09Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1132852025-10-27T00:57:07Z http://psasir.upm.edu.my/id/eprint/113285/ Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization Thivaly, Diffa Althafania Setyawan, Hendrix Yulis Mohd Yusoff, Mohd Zulkhairi Mohamed, Mohd Shamzi Ahmad Farid, Mohammed Abdillah This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperature, and concentrations of the activating agent, KOH, the outcomes offer relevant insights that could be leveraged to maximize biochar production for tailored applications. This study stands out for its innovative use of Artificial Neural Network (ANN) approaches for predictive modeling. Fifty datasets, supplemented with secondary data obtained from the literature and experiments, were utilized for the purposes of training, testing, and validating the neural network model. Here, the datasets were processed utilizing the Deep Neural Network (DNN) framework, which was designed and implemented with the minimal loss function framework feasible. The architectural configuration comprises the following; an input layer, four hidden layers (128-neuron dense layer, batch normalization, and 64-neuron dense layer, batch normalization), a dropout layer, and an output layer. With an R2 of 0.8238 for biochar yield and 0.7324 for iodine number, the trained DNN model showed a relatively high degree of accuracy in making predictions. Springer Science and Business Media Deutschland GmbH 2024-09-20 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/113285/1/113285.pdf Thivaly, Diffa Althafania and Setyawan, Hendrix Yulis and Mohd Yusoff, Mohd Zulkhairi and Mohamed, Mohd Shamzi and Ahmad Farid, Mohammed Abdillah (2024) Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization. Environmental Monitoring and Assessment, 196 (10). art. no. 962. pp. 1-17. ISSN 0167-6369; eISSN: 1573-2959 https://link.springer.com/article/10.1007/s10661-024-13119-7 10.1007/s10661-024-13119-7 |
| spellingShingle | Thivaly, Diffa Althafania Setyawan, Hendrix Yulis Mohd Yusoff, Mohd Zulkhairi Mohamed, Mohd Shamzi Ahmad Farid, Mohammed Abdillah Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization |
| title | Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization |
| title_full | Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization |
| title_fullStr | Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization |
| title_full_unstemmed | Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization |
| title_short | Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization |
| title_sort | activated biochar production from young coconut waste (cocos nucifera) as bioadsorbent: a pathway through artificial neural network (ann) optimization |
| url | http://psasir.upm.edu.my/id/eprint/113285/ http://psasir.upm.edu.my/id/eprint/113285/ http://psasir.upm.edu.my/id/eprint/113285/ http://psasir.upm.edu.my/id/eprint/113285/1/113285.pdf |