From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue
This study investigates the anaerobic co-digestion of cow manure (CM) and molasses residue (MR), focusing on the impact of various support carriers on reactor performance and machine learning model predictions. BMP tests identified a 50:50 CM:MR ratio as optimal for methane production, yielding the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/119694/ http://psasir.upm.edu.my/id/eprint/119694/1/119694.pdf |
| _version_ | 1848868037640323072 |
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| author | Jaman, Khairina Idrus, Syazwani Harun, Razif Nik Daud, Nik Norsyahariati Rehan, Balqis Mohamed Ahsan, Amimul Zamrisham, Ain Fitriah |
| author_facet | Jaman, Khairina Idrus, Syazwani Harun, Razif Nik Daud, Nik Norsyahariati Rehan, Balqis Mohamed Ahsan, Amimul Zamrisham, Ain Fitriah |
| author_sort | Jaman, Khairina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study investigates the anaerobic co-digestion of cow manure (CM) and molasses residue (MR), focusing on the impact of various support carriers on reactor performance and machine learning model predictions. BMP tests identified a 50:50 CM:MR ratio as optimal for methane production, yielding the highest biogas production (1540 mL), SMP (45.05 mLCH₄/gVSadded), and VS removal (51.4 %). Semi-continuous experiments were conducted with support carriers—lava rock (LR), nanoparticles (NPs), biochar (BC), and synthetic grass (SG), under mesophilic conditions with the 50:50 CM:MR ratio and organic loading rates of 1–6 gVS/L/day for 100 days. LR showed the best performance, producing the highest biogas (170 mL), SMP (22.5 mL CH₄/gVSadded), and VS removal (59.8 %). Compared to other support carriers, LR exhibited the largest pore size at 53.7 nm (92 % larger than BC and 88.6 % larger than NPs), which significantly enhanced nutrient diffusion and microbial accessibility. Machine learning models, including ANN and SVM, were developed from BMP data, with SVM showing superior predictive accuracy (R² = 0.84373) compared to ANN (R² = 0.71367). SEM and EPS analyses revealed a higher microbial population on LR than on BC. These results suggest LR's large pore size make it a promising support carrier for improving AD performance. |
| first_indexed | 2025-11-15T14:46:01Z |
| format | Article |
| id | upm-119694 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:46:01Z |
| publishDate | 2025 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1196942025-09-08T04:44:55Z http://psasir.upm.edu.my/id/eprint/119694/ From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue Jaman, Khairina Idrus, Syazwani Harun, Razif Nik Daud, Nik Norsyahariati Rehan, Balqis Mohamed Ahsan, Amimul Zamrisham, Ain Fitriah This study investigates the anaerobic co-digestion of cow manure (CM) and molasses residue (MR), focusing on the impact of various support carriers on reactor performance and machine learning model predictions. BMP tests identified a 50:50 CM:MR ratio as optimal for methane production, yielding the highest biogas production (1540 mL), SMP (45.05 mLCH₄/gVSadded), and VS removal (51.4 %). Semi-continuous experiments were conducted with support carriers—lava rock (LR), nanoparticles (NPs), biochar (BC), and synthetic grass (SG), under mesophilic conditions with the 50:50 CM:MR ratio and organic loading rates of 1–6 gVS/L/day for 100 days. LR showed the best performance, producing the highest biogas (170 mL), SMP (22.5 mL CH₄/gVSadded), and VS removal (59.8 %). Compared to other support carriers, LR exhibited the largest pore size at 53.7 nm (92 % larger than BC and 88.6 % larger than NPs), which significantly enhanced nutrient diffusion and microbial accessibility. Machine learning models, including ANN and SVM, were developed from BMP data, with SVM showing superior predictive accuracy (R² = 0.84373) compared to ANN (R² = 0.71367). SEM and EPS analyses revealed a higher microbial population on LR than on BC. These results suggest LR's large pore size make it a promising support carrier for improving AD performance. Elsevier 2025 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/119694/1/119694.pdf Jaman, Khairina and Idrus, Syazwani and Harun, Razif and Nik Daud, Nik Norsyahariati and Rehan, Balqis Mohamed and Ahsan, Amimul and Zamrisham, Ain Fitriah (2025) From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue. Biochemical Engineering Journal, 224. art. no. 109904. ISSN 1369-703X; eISSN: 1873-295X https://linkinghub.elsevier.com/retrieve/pii/S1369703X25002785 10.1016/j.bej.2025.109904 |
| spellingShingle | Jaman, Khairina Idrus, Syazwani Harun, Razif Nik Daud, Nik Norsyahariati Rehan, Balqis Mohamed Ahsan, Amimul Zamrisham, Ain Fitriah From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| title | From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| title_full | From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| title_fullStr | From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| title_full_unstemmed | From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| title_short | From macrostructure to machine learning: Lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| title_sort | from macrostructure to machine learning: lava rock as a superior carrier in anaerobic co-digestion of manure and molasses residue |
| url | http://psasir.upm.edu.my/id/eprint/119694/ http://psasir.upm.edu.my/id/eprint/119694/ http://psasir.upm.edu.my/id/eprint/119694/ http://psasir.upm.edu.my/id/eprint/119694/1/119694.pdf |