Enhanced CO2 capture using KOH‑functionalized oil palm ash adsorbent : experimental and applied machine learning approach
This work aimed to develop a cost-effective, environmentally friendly, and sustainable adsorbent for CO2 capture using biomass waste, specifically oil palm ash (OPA), which is abundantly available as agricultural waste in Malaysia. Pristine OPA was first subjected to acid washing, followed by carb...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Springer Nature
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/49205/1/Saleh%20et%20al.%20%282025%29.pdf http://ir.unimas.my/id/eprint/49205/ https://link.springer.com/article/10.1007/s44246-025-00227-3 https://doi.org/10.1007/s44246-025-00227-3 |
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| Summary: | This work aimed to develop a cost-effective, environmentally friendly, and sustainable adsorbent for CO2 capture using
biomass waste, specifically oil palm ash (OPA), which is abundantly available as agricultural waste in Malaysia. Pristine OPA was first subjected to acid washing, followed by carbonization and chemical activation with KOH to enhance
its physicochemical properties. Since machine learning (ML) can provide new insights into the design of adsorption
processes and the understanding of CO2 adsorption mechanism, this paper presents a detailed comparative analysis
of experimental and ML approaches. In this study, the surface and pore characteristics of OPA-KOH(1:2) were significantly enhanced through carbonization and KOH-functionalization, achieving an optimized mesoporous structure (average pore size: 72.71 Å), with a surface area of 30.95 m2 /g. Despite having a moderate surface area, the tailored pore structure promoted efficient CO2 diffusion, thus enabling high CO2 adsorption capacity of 2.9 mmol/g, which was comparable or exceeds that some Acs with higher surface areas. Systematic analyses of adsorption isotherms,
kinetics, and thermodynamics have confirmed that the CO2 adsorption onto OPA-KOH(1:2) occurred exothermically
and was predominantly driven by a physisorption mechanism, supported by a weak chemisorption mechanism. MLbased models employed in this study have demonstrated that the bilayered neural network (NN) model could accurately predict CO2 adsorption onto OPA-KOH(1:2), with exceptionally high accuracy of R2>0.99. These findings have provided valuable insights into the capture, conversion, utilization, and storage (CCUS) technology, while highlighting OPA-KOH(1:2) as an affordable and environmentally friendly adsorbent for effective CO2 capture. |
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