The prediction of CO2 adsorption on rice husk activated carbons via deep learning neural network

Palle, Kishor; Vunguturi, Shanthi; Gayatri, Sambhani Naga; Rao, K. Subba; Babu, P. Ramesh; Vijay, R.

doi:10.1557/s43579-022-00197-2

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…The role of different characteristics, that is, pyridinic nitrogen (N-6), pyrolytic nitrogen (N-5), oxidized nitrogen (N-X), graphitic nitrogen (N-Q), and the fraction of N-6/N-X, of N-containing functional groups was identified, evidencing that N-6, N-5, and N-X considerably functioned in the CO 2 capture. The CO 2 capture by rice husk derived activated carbons was studied by a NN-based algorithm, while only physical properties like pore volumes and surface areas of activated carbons were considered in this work due to the limited accessibility of data (Palle et al, 2022). It can be clearly found that, for carbonaceous materials, of which the derivation of CO 2 capture performance from theoretical calculation is relatively difficult due to the structural complexity of activated carbons, the high-throughput screening on their CO 2 selectivity is unpractical at the current stage.…”

Section: Frontiers In Energy Researchmentioning

confidence: 99%

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

et al. 2023

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Effective carbon dioxide (CO2) capture plays indispensable roles in closing the global carbon cycle, serving the sustainable production of energy, and achieving the grand 1.5 °C goal by 2050. Considering the diversity and complexity of CO2 capture materials, machine learning has stepped into this field years ago and become a powerful tool that promotes the screening and design of involving parameters. From these perspectives, this critical review firstly summarizes the technical backgrounds for the applications of ML-based methods in CO2 capture. Then, through categorizing the materials into two major groups, that is, adsorbents (containing metal organic frameworks, carbonaceous materials, polymers, and zeolites) and absorbents (involving ionic liquids, amine-based absorbents, and deep eutectic solvents), the applications of this effective tool in relevant areas are scrutinized. The major concerns remain to be further addressed are derived based on the above discussions, namely 1) the development of consistent and integrated databases, 2) the wise digitalization of inherent properties of materials, and 3) the validation of the accuracy of ML-derived results under practical scenarios. The main purpose of this critical review is bridging the previous achievements and further developments of ML-assisted design of CO2 capture techniques.

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Section: Frontiers In Energy Researchmentioning

confidence: 99%