Cargando…
Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents
Designing a model to connect CO(2) adsorption data with various adsorbents based on graphene oxide (GO) which is produced from various forms of solid biomass, can be a promising method to develop novel and efficient adsorbents for CO(2) adsorption application. In this work, the information of severa...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747901/ https://www.ncbi.nlm.nih.gov/pubmed/36513731 http://dx.doi.org/10.1038/s41598-022-26138-6 |
_version_ | 1784849706374922240 |
---|---|
author | Fathalian, Farnoush Aarabi, Sepehr Ghaemi, Ahad Hemmati, Alireza |
author_facet | Fathalian, Farnoush Aarabi, Sepehr Ghaemi, Ahad Hemmati, Alireza |
author_sort | Fathalian, Farnoush |
collection | PubMed |
description | Designing a model to connect CO(2) adsorption data with various adsorbents based on graphene oxide (GO) which is produced from various forms of solid biomass, can be a promising method to develop novel and efficient adsorbents for CO(2) adsorption application. In this work, the information of several GO-based solid sorbents were extracted from 17 articles aimed to develop a machine learning based model for CO(2) adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO(2) uptake capacity was defined as model response, alsoseven different models, including support vector machine, gradient boosting, random forest, artificial neural network (ANN) based on multilayer perceptron (MLP) and radial basis function (RBF), Extra trees regressor and extreme gradient boosting, were employed to estimate the CO(2) adsorption capacity. The best performance was obtained for ANN based on MLP method (R(2) > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, β(1) = 0.9, β(2) = 0.999, epochs = 1971, and batch size = 32. To investigate CO(2) uptake dependency on mentioned effective parameters, three dimensional diagrams were reported based on MLP network, also the MLP network characteristics including weight and bias matrices were reported for further application of CO(2) adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO(2) removal efficiency as the target based on adsorbent properties to pick more efficient adsorbents without increasing processing time. Current work employed statistical analysis and machine learning to support the logical design of porous GO for CO(2) separation, aiding in screening adsorbents for cleaner manufacturing. |
format | Online Article Text |
id | pubmed-9747901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97479012022-12-15 Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents Fathalian, Farnoush Aarabi, Sepehr Ghaemi, Ahad Hemmati, Alireza Sci Rep Article Designing a model to connect CO(2) adsorption data with various adsorbents based on graphene oxide (GO) which is produced from various forms of solid biomass, can be a promising method to develop novel and efficient adsorbents for CO(2) adsorption application. In this work, the information of several GO-based solid sorbents were extracted from 17 articles aimed to develop a machine learning based model for CO(2) adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO(2) uptake capacity was defined as model response, alsoseven different models, including support vector machine, gradient boosting, random forest, artificial neural network (ANN) based on multilayer perceptron (MLP) and radial basis function (RBF), Extra trees regressor and extreme gradient boosting, were employed to estimate the CO(2) adsorption capacity. The best performance was obtained for ANN based on MLP method (R(2) > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, β(1) = 0.9, β(2) = 0.999, epochs = 1971, and batch size = 32. To investigate CO(2) uptake dependency on mentioned effective parameters, three dimensional diagrams were reported based on MLP network, also the MLP network characteristics including weight and bias matrices were reported for further application of CO(2) adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO(2) removal efficiency as the target based on adsorbent properties to pick more efficient adsorbents without increasing processing time. Current work employed statistical analysis and machine learning to support the logical design of porous GO for CO(2) separation, aiding in screening adsorbents for cleaner manufacturing. Nature Publishing Group UK 2022-12-13 /pmc/articles/PMC9747901/ /pubmed/36513731 http://dx.doi.org/10.1038/s41598-022-26138-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Fathalian, Farnoush Aarabi, Sepehr Ghaemi, Ahad Hemmati, Alireza Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents |
title | Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents |
title_full | Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents |
title_fullStr | Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents |
title_full_unstemmed | Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents |
title_short | Intelligent prediction models based on machine learning for CO(2) capture performance by graphene oxide-based adsorbents |
title_sort | intelligent prediction models based on machine learning for co(2) capture performance by graphene oxide-based adsorbents |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747901/ https://www.ncbi.nlm.nih.gov/pubmed/36513731 http://dx.doi.org/10.1038/s41598-022-26138-6 |
work_keys_str_mv | AT fathalianfarnoush intelligentpredictionmodelsbasedonmachinelearningforco2captureperformancebygrapheneoxidebasedadsorbents AT aarabisepehr intelligentpredictionmodelsbasedonmachinelearningforco2captureperformancebygrapheneoxidebasedadsorbents AT ghaemiahad intelligentpredictionmodelsbasedonmachinelearningforco2captureperformancebygrapheneoxidebasedadsorbents AT hemmatialireza intelligentpredictionmodelsbasedonmachinelearningforco2captureperformancebygrapheneoxidebasedadsorbents |