Non-equilibrium modeling of CO2 reactive-absorption process using sodium hydroxide–ammonia–water solution in a packed bed column

Ghaemi, Ahad; Hemmati, Alireza; Mashhadimoslem, Hossein

doi:10.1007/s13738-021-02190-3

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…GO has different applications, primarily as an adsorbent, due to its high porosity, heightened surface area, and superior chemical stability, supporting several reactive functional groups, such as hydroxyl, epoxy, and carboxyl 16 , 17 . Furthermore, GO is employed as an energy transformation and storage material for nanoscale engineering 18 .…”

Section: Introductionmentioning

confidence: 99%

Intelligent prediction models based on machine learning for CO2 capture performance by graphene oxide-based adsorbents

Fathalian

Aarabi

Ghaemi

et al. 2022

Sci Rep

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Designing a model to connect CO2 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 CO2 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 CO2 adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO2 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 CO2 adsorption capacity. The best performance was obtained for ANN based on MLP method (R2 > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, β1 = 0.9, β2 = 0.999, epochs = 1971, and batch size = 32. To investigate CO2 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 CO2 adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO2 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 CO2 separation, aiding in screening adsorbents for cleaner manufacturing.

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Section: Introductionmentioning

confidence: 99%

Intelligent prediction models based on machine learning for CO2 capture performance by graphene oxide-based adsorbents

Fathalian

Aarabi

Ghaemi

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Experimental and Thermodynamic Study on new CO2 absorbents

Zhou

Liu

et al. 2022

Carbon Capture Science & Technology

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Study on Dense Phase Separation of Associated Gas with High Carbon Dioxide Content

Jing,

Zhang,

Wang

et al. 2024

Processes

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With the continuous exploitation of offshore natural gas, the content of CO2 produced gradually increases. It is not economical to separate more CO2 from natural gas after transportation, and more CO2 will aggravate the corrosion of pipelines. The commonly used decarburization process is not suitable for offshore platforms, and there are problems of high energy consumption and large space occupation. Therefore, dense phase separation of associated gas with high carbon dioxide content is a better separation method. In this paper, the equation of state is optimized by comparing the experimental and CO2 system phase characteristics simulation. Based on the selected equation of state (EOS), a three-level separation model of phase equilibrium characteristics is established. The separation efficiency is simulated to complete the separation of CO2 and methane. The separation process is optimized by a genetic algorithm, and the temperature and pressure under the best separation efficiency are determined. The PR-EOS was selected as the equation with the highest calculation accuracy. Through process simulation and algorithm optimization, the best separation efficiency was 72.23%.

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Non-equilibrium modeling of CO2 reactive-absorption process using sodium hydroxide–ammonia–water solution in a packed bed column

Cited by 3 publications

References 33 publications

Intelligent prediction models based on machine learning for CO2 capture performance by graphene oxide-based adsorbents

Intelligent prediction models based on machine learning for CO2 capture performance by graphene oxide-based adsorbents

Experimental and Thermodynamic Study on new CO2 absorbents

Study on Dense Phase Separation of Associated Gas with High Carbon Dioxide Content

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