Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model...

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Vydáno v:Journal of environmental chemical engineering Ročník 12; číslo 2; s. 112210
Hlavní autoři: Ewees, Ahmed A., Vo Thanh, Hung, Al-qaness, Mohammed A.A., Abd Elaziz, Mohamed, Samak, Ahmed H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.04.2024
Témata:
CCUS
CO2
Marine predators algorithm
Seagull optimization algorithm
Seagull optimization algorithm
CCUS
N2
Marine predators algorithm
CO2
ISSN:2213-3437
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Abstract Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets. [Display omitted] •Emphasizes the importance of precise viscosity assessment for CCUS and diverse industries.•Aims to create a smart model for predicting CO2-N2 viscosity.•Focuses on improving the dendritic neural regression (DNR) framework.•Introduces a novel Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for model enhancement.•Offers unprecedented insights into CO2-N2 viscosity prediction.•Promises a breakthrough with great potential for CCUS projects and various industries.
AbstractList Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets. [Display omitted] •Emphasizes the importance of precise viscosity assessment for CCUS and diverse industries.•Aims to create a smart model for predicting CO2-N2 viscosity.•Focuses on improving the dendritic neural regression (DNR) framework.•Introduces a novel Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for model enhancement.•Offers unprecedented insights into CO2-N2 viscosity prediction.•Promises a breakthrough with great potential for CCUS projects and various industries.
ArticleNumber 112210
Author Ewees, Ahmed A.
Vo Thanh, Hung
Samak, Ahmed H.
Abd Elaziz, Mohamed
Al-qaness, Mohammed A.A.
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  organization: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
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  givenname: Mohamed
  surname: Abd Elaziz
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  givenname: Ahmed H.
  surname: Samak
  fullname: Samak, Ahmed H.
  email: eng_ahmed_samak@yahoo.co.uk
  organization: Faculty of Science, Menofia University, Shibeen El-Kom, Egypt
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CCUS
N2
Marine predators algorithm
CO2
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Snippet Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of...
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SubjectTerms CCUS
CO2
Marine predators algorithm
Seagull optimization algorithm
Title Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage
URI https://dx.doi.org/10.1016/j.jece.2024.112210
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