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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Veröffentlicht in:Journal of environmental chemical engineering Jg. 12; H. 2; S. 112210
Hauptverfasser: Ewees, Ahmed A., Vo Thanh, Hung, Al-qaness, Mohammed A.A., Abd Elaziz, Mohamed, Samak, Ahmed H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2024
Schlagworte:
CCUS
CO2
Marine predators algorithm
Seagull optimization algorithm
Seagull optimization algorithm
CCUS
N2
Marine predators algorithm
CO2
ISSN:2213-3437
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Zusammenfassung: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.
ISSN:2213-3437
DOI:10.1016/j.jece.2024.112210