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

Ahmed A. Ewees, Hung Vo Thanh*, Mohammed A.A. Al-qaness*, Mohamed Abd Elaziz, Ahmed H. Samak

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号112210
ジャーナルJournal of Environmental Chemical Engineering
12
2
DOI
出版ステータスPublished - 2024 4月
外部発表はい

ASJC Scopus subject areas

  • 化学工学(その他)
  • 廃棄物管理と処理
  • 汚染
  • プロセス化学およびプロセス工学

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