Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field-Reference-Cited by-同舟云学术

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Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field

Published:2024-04 Issue: Volume:293 Page:130413
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Naghizadeh Arefeh,Jafari Saeed,Norouzi-Apourvari Saied,Schaffie Mahin,Hemmati-Sarapardeh Abdolhossein

Publisher

Elsevier BV

Reference61 articles.

1. Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework;You;Appl Energy,2020

2. Site selection for carbon dioxide geological storage using analytic network process;Hsu;Separ Purif Technol,2012

3. Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms;Amar;J Petrol Sci Eng,2021

4. Experimental investigation of the flue gas thermochemical composition of an oxy-fuel swirl burner;Bürkle;Fuel,2018

5. Feasibility evaluation of CO2 EOR and storage in tight oil reservoirs: a demonstration project in the Ordos Basin;Ren;Fuel,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection;Energy;2024-10

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