Carbon Capture and Storage Optimization with Machine Learning using an ANN model

¹ Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russian Federation
² Lovely Professional University, Phagwara, Punjab, India ;
³ Department of CSE, GRIET, Bachupally, Hyderabad, Telangana, India.
⁴ Department of Computer Science & Engineering-Data Science, KG Reddy College of Engineering and Technology, Chilkur(Vil), Moinabad(M), Ranga Reddy(Dist), Hyderabad, 500075, Telangana, India.
⁵ Uttaranchal University, Dehradun - 248007, India
⁶ Centre of Research Impact and Outcome, Chitkara University, Rajpura - 140417, Punjab, India
⁷ Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh - 174103 India
⁸ Department of Chemistry, Research & Incubation Centre, Rayat Bahra University Chandigarh-Ropar NH 205, Greater Mohali, Punjab, 140103, India

^* Corresponding author: ekotov.cfd@gmail.com

Abstract

The purpose of this study is to evaluate the accuracy of predictions regarding the work capacity of CO₂ and the selectivity of MOF, using machine learning methodologies in relation to CO₂/N₂. A dataset was used that includes numerous characteristics of MOFs for the development of a neural network model. The factors that determined the operational capacity of CO₂ and the CO₂/N₂ selectivity included pore size, surface area, chemical composition, among others. The model demonstrated its work capacity by evaluating the selectivity of CO₂; the mean absolute errors for the CO₂/N₂ selectivity were 25 and 0.8 mmol/g, respectively. The correlation Analysis showed a fairly negative correlation (-0.014) between the operational capacity of CO₂ and its chemical makeup and a very positive correlation ( 0.029) between the surface area and amount of pore size. Thus, the gas absorbability is not top-dependent exclusively; pore size and surface area of a material contribute to the capacity as well. More research should be carried out to evaluate a machine learning capability on predicting the nature of different Flow Object Models (MOFs) with an aim of increasing efficiency, precision and dependability of the models.