An accurate correlation for estimation the pH of CO2-saturated solutions: Implications for CO2 sequestration, By Reza Azin

Research

Title	An accurate correlation for estimation the pH of CO2-saturated solutions: Implications for CO2 sequestration
Type	Presentation
Keywords	pH estimation CO2 sequestration Ocean Machine learning
Researchers	Mohammad Rasul Dehghani Firuzabadi (First researcher) , Moien Kafi (Second researcher) , Yousef Kazemzadeh (Third researcher) , Reza Azin (Fourth researcher)

Abstract

CO2 sequestration, particularly through ocean injection, is a key method for reducing atmospheric CO2 due to the oceans' capacity. However, this raises concerns about water pH changes. This study addresses gaps in previous research by employing a large, diverse dataset for accurate pH prediction under various conditions. Data on temperature, pressure, salinity, and pH of NaCl solutions saturated with CO2 were collected. Pressure showed the highest correlation with pH, while temperature had the lowest. The data was divided into training and testing sets to develop a Gene Expression Programming (GEP) model. Error metrics for training and testing sets showed R² values of 0.9037 and 0.9035, and RMSE values of 0.1172 and 0.1182. Residual error plots indicated good performance across pressure and salinity ranges. Sensitivity analysis confirmed pressure had the highest influence on pH output. The model provides valuable insights for predicting pH changes in CO2-saturated NaCl solutions, aiding CO2 sequestration efforts and environmental studies.