Based on machine learning model for prediction of CO2 adsorption of synthetic zeolite in two-step solid waste treatment

Abstract

The rising environmental issues caused by carbon dioxide emissions and accumulation of industrial solid waste accelerate the development of carbon capture utilization and storage (CCUS), especially the technology using industrial solid waste as a raw material to prepare environmentally friendly and sustainable porous materials to capture CO2. This study developed four models including support vector regression(SVR), multivariate adaptive regression spline(Mars), random forest(RF), and gradient boosting machine(GBM) based on 762 CO2 adsorption datasets of zeolites synthesized from five different industrial solid waste materials to predict the CO2 adsorption capacity and analyze impact of various factors on CO2 adsorption performance during synthesis and adsorption processes. The results suggested that gradient boosting machine(GBM) and the support vector regression(SVR) have good accuracy and generalization performance. The R2 of the model reached 0.99 and 0.96 respectively, which is in good agreement with the laboratory data. In general, the specific surface area(S) and adsorption pressure(P) during the adsorption process of zeolite have a great influence on the final adsorption performance. The correlation between the specific surface area(S) and the hydrothermal reaction temperature(T2) is the largest, and its Pearson Correlation Coefficient is 0.61. This study paved a new approach for the accumulation treatment of industrial solid waste and low-carbon industry via statistical analysis and machine learning method, which is beneficial to environmental protection and sustainable development.