Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture

The alarming increase in the concentration of carbon dioxide (CO2) in the atmosphere, mainly due to human emissions, represents a significant threat to life. In this context, carbon capture and storage (CCS) technologies have emerged as promising solutions, such as adsorption on carbonaceous materials, standing out as a prominent approach. This study aims to quantify the maximum CO2 capture in the laboratory scale using functionalized activated carbon by passion fruit peel biomass (FACPFP) and to develop a simple and improved machine learning model to predict the capture of this greenhouse gas. FACPFP was successfully prepared through chemical activation with K2C2O4 and doping with ethylenediamine (EDA) at 700 degrees C and 1 h. The samples were thoroughly characterized by thermogravimetric analysis (TGA), scanning electron microscopy (SEM) with energy dispersive X-ray detector (EDX), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). CO2 sorption was assessed using functional density theory (DFT). For predictive model, multiple linear regression with cross-validation was used. Under CO2 atmosphere conditions, the textural parameters allowed to see the probable presence of ultra-micropores, the BET surface area, the total pore and micropore volume were 105 m2/g, 0.03 cm3 /g and 0.06 cm3 /g, respectively. The maximum CO2 adsorption capacity in the FACPFP reached about 2.2 mmol/g at 0 degrees C and 1 bar. The predictive model demonstrated an improvement of CO2 adsorption precision, raising it from 53% to 61% with cross-validation. This study also aims to stimulate future investigations in the area of CO2 capture, due to the extreme relevance of this topic.