Machine learning models and dataset for the prediction of Cr6+ removal of aqueous solutions using the pine cone residue

The experimental results of the adsorption of Cr6+ from aqueous solutions using the pine cone residue and the machine learning models used to investigate the experimental parameters are available on this page. Three adsorption conditions are optimized: contact time, pH, and initial solution concentration. The file “Pinha.csv” presents the results of the experiments considering various adsorption conditions. In addition, three machine learning models are employed to predict the behaviour of the experimental results: a multiple linear regression model, a decision tree model, and a random forest model. The machine learning models are in the “Machine learning models.ipynb” file. The maximum Cr6+ removal obtained with the pine cone residue is near 91%, and the machine learning models presented a high correlation coefficient of over 0.9, highlighting the potential of this type of methodology to enhance experimental studies.