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.本页面公开了利用松果残渣吸附水溶液中六价铬（Cr6+）的实验结果，以及用于探究该吸附实验参数的机器学习模型相关数据。本研究优化了三项吸附条件：接触时长、溶液pH值与初始溶液浓度。文件"Pinha.csv"收录了不同吸附条件下的实验结果。此外，本研究采用三类机器学习模型对实验的吸附行为进行预测：多元线性回归模型、决策树模型与随机森林模型。上述机器学习模型均存储于"Machine learning models.ipynb"文件中。利用松果残渣实现的六价铬最大去除率接近91%，且各机器学习模型的相关系数均超过0.9，充分彰显了此类方法在优化实验研究方面的应用潜力。