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

Published: 20 February 2025| Version 1 | DOI: 10.17632/6bspjkt7bg.1
Contributors:
,
,
, Luana Souza Almeida,
,
,
,

Description

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.

Files

Steps to reproduce

To reproduce this research, scientists should open the Python code (“Machine learning models.ipynb”) with an IDE such as Jupyter Notebooks or VS Code. The Pandas, Sklearn, Matplotlib, and Plotly libraries are required for the code to work. The code is divided into subsections: “1) Import libraries”, “2) Import and pre-process the data”, “3) Multiple Linear Regression”, “4) Decision Tree”, “5) Random Forest Model”, “6) Plotting the results”, “7) Plotting the decision tree”. Sections 1 and 2 are standard for all the machine learning models, available in sections 3, 4, and 5. The visualization of the results is presented in sections 6 and 7. Notice that the code automatically reads the results on the database file (“Pinha.csv”); hence, both the code and the csv file should be placed in the same folder. If the user cannot access the ipynb program, a “.py” file that can be opened on traditional Python IDEs is also provided.

Institutions

Universidade de Sao Paulo, Universidade Federal do ABC, Universidade do Estado do Rio de Janeiro

Categories

Machine Learning, Adsorption, Crop Residue

Funding

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

E-26/010.101232/2018

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

-26/010/002530/2019

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

E-26/210.450/2021

Fundação de Amparo à Pesquisa do Estado de São Paulo

2020/13703-3

Fundação de Amparo à Pesquisa do Estado de São Paulo

2021/14714-1

Fundação de Amparo à Pesquisa do Estado de São Paulo

2023/14598-7

National Council for Scientific and Technological Development

308053/2021-4

National Council for Scientific and Technological Development

403934/2021-4

Universidade Federal do ABC

Coordenação de Aperfeicoamento de Pessoal de Nível Superior

REVALORES Strategic Unit

Multiuser Central Facilities

Financiadora de Estudos e Projetos

01.18.0071.00/0476/16

Licence