Prediction of uranium adsorption capacity on biochar by machine learning methods
Journal of Environmental Chemical Engineering, ISSN: 2213-3437, Vol: 10, Issue: 5, Page: 108449
2022
- 43Citations
- 45Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Most Recent News
Investigators at North China Electric Power University Detail Findings in Machine Learning (Prediction of Uranium Adsorption Capacity On Biochar By Machine Learning Methods)
2022 OCT 31 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Researchers detail new data in Machine Learning. According to
Article Description
The effective separation of uranium is a challenge for the treatment of radioactive wastewater. In this study, four machine learning (ML) methods (linear regression, support vector regression, random forest, and multilayer perceptron artificial neural network) were applied to predict the adsorption capacity of uranium on biochar. The relative importance of physical and chemical properties of biochar was also analyzed. Independent adsorption experiments were conducted with four biochar to verify the ML model. After training and verification, the model obtained with two hidden layers perceptron artificial neural network performs best by comparing the values of R 2 and RMSE. The structural properties of biochar, such as specific surface area, are more important for the adsorption capacity of uranium than the chemical composition. ML modeling provides a new strategy for the design and tailoring of biochar for uranium adsorption, which can significantly reduce the experimental workload and the safety risks associated with radioactivity.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2213343722013227; http://dx.doi.org/10.1016/j.jece.2022.108449; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85137179650&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2213343722013227; https://dx.doi.org/10.1016/j.jece.2022.108449
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know