Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications
Abstract
:1. Introduction
2. Methodology
2.1. Natural Zeolite Pretreatment and Iron Modification
2.2. Adsorbate Preparation
2.3. Determination of Breakthrough Curves for Selenite Adsorption on Modified Zeolite Using Packed-Bed Micro-Columns
2.4. Model Formulation
2.4.1. Multilinear and Non-Linear Regression
2.4.2. Boosted Decision Tree Algorithms
- -
- AdaBoost
- -
- Gradient Boosting
- -
- CatBoost
- -
- XGBoost
- -
- LightGBM
2.5. Cross-Validation
2.6. Evaluation Measurement
3. Selenite Adsorption Dataset (SAD)
Correlation Matrix Analysis
4. Results and Discussion
4.1. Clinoptilolite Characterization
4.2. Continuous Adsorption Experiments
4.2.1. Effect of Initial Inlet Concentration on Breakthrough Curves
4.2.2. Effect of Ionic Strength on Breakthrough Curves
4.3. Statistical Analysis
4.4. Performance of ML Algorithms
4.5. Feature Importance
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Random samples of SAD | Features | |||
Concentration (M) | Ionic Strength (M) | Time (min) | C/Co | |
10−5 | 0.01 | 3 | 0.004974 | |
10−5 | 0.01 | 6 | 0.036074 | |
10−5 | 0.01 | 9 | 0.040607 | |
10−5 | 1 | 59 | 0.125845 | |
10−5 | 1 | 63 | 0.197145 | |
10−5 | 1 | 67 | 0.220659 | |
10−4 | 0.01 | 300 | 0.962113 | |
10−4 | 0.01 | 304 | 0.962832 | |
10−4 | 0.01 | 308 | 0.942469 | |
10−4 | 1 | 129 | 0.774688 | |
10−4 | 1 | 136 | 0.77704 | |
10−4 | 1 | 143 | 0.788269 | |
⋮ ⋮ ⋮ ⋮ | ⋮ ⋮ ⋮ ⋮ | ⋮ ⋮ ⋮ ⋮ | ⋮ ⋮ ⋮ ⋮ | |
Mean | 0.0000545 | 0.495 | 114 | 0.499 |
Median | 0.00001 | 0.01 | 98 | 0.532 |
Minimum | 0.00001 | 0.01 | 3 | 0.002 |
Maximum | 0.0001 | 1 | 348 | 0.962 |
Element | Weight % | |
---|---|---|
Natural Zeolite | Modified Zeolite | |
O | 67.57 | 51.98 |
Mg | 0.42 | 0.48 |
Al | 4.36 | 4.99 |
Si | 17.69 | 25.92 |
K | 1.34 | 0.39 |
Na | 0.53 | 1.18 |
Ca | 2.71 | 1.17 |
Fe | 0.54 | 8.04 |
Others | 4.84 | 5.85 |
Performance Metrics | Prediction Models | ||||
---|---|---|---|---|---|
AdaBoost | Gradient Boosting | XGBoost | LightGBM | CatBoost | |
R2 | 63.78% | 97.97% | 43.27% | 99.00% | 99.57% |
MAE | 0.15 | 0.03 | 0.19 | 0.02 | 0.015 |
MAPE | 0.80 | 0.12 | 1.46 | 0.14 | 0.06 |
RMSE | 0.16 | 0.04 | 0.22 | 0.03 | 0.02 |
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Halalsheh, N.; Alshboul, O.; Shehadeh, A.; Al Mamlook, R.E.; Al-Othman, A.; Tawalbeh, M.; Saeed Almuflih, A.; Papelis, C. Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications. Water 2022, 14, 2519. https://doi.org/10.3390/w14162519
Halalsheh N, Alshboul O, Shehadeh A, Al Mamlook RE, Al-Othman A, Tawalbeh M, Saeed Almuflih A, Papelis C. Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications. Water. 2022; 14(16):2519. https://doi.org/10.3390/w14162519
Chicago/Turabian StyleHalalsheh, Neda, Odey Alshboul, Ali Shehadeh, Rabia Emhamed Al Mamlook, Amani Al-Othman, Muhammad Tawalbeh, Ali Saeed Almuflih, and Charalambos Papelis. 2022. "Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications" Water 14, no. 16: 2519. https://doi.org/10.3390/w14162519
APA StyleHalalsheh, N., Alshboul, O., Shehadeh, A., Al Mamlook, R. E., Al-Othman, A., Tawalbeh, M., Saeed Almuflih, A., & Papelis, C. (2022). Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications. Water, 14(16), 2519. https://doi.org/10.3390/w14162519