Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Experimental Methods
3. Computational Protocol
3.1. Multiple-Regression Modeling by Response Surface Methodology (RSM)
3.2. Machine Learning by Artificial Neural Network (ANN)
3.3. Machine Learning by Support Vector Machine (SVM)
4. Results and Discussions
4.1. Design of Experiments (DoE)
4.2. Data-Driven Modeling: RSM vs. Machine Learning (ANN and SVM)
4.3. Multivariate Optimization of Adsorption-Ultrafiltration Hybrid Process
4.4. Testing Optimal Conditions on a New Composite Membrane
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Run/Trial | Sorbent Dose | pH of Feed Aqueous Solutions | Response: Nitrite Removal Efficiency Y (%) | ||
---|---|---|---|---|---|
Coded x1 | Actual SD, % w/v | Coded x2 | Actual pH | ||
1 | −1 | 0.20 | −1 | 5.5 | 50.09 |
2 | +1 | 0.60 | −1 | 5.5 | 82.35 |
3 | −1 | 0.20 | +1 | 8.5 | 67.54 |
4 | +1 | 0.60 | +1 | 8.5 | 83.53 |
5 | −1.414 | 0.12 | 0 | 7.0 | 59.69 |
6 | +1.414 | 0.68 | 0 | 7.0 | 86.03 |
7 | 0 | 0.40 | −1.414 | 4.9 | 72.64 |
8 | 0 | 0.40 | +1.414 | 9.1 | 70.36 |
9 | 0 | 0.40 | 0 | 7.0 | 80.48 |
10 | 0 | 0.40 | 0 | 7.0 | 81.35 |
11 | 0 | 0.40 | 0 | 7.0 | 81.07 |
Statistical Descriptor for Residuals | Sources of Residuals (Yexperimental − Ymodel) | ||
---|---|---|---|
RSM Model | ANN Model | SVM Model | |
Minimal residue (min) | −4.7580 | −3.1324 | −2.0045 |
Maximal residue (max) | 4.5140 | 0.6928 | 1.1522 |
Amplitude (max-min) | 9.2720 | 3.8252 | 3.1567 |
Median | 0.1030 | 0.1090 | 0.2822 |
Average | 0.0002 | 0.3204 | 0.0672 |
Standard deviation | 2.8035 | 1.0462 | 1.0079 |
(LCC) | 0.939 | 0.994 | 0.999 |
R2 (ANOVA) | 0.938 | 0.989 | 0.992 |
Model | Sorbent Dose (% w/v) | pH of Feed Solution | Response (Removal Efficiency, %) | |||
---|---|---|---|---|---|---|
x1 (Coded) | SD (Actual) | x2 (Coded) | pH (Actual) | |||
RSM | 1.393 | 0.678 | −0.365 | 6.4 | 88.05 | 85.93 |
ANN | 1.409 | 0.682 | 0.073 | 7.1 | 85.53 | 86.18 |
SVM | 1.372 | 0.674 | 0.007 | 7.0 | 84.95 | 86.28 |
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Cojocaru, C.; Pascariu, P.; Enache, A.-C.; Bargan, A.; Samoila, P. Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning. Nanomaterials 2023, 13, 697. https://doi.org/10.3390/nano13040697
Cojocaru C, Pascariu P, Enache A-C, Bargan A, Samoila P. Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning. Nanomaterials. 2023; 13(4):697. https://doi.org/10.3390/nano13040697
Chicago/Turabian StyleCojocaru, Corneliu, Petronela Pascariu, Andra-Cristina Enache, Alexandra Bargan, and Petrisor Samoila. 2023. "Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning" Nanomaterials 13, no. 4: 697. https://doi.org/10.3390/nano13040697
APA StyleCojocaru, C., Pascariu, P., Enache, A. -C., Bargan, A., & Samoila, P. (2023). Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning. Nanomaterials, 13(4), 697. https://doi.org/10.3390/nano13040697