Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Adsorbent Preparation and Characterization
2.3. Batch Experiment and Chromium Analysis
3. Network Development
3.1. ANN Model
3.2. ANFIS Model
3.3. Data Processing in ANN and ANFIS
3.4. Statistical Evaluation
- Correlation coefficient (R)
- Sum of squares error (SSE)
- Sum of the absolute error (SAE)
- Average relative error (ARE)
- Absolute average deviation (AAD)
- Mean squared error (MSE)
- Root mean square error (RMSE)
- Hybrid fractional error function (HYBRID)
- Marquart’s percentage standard deviation (MPSD)
- Chi-square
3.5. Software Used
4. Results and Discussion
4.1. ANN Performance
4.2. ANFIS Performance
4.3. Comparison of ANN and ANFIS Models
4.4. Practical Implications of the Work and Future Research Perspectives
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Pollutant (s) | Adsorbent | Batch/Continuous | Type of Algorithm | Input Parameters | Output Parameters | Data Points Used | Network Architecture | References |
---|---|---|---|---|---|---|---|---|
Arsenic | Iron/olivine composite | Batch | Levenberg-Marquardt (LM) BP algorithm | Cad, Ci, time, qag, and pH | REAs | 38 | 5:12:1 (As (III)T) 5:14:1 (As (V)T) | [28] |
Ethanol mediated-Arsenic (III) | Zn-loaded pinecone (PC) biochar | Batch | BP algorithm and genetic algorithm | CAs, CEtOH, and pH | ACAs | 17 | 3:4:1 | [29] |
Fluoride | Protonated clinoptilolite | Batch | Hybrid feedforward algorithm | pH, Ci, and temperature | ACF | 99 | 3:3:1 | [30] |
Methylene blue (MB) | Biowaste AC 1 | Batch | BP algorithm | Ci, Cad, pH, temperature, and time | ACdye | 88 | 5:10:1 | [31] |
Zinc (II) | Palm kernel shell AC 1 | Batch | PSO and LM-BP algorithm | Ci, pH, time, Cad, and temperature | REZn | 270 | 5:7:1 | [32] |
Methyl orange dye | Polyaniline nano-adsorbent | Batch | LM-BP algorithm | pH, Ci, Cad, temperature, and time | REdye | 322 | 5:12:1 | [33] |
Phenol | Scoria stone | Batch | CS-NN 3 | Cad, Ci, and time | REPh | 100 | 3:10:1 | [34] |
Arsenic | Opuntia ficus indica biomass | Batch | Hybrid model | Ci, temperature, pH, and time | ACAs | 81 | 4:3:1 | [21] |
AB and AR 4 | Chitosan hydrogels | Batch | LM-BP algorithm | Ci-AB, Ci-AR, time, ϕ, and w/w% | ACAB-AC | 315 | 5:10:10:10:1 | [35] |
Copper and Manganese | GN-SDS 2 | Continuous | Quick prop algorithm | Ci, Cad, pH, and temperature | RECu,Mg | 40 | 4:10:1 | [36] |
Cadmium, nickel, zinc, and copper | Bone Biochar | Continuous | LM-BP algorithm | Ci and time | Ct/C0 | 1420 | 2:6:1 | [37] |
Coal-based pollutant | AC 1 | Continuous | LM–BP algorithm | β, ReL, t/tmax, and Ck/C0 | qt/qmax | 208 | 4:9:1 | [38] |
Triclosan and ibuprofen | AC | Continuous | Gradient descent algorithm | Ci and q | C-t | 10 | 2:4:1 | [39] |
Run | Actual Level of Factors | Chromium RE (%) | |||
---|---|---|---|---|---|
X1 | X2 (g/L) | X3 (mg/L) | Experimental | Predicted | |
1 | 4 | 2.5 | 10 | 80.54 | 84.38 |
2 | 8 | 2.5 | 10 | 69.79 | 69.87 |
3 | 4 | 7.5 | 10 | 92.72 | 94.54 |
4 | 8 | 7.5 | 10 | 90.36 | 89.56 |
5 | 8 | 2.5 | 30 | 52.35 | 49.72 |
6 | 8 | 7.5 | 30 | 82.20 | 77.54 |
7 | 2.64 | 5 | 20 | 95.48 | 93.94 |
8 | 9.36 | 5 | 20 | 73.95 | 74.85 |
9 | 6 | 0.795 | 20 | 49.58 | 49.69 |
10 | 6 | 9.204 | 20 | 88.34 | 87.69 |
11 | 6 | 5 | 3.182 | 92.81 | 91.28 |
12 | 6 | 5 | 36.82 | 72.15 | 72.98 |
13 | 6 | 5 | 20 | 80.85 | 80.99 |
14 | 6 | 5 | 20 | 80.89 | 80.99 |
15 | 6 | 5 | 20 | 80.84 | 80.99 |
16 | 6 | 5 | 20 | 80.87 | 80.99 |
17 | 6 | 5 | 20 | 83.49 | 80.99 |
18 | 6 | 5 | 20 | 81.02 | 80.99 |
Characteristics | Features/Value |
---|---|
ANN model | |
Network type | Feed-forward backpropagation |
Training function | Trainlm (Levenberg Marquardt) |
Number of hidden layers | 1 |
Number of data used for network training | 14 |
Number of data used for testing | 4 |
Transfer function in hidden layer | tansig (Sigmoid) |
Optimum no. of neurons in hidden layer | 3 |
Epoch number | 164 |
ANFIS Model | |
Number of nodes | 58 |
Number of linear parameters | 72 |
Number of nonlinear parameters | 24 |
Total number of parameters | 96 |
Number of training data pairs | 14 |
Number of validation data pairs | 04 |
Number of fuzzy rules | 18 |
Parameters | ANN Model | ANFIS Model | ||
---|---|---|---|---|
Training | Testing | Training | Testing | |
R | 1.00 | 0.97 | 0.99 | 0.82 |
R2 | 1.00 | 0.98 | 0.99 | 0.94 |
SSE | 0.03 | 60.58 | 5.57 | 233.15 |
SAE | 0.51 | 10.89 | 4.60 | 19.12 |
ARE | 0.05 | 2.88 | 0.40 | 5.51 |
AAD | 0.23 | 1009.72 | 39.75 | 3885.83 |
MSE | <0.01 | 10.10 | 0.40 | 38.85 |
RMSE | 0.05 | 3.18 | 0.63 | 6.23 |
HYBRID | <0.01 | 33.66 | 0.61 | 144.72 |
MPSD | 6.03 | 580.16 | 78.24 | 1203.03 |
Chi-Square | <0.01 | 1.01 | 0.07 | 4.34 |
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Zafar, M.; Aggarwal, A.; Rene, E.R.; Barbusiński, K.; Mahanty, B.; Behera, S.K. Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System. Processes 2022, 10, 447. https://doi.org/10.3390/pr10030447
Zafar M, Aggarwal A, Rene ER, Barbusiński K, Mahanty B, Behera SK. Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System. Processes. 2022; 10(3):447. https://doi.org/10.3390/pr10030447
Chicago/Turabian StyleZafar, Mohd, Ayushi Aggarwal, Eldon R. Rene, Krzysztof Barbusiński, Biswanath Mahanty, and Shishir Kumar Behera. 2022. "Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System" Processes 10, no. 3: 447. https://doi.org/10.3390/pr10030447