Artificial Neural Network-Based Prediction of Clogging Duration to Support Backwashing Requirement in a Horizontal Roughing Filter: Enhancing Maintenance Efficiency
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area and Water Quality Data
2.2. Constructed HRF Equipment
2.3. Experimental Data and Sampling
2.4. Water Measuring Instruments
2.5. ANN Software and Structure
2.6. Data Normalization
2.7. ANN Performance
2.8. ANN Training and Performance
3. Results
3.1. Experimental Results
Source Graywater Quality Indicators
3.2. ANN Training Results
3.3. ANN Validation
4. Discussion
4.1. ANN Performance Analysis
4.2. ANN Applications and Implications for HRF Operation
4.3. Implications for HRF Operation and Maintenance
4.4. Limitations and Future Perspectives
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Filter Parameters | Recommended Literature Values by [2,7] | HRF Design Values | |
---|---|---|---|
Gravel media | Gravel L1 (mm) | 18–12 | 14 mm |
Gravel L2 (mm) | 12–8 | 10 mm | |
Gravel L3 (mm) | 8–4 | 8 mm | |
Gravel type | (Granite, Quartz, Local) | Quartzite | |
Filter depth (m) | 0.2–1.2 | 0.3 | |
Total height (m) | ≈1.2 | 1 | |
Filtration velocity (m/hr) | 0.3–1.5 | 0.3–0.9 | |
Hydraulic loading rate (m3/m2/day) | 7.2–21.6 | ||
Filter length (m) | L1 (m) | 1.5 | |
L2 (m) | 1 | ||
L3 (m) | 0.5 | ||
Filter width (m) | 1–2.3 | 1 | |
Filter material | Steel and PVC |
ANN Characteristics | Specification |
---|---|
1. Type of neural network | Feed Forward Back Propagation |
2. Number of neurons in the input layer | 4 |
3. Number of neurons in the hidden layer(s) | 4–10 |
4. Number of neuron(s) in the output layer | 2 |
5. Number of input parameters | 4 |
6. Number of output parameter | 2 |
7. Size of the ANN data cases | 637 |
8. Performance function | MSE, R-coefficients |
9. Training algorithms | LM and SCG |
10. Learning functions | learngdm, learngd |
11. Activation function in the hidden layer | tansig, logsig |
12. Activation function in the output layer | linear |
13. Maximum number of epochs | 1000 |
14. Minimum MSE Value | <0.001 |
Inputs | Outputs | |||||
---|---|---|---|---|---|---|
Filtration Rate (m/h) | pH (-) | Turbidity (NTU) | Conductivity (µS/cm) | Clogging Duration (days) | Turbidity (NTU) | |
Mean | - | 9.1 | 217 | 681 | 18 | 32 |
Min | 0.3 | 6.8 | 120 | 301 | 10 | 6 |
Max | 0.9 | 11.4 | 319 | 1094 | 25 | 95 |
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Mtsweni, S.; Bakare, B.F.; Rathilal, S. Artificial Neural Network-Based Prediction of Clogging Duration to Support Backwashing Requirement in a Horizontal Roughing Filter: Enhancing Maintenance Efficiency. Water 2025, 17, 2319. https://doi.org/10.3390/w17152319
Mtsweni S, Bakare BF, Rathilal S. Artificial Neural Network-Based Prediction of Clogging Duration to Support Backwashing Requirement in a Horizontal Roughing Filter: Enhancing Maintenance Efficiency. Water. 2025; 17(15):2319. https://doi.org/10.3390/w17152319
Chicago/Turabian StyleMtsweni, Sphesihle, Babatunde Femi Bakare, and Sudesh Rathilal. 2025. "Artificial Neural Network-Based Prediction of Clogging Duration to Support Backwashing Requirement in a Horizontal Roughing Filter: Enhancing Maintenance Efficiency" Water 17, no. 15: 2319. https://doi.org/10.3390/w17152319
APA StyleMtsweni, S., Bakare, B. F., & Rathilal, S. (2025). Artificial Neural Network-Based Prediction of Clogging Duration to Support Backwashing Requirement in a Horizontal Roughing Filter: Enhancing Maintenance Efficiency. Water, 17(15), 2319. https://doi.org/10.3390/w17152319