Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine
Abstract
1. Introduction
2. Methods
2.1. Experimental
2.2. Analysis of Variance (ANOVA) Testing
2.3. ANN Modeling
- Input layer: 3 neurons (corresponding to IT, Load, and NP).
- Hidden layers: 1 hidden layer comprising 5 neurons with tangent sigmoid activation functions.
- Output layer: 1 neuron with a linear activation function, tailored for continuous regression output.
- is the input vector comprising injection timing, load, and nanoparticle size.
- and are the weight matrix and bias vector of the hidden layer.
- is the hyperbolic tangent sigmoid function, which maps each neuron’s input to the range (−1, 1) and introduces nonlinearity to capture complex relationships.
- and are the weight matrix and bias vector of the output layer.
- is the output vector representing NOx and BTE.
2.4. Response Surface Plots
2.5. Optimization Modeling
- Swarm size: 200 (calculated as 30 n, where n = 3 represents the number of process parameters).
- Inertia weight (w): 0.5 (constant across iterations).
- Cognitive coefficient (c1): 2.0.
- Social coefficient (c2): 2.0.
- Maximum iterations: 100.
3. Results and Discussion
3.1. Experimental Results
3.1.1. Effect of Engine Load on NOx and BTE
3.1.2. Influence of CeO2 Nanoparticles on Combustion and Emissions
3.1.3. Role of Injection Timing in NOx and Efficiency Trade-Off
3.2. ANOVA Results
3.3. Optimization Using ANN-PSO
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Property | Diesel | WCO Neat Biodiesel | B20 Biodiesel |
---|---|---|---|
Density (kg/m3) | 825 | 880 | 843 |
Lower calorific value (MJ/kg) | 43.2 | 39.6 | 40.3 |
Kinematic viscosity (mm2/s) | 3.85 | 5.3 | 3.7 |
Flash point (°C) | 55 | 135 | 66 |
Type | Single Cylinder, Four Stroke, Air Cooled Engine |
---|---|
Make | Kirloskar |
Bore × stroke | 87.5 mm × 110 mm |
Compression ratio | 17.5:1 |
Rated output | 5.2 kW at 1500 rpm |
Injection pressure | 240 bar |
Swept volume | 661 cm3 |
Variable | p-Value | Significance | |
---|---|---|---|
NOx | BTE | ||
IT | 0.017253 | 8.58 × 10−5 | Significant |
Load | 4.21 × 10−8 | 1.01 × 10−13 | Significant |
NP | 0.0375 | 0.0452 | Significant |
IT × Load | 0.896 | 0.275 | Not Significant |
IT × NP | 0.928 | 0.907 | Not Significant |
Load × NP | 0.915 | 0.999 | Not Significant |
Load | PSO | Experimental | ||||||
---|---|---|---|---|---|---|---|---|
IT (°) | NP (nm) | NOx (ppm) | BTE (%) | IT (°) | NP (nm) | NOx (ppm) | BTE (%) | |
25% | 26.66 | 33.07 | 407 | 21.17 | 27.00 | 30.00 | 305 ± 6 | 21.11 ± 0.14 |
50% | 28.40 | 44.85 | 1021 | 25.99 | 27.00 | 50.00 | 1108 ± 23 | 24.62 ± 0.16 |
75% | 27.95 | 35.78 | 1160 | 31.49 | 27.00 | 30.00 | 1272 ± 27 | 32.13 ± 0.21 |
100% | 30.00 | 21.92 | 1096 | 31.71 | 30.00 | 20.00 | 996 ± 21 | 28.58 ± 0.19 |
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Mohan, S.; Ashwini, K.; Ghadai, R.K.; Nag, A.; Petrů, J.; Dinesha, P. Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine. Sustainability 2025, 17, 9004. https://doi.org/10.3390/su17209004
Mohan S, Ashwini K, Ghadai RK, Nag A, Petrů J, Dinesha P. Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine. Sustainability. 2025; 17(20):9004. https://doi.org/10.3390/su17209004
Chicago/Turabian StyleMohan, Sooraj, K. Ashwini, Ranjan Kumar Ghadai, Akash Nag, Jana Petrů, and P. Dinesha. 2025. "Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine" Sustainability 17, no. 20: 9004. https://doi.org/10.3390/su17209004
APA StyleMohan, S., Ashwini, K., Ghadai, R. K., Nag, A., Petrů, J., & Dinesha, P. (2025). Integrated Machine Learning Framework-Based Optimization of Performance and Emissions of Nanomaterial—Integrated Biofuel Engine. Sustainability, 17(20), 9004. https://doi.org/10.3390/su17209004