Predicting Octane Number of Petroleum-Derived Gasoline Fuels from MIR Spectra, GC-MS, and Routine Test Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Type of Fuel
2.2. Input Features
2.2.1. Chemical Composition
2.2.2. Infrared Spectroscopic Analysis
2.2.3. Physical Properties
2.3. Analytical Method
3. Results and Discussion
3.1. Composition-Based Model
3.2. Infrared Spectroscopy Data-Based Model
3.3. Physical-Properties-Based Model
4. Conclusions
- Despite their empirical nature, the proposed prediction models had the potential to predict the RON and MON of real gasoline fuels commercialized in Colombia.
- The results showed that the best performance for both MON and RON prediction corresponded with the composition-based model, since it presented lesser evaluation indices (RMSE, MAE, and R2) and more than 80% of residuals were within the established criteria (sum of the reproducibility and the uncertainty of the standard method).
- Although the routine-test-data-based method performed poorly according to the established criterion, its use could be recommended in cases of scarce data since it showed an acceptable value of R2 and physical consistency. The main novelty of the last method is to correlate a complex parameter such as ON with simple and easy-to-measure fuel properties such as the API gravity and representative points of the distillation curve.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Author | Input Feature | Analytical Method | RON Range | R2 | RMSE |
---|---|---|---|---|---|
Wu et al. [7] | NIR | ANN | 90–100 | 0.970 | 0.125 |
De Paulo et al. [25] | FES: flame spectroscopy emission | PLS | 92–100 | 0.955 | 0.167 |
Mendes et al. [34] | Distillation curves | PLS | 97.4–101.4 | - | 0.085 |
Kardamakis et al. [19] | NIR | MLR and LPC (Linear predictive coding) | 90.7–102.2 | 0.987 | 0.310 |
Felicío et al. [18] | NIR | Serial PLS | 89–101 | - | 0.270 |
Jameel et al. [2] | NMR | ANN | - | 0.990 | - |
Kelly et al. [10] | NIR | PLS | 91–98 | - | 0.230 |
Jeon et al. [17] | NIR | Ridge regression | 90–98 | - | 0.067 |
Cooper et al. [22] | Raman | PLS | - | - | 0.535 |
Van Leeuwen et al. [14] | GC | ANN | - | - | 0.350 |
Name | Formula | Density g/cm3 at 20 °C | RON | MON |
---|---|---|---|---|
Hexane | C6H14 | 0.6590 | 24.8 | 26 |
Cyclohexane | C6H12 | 0.7781 | 83 | 77.2 |
Benzene | C6H6 | 0.8756 | 105 | 102.5 |
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Benavides, A.; Zapata, C.; Benjumea, P.; Franco, C.A.; Cortés, F.B.; Ruiz, M.A. Predicting Octane Number of Petroleum-Derived Gasoline Fuels from MIR Spectra, GC-MS, and Routine Test Data. Processes 2023, 11, 1437. https://doi.org/10.3390/pr11051437
Benavides A, Zapata C, Benjumea P, Franco CA, Cortés FB, Ruiz MA. Predicting Octane Number of Petroleum-Derived Gasoline Fuels from MIR Spectra, GC-MS, and Routine Test Data. Processes. 2023; 11(5):1437. https://doi.org/10.3390/pr11051437
Chicago/Turabian StyleBenavides, Alirio, Carlos Zapata, Pedro Benjumea, Camilo A. Franco, Farid B. Cortés, and Marco A. Ruiz. 2023. "Predicting Octane Number of Petroleum-Derived Gasoline Fuels from MIR Spectra, GC-MS, and Routine Test Data" Processes 11, no. 5: 1437. https://doi.org/10.3390/pr11051437
APA StyleBenavides, A., Zapata, C., Benjumea, P., Franco, C. A., Cortés, F. B., & Ruiz, M. A. (2023). Predicting Octane Number of Petroleum-Derived Gasoline Fuels from MIR Spectra, GC-MS, and Routine Test Data. Processes, 11(5), 1437. https://doi.org/10.3390/pr11051437