Enhancing Random Forest Model Accuracy using GridSearchCV Optimization for Predicting Multi-Cylinder Engine Performance with Hydrogen-Enriched Natural Gas Blends

Prasanna S Sutar; Ravi Sekhar; Shailesh B Sonawane; Debjyoti Bandyopadhyay; Sandeep D Rairikar; Sukrut S Thipse; Hiranmayee Ganorkar; Aryan V. Kadam

doi:10.5614/j.eng.technol.sci.2025.57.5.9

Enhancing Random Forest Model Accuracy using GridSearchCV Optimization for Predicting Multi-Cylinder Engine Performance with Hydrogen-Enriched Natural Gas Blends

Published: 2025-01-01 | DOI: 10.5614/j.eng.technol.sci.2025.57.5.9 | Vol 57, Issue 5 (2025)

Authors

Prasanna S Sutar

Research Scholar, Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Pashan-Sus Road, Pune, 412115, Maharashtra

ORCID: 0000-0003-1418-5088 · OpenAlex: A5054093047

Ravi Sekhar

Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Pashan-Sus Road, Pune, 412115, Maharashtra

ORCID: 0000-0002-4732-5246 · OpenAlex: A5028206888

Shailesh B Sonawane

The Automotive Research Association of India (ARAI), Vetal Hill, Off-Paud Road, Pune 411038, Maharashtra,

Debjyoti Bandyopadhyay

The Automotive Research Association of India (ARAI), Vetal Hill, Off-Paud Road, Pune 411038, Maharashtra,

ORCID: 0000-0001-6099-8413 · OpenAlex: A5087183121

Sandeep D Rairikar

The Automotive Research Association of India (ARAI), Vetal Hill, Off-Paud Road, Pune 411038, Maharashtra,

Sukrut S Thipse

The Automotive Research Association of India (ARAI), Vetal Hill, Off-Paud Road, Pune 411038, Maharashtra,

Hiranmayee Ganorkar

Technovuus, ARAI, Vetal Hill, Off-Paud Road, Pune 411038, Maharashtra,

OpenAlex: A5119909484

Aryan V. Kadam

San Jose State University, San Jose, California,

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Abstract

Diesel generators (gensets) are essential in India for industries, construction, agriculture, and as backup power for hospitals and data centres. Common fuels include diesel, petrol, natural gas, and, increasingly, solar energy, with hybrid systems gaining popularity for improved efficiency and reduced emissions. Diesel gensets remain reliable and cost-effective, especially in remote areas, but growing environmental concerns are driving adoption of cleaner alternatives like compressed natural gas (CNG), bio-CNG, and dual-fuel systems. HCNG (hydrogen-enriched compressed natural gas) gensets are more efficient and environmentally friendly, though they require greater initial investment. Adding hydrogen enhances combustion and reduces emissions. In this study, various HCNG blends were tested on a multi-cylinder, single-speed gas engine. Experimental evaluation of combustion and performance characteristics is typically time and resource-intensive, so Machine Learning (ML) was applied to streamline the process, thereby minimizing the number of required experiments. The engine performance is assessed using the engine dynamometer, whereas the combustion characteristics are obtained from the High-Speed Data Acquisition (HSDA) system. A Random Forest (RF) regression model was developed to predict performance and combustion characteristics for higher HCNG blends from lower-blend data, with hyperparameter optimization used to improve accuracy and minimize overfitting. Predicted values were validated against experimental results, showing strong correlations. Key parameters like Brake-Specific Fuel Consumption (BSFC), Brake Mean Effective Pressure (BMEP), Exhaust Temperature, Maximum In-Cylinder Combustion Pressure (Pmax), Indicated Mean Effective Pressure (IMEP) and Combustion Duration were predicted, with evaluations showing strong correlations between predicted values and actual results.

Keywords

CNG; combustion; GridSearchCV; HCNG; hyperparameters; machine learning; random forest

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Research Intelligence

Data from OpenAlex ↗

Metrics

0.00

FWCIfield-weighted

15th

Percentilevs same year + field

Article

Work type

GOLD

Open Access

Topics

Advanced Combustion Engine Technologies Primary

1.00

Fluid Flow and Transfer Processes › Chemical Engineering › Physical Sciences

Vehicle emissions and performance

0.99

Engineering Applied Research

0.97

Semantic Profile AI-classified research signals

Core Domains

Combustion 0.63

level 2

Compressed natural gas 0.56

level 2

Automotive engineering 0.53

level 1

Secondary Topics

Natural gas 0.51 Environmental science 0.49 Fuel efficiency 0.47 Process engineering 0.44 Diesel engine 0.43 Random forest 0.41 Computer science 0.37 Diesel fuel 0.35 Engineering 0.31 Fossil fuel 0.31 Gas engine 0.31 Power (physics) 0.30 Biofuel 0.28 Internal combustion engine 0.27 Backup 0.26 Biomass (ecology) 0.26 Waste management 0.26 Ignition system 0.25 Hyperparameter 0.25

Institution Network

Symbiosis International University IN
Prasanna S Sutar · Ravi Sekhar
The Automotive Research Association of India IN
Shailesh Sonawane · Debjyoti Bandyopadhyay · S. D. Rairikar · S. S. Thipse · Hiranmayee Ganorkar

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[ref-1] Airamadan, A. S., Al Ibrahim, Z., Mohan, B., & Badra, J. (2022). Machine Learning Model for Spark-Assisted Gasoline Compression Ignition Engine. SAE Technical Papers. https://doi.org/10.4271/2022-01-0459 DOI: 10.4271/2022-01-0459

[ref-2] Bandyopadhyay, D., Sutar, P., Sonawane, S., Rairikar, S., Thipse, Dr. S., & Sale, O. (2025). Raw Emissions Determination and Calculations for Blended Fuel – HCNG. ARAI Journal of Mobility Technology, 5(1), 1490–1501. https://doi.org/10.37285/ajmt.5.1.10 DOI: 10.37285/ajmt.5.1.10

[ref-3] Cheng, M., Zhao, X., Dhimish, M., Qiu, W., & Niu, S. (2024). A Review of Data-Driven Surrogate Models for Design Optimization of Electric Motors. IEEE Transactions on Transportation Electrification, 10(4), 8413–8431. https://doi.org/10.1109/TTE.2024.3366417 DOI: 10.1109/tte.2024.3366417

[ref-4] Choi, Y., Park, C.-W., Won, S.-Y., & Kim, C.-G. (2011). A Study on the Optimization of Combustion and Emission Performance in a Heavy-duty HCNG Engine. Journal of the Korean Institute of Gas, 15(2), 15–20. https://doi.org/10.7842/kigas.2011.15.2.015 DOI: 10.7842/kigas.2011.15.2.015

[ref-5] De Simio, L., Gambino, M., & Iannaccone, S. (2011). Effect of natural gas/hydrogen blends on spark ignition stoichiometric engine efficiency. SAE Technical Papers. https://doi.org/10.4271/2011-24-0121 DOI: 10.4271/2011-24-0121

[ref-6] Deng, J., Ma, F., Li, S., He, Y., Wang, M., Jiang, L., & Zhao, S. (2011). Experimental study on combustion and emission characteristics of a hydrogen-enriched compressed natural gas engine under idling condition. International Journal of Hydrogen Energy, 36(20), 13150–13157. https://doi.org/10.1016/j.ijhydene.2011.07.036 DOI: 10.1016/j.ijhydene.2011.07.036

[ref-7] Duan, H., Yin, X., Kou, H., Wang, J., Zeng, K., & Ma, F. (2023). Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN). Fuel, 331, 125872. https://doi.org/10.1016/j.fuel.2022.125872 DOI: 10.1016/j.fuel.2022.125872

[ref-8] Fact.MR. (2024). P-Phenylenediamine Market Size & Industry Analysis to 2032. https://www.factmr.com/report/p-phenylenediamine-market

[ref-9] Farhan, M., Chen, T., Rao, A., Shahid, M. I., Liu, Y., & Ma, F. (2024). Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool. Energy, 304, 132135. https://doi.org/10.1016/j.energy.2024.132135 DOI: 10.1016/j.energy.2024.132135

[ref-10] Ghareeb, A., Abdulkarim, A. H., Salman, A. S., Kakei, A., Canli, E., Chiasson, A., Choi, J.-K., & Dalkilic, A. S. (2024). Prediction of the operational performance of a vehicle seat thermal management system using statistical and machine learning techniques. Case Studies in Thermal Engineering, 60, 104626. https://doi.org/10.1016/j.csite.2024.104626 DOI: 10.1016/j.csite.2024.104626

[ref-11] Gong, C., Li, D., Li, Z., & Liu, F. (2016). Numerical study on combustion and emission in a DISI methanol engine with hydrogen addition. International Journal of Hydrogen Energy, 41(1), 647–655. https://doi.org/10.1016/j.ijhydene.2015.11.062 DOI: 10.1016/j.ijhydene.2015.11.062

[ref-12] Hao, D., Mehra, R. K., Luo, S., Nie, Z., Ren, X., & Fanhua, M. (2020). Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition. International Journal of Hydrogen Energy, 45(8), 5309–5325. https://doi.org/10.1016/j.ijhydene.2019.04.039 DOI: 10.1016/j.ijhydene.2019.04.039

[ref-13] Hora, T. S., & Agarwal, A. K. (2016). Effect of varying compression ratio on combustion, performance, and emissions of a hydrogen enriched compressed natural gas fuelled engine. Journal of Natural Gas Science and Engineering, 31, 819–828. https://doi.org/10.1016/j.jngse.2016.03.041 DOI: 10.1016/j.jngse.2016.03.041

[ref-14] Hou, Y., Zhang, Z., Liu, P., Song, C., & Wang, Z. (2021). Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles. Advances in Mechanical Engineering, 13(7). https://doi.org/10.1177/16878140211027735 DOI: 10.1177/16878140211027735

[ref-15] Ishaq, H., & Dincer, I. (2020). Performance investigation of adding clean hydrogen to natural gas for better sustainability. Journal of Natural Gas Science and Engineering, 78, 103236. https://doi.org/10.1016/j.jngse.2020.103236 DOI: 10.1016/j.jngse.2020.103236

[ref-16] Kim, D.-K., Ryu, D., Lee, Y., & Choi, D.-H. (2024). Generative models for tabular data: A review. Journal of Mechanical Science and Technology, 38(9), 4989–5005. https://doi.org/10.1007/s12206-024-0835-0 DOI: 10.1007/s12206-024-0835-0

[ref-17] Kou, L., Qin, Y., Zhao, X., & Fu, Y. (2019). Integrating synthetic minority oversampling and gradient boosting decision tree for bogie fault diagnosis in rail vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 233(3), 312–325. https://doi.org/10.1177/0954409718795089 DOI: 10.1177/0954409718795089

[ref-18] Lather, R. S., & Das, L. M. (2019). Performance and emission assessment of a multi-cylinder S.I engine using CNG & HCNG as fuels. International Journal of Hydrogen Energy, 44(38), 21181–21192. https://doi.org/10.1016/j.ijhydene.2019.03.137 DOI: 10.1016/j.ijhydene.2019.03.137

[ref-19] Lee, J., & Kwon, D. (2025). Reduction in electronic package design processes through AI-based surrogate models. JMST Advances, 7, 125–130. https://doi.org/10.1007/s42791-025-00106-3 DOI: 10.1007/s42791-025-00106-3

[ref-20] Liu, J., Duan, X., Yuan, Z., Liu, Q., & Tang, Q. (2017). Experimental study on the performance, combustion and emission characteristics of a high compression ratio heavy-duty spark-ignition engine fuelled with liquefied methane gas and hydrogen blend. Applied Thermal Engineering, 124, 585–594. https://doi.org/10.1016/j.applthermaleng.2017.06.067 DOI: 10.1016/j.applthermaleng.2017.06.067

[ref-21] M., G. N., Banapurmath, N. R., & Tewari, P. G. (2016). Performance, Combustion and Emission characteristics of a Manifold Injected HCNG-Biodiesel Dual Fuel Operation / Bir Manifolda Enjekte Edilen HCNG-Biyodizelin Çift Yakıtlı Çalışmasının Performans, Yanma ve Emisyon Karakteristikleri. International Journal of Automotive Engineering and Technologies, 4(4), 201.

[ref-22] Mehra, R. K., Duan, H., Luo, S., Rao, A., & Ma, F. (2018). Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios. Applied Energy. https://doi.org/10.1016/j.apenergy.2018.06.085 DOI: 10.1016/j.apenergy.2018.06.085

[ref-23] Michikawauchi, R., Tanno, S., Ito, Y., Kanda, M., & Kawauchi, M. (2011). Combustion improvement of CNG engines by hydrogen addition. SAE Technical Papers. https://doi.org/10.4271/2011-01-1996 DOI: 10.4271/2011-01-1996

[ref-24] Mustafi, N. N., & Agarwal, A. K. (2019). Combustion and Emission Characteristics, and Emission Control of CNG Fueled Vehicles. In A. P. Singh, Y. C. Sharma, N. N. Mustafi, & A. K. Agarwal (Eds.), Alternative Fuels and Their Utilization Strategies in Internal Combustion Engines (pp. 201–228). Springer Singapore. https://doi.org/10.1007/978-981-15-0418-1_12 DOI: 10.1007/978-981-15-0418-1_12

[ref-25] Pandey, V., Guluwadi, S., & Tafesse, G. H. (2022). Performance and emission study of low HCNG fuel blend in SI engine with fixed ignition timing. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2021.2010925 DOI: 10.1080/23311916.2021.2010925

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