Vol. 29 No. 3 (2026) Cover Image
Vol. 29 No. 3 (2026)

Published: September 20, 2026

Pages: 530-541

Articles

Experimental and Machine Learning Analysis of a CI Engine Fueled with American Saffron Biodiesel–Diesel Blends

Valiveti Sivaramakrishna 1 Shaik Hussain 2 Chandra Mohana Reddy Gangireddygari 3 Putha Prasad Kumar 1 V. Sai Srikanth 4
1 Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad - 500090, Telangana, India.
2 School of Civil & Mechanical Sciences & Technology, Malla Reddy (MR) Deemed to be University, Hyderabad - 500100, Telangana, India.
3 Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad - 500043, Telangana, India.
4 Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad – 500100, Telangana, India.
History
  • Received: February 12, 2026
  • Revised: March 17, 2026
  • Accepted: May 12, 2026
  • Available Online: September 20, 2026
DOI

https://doi.org/10.29194/NJES.29030530

Abstract

Alternative fuels come from non-traditional sources and can partly replace fossil fuels to cut environmental impact and support sustainable energy use. A single-cylinder diesel engine with rated power 5.2 Kw at 1500 rpm was tested with American Saffron Biodiesel blends. B0 (diesel), B10 (B10D90), B20 (B20D80), and B30 (B30D70), under different loads, and the results were compared with diesel. B20 showed the best improvement, with 13.41% higher brake thermal efficiency and 30.3% lower fuel usage than conventional fuel, at 100% load. B30 reduced HC by 5.88%, while B10 gave the lowest CO, reduced by 8.51%. The main contribution is the use of decision tree machine-learning regression to predict and optimize performance and emissions. The model achieved R = 0.91 and R² = 0.83, supporting the prediction of multivariable engine responses across operating conditions.

Keywords

References

  1. Paramasivam, Prabhu, Khaled Alnamasi, Abdullah MA Alsharif, and Praveen Kumar Kanti. "Performance and emission analysis of a dual-fuel engine using biogas and algal biodiesel: Machine learning prediction and response surface optimization." Case Studies in Thermal Engineering (2025): 107227. https://doi.org/10.1016/j.csite.2025.107227
  2. Pallicheruvu, Naveen Kumar, and Sakthivel Gnanasekaran. "ANN-driven prediction of optimal machine learning models for engine performance in a dual-fuel mode powered by biogas and fish oil biodiesel." Energy Conversion and Management: X 25 (2025): 100827. https://doi.org/10.1016/j.ecmx.2024.100827
  3. Ramalingam, Krishnamoorthy, Mohd Zulkifly Abdullah, P. V. Elumalai, Xu Yong, Kashi Sai Prasad, Choon Kit Chan, S. Prabhakar, and Bai Yuqi. "An evaluation of maximizing production and usage of biofuel by machine learning and experimental approach." Scientific Reports 15, no. 1 (2025): 33265. https://doi.org/10.1038/s41598-025-18757-6
  4. Pawar, Chetan, B. Shreeprakash, Beekanahalli Mokshanatha, Keval Chandrakant Nikam, Nitin Motgi, Laxmikant D. Jathar, Sagar D. Shelare et al. "Machine learning-based assessment of the influence of nanoparticles on biodiesel engine performance and emissions: a critical review." Archives of Computational Methods in Engineering 32, no. 1 (2025): 499-533. https://doi.org/10.1007/s11831-024-10144-0
  5. Karaoğlan, Kürşat Mustafa, Burak Çiftçi, Mustafa Karagöz, and Mustafa Bahattin Çelik. "Machine learning-based cylinder pressure estimation using newly developed biodiesel-fusel oil mixtures in diesel engines." Journal of the Brazilian Society of Mechanical Sciences and Engineering 47, no. 11 (2025): 569. https://doi.org/10.1007/s40430-025-05854-w
  6. Sanjeevannavar, Mallesh B., Nagaraj R. Banapurmath, V. Dananjaya Kumar, Sushrut S. Halewadimath, Ashok M. Sajjan, Irfan Anjum Badruddin, Vinay Atgur, Manzoore Elahi M. Soudagar, and Sarfaraz Kamangar. "Experimental and machine learning-based optimization of dual-fuel engine performance using biodiesel and hydrogen-producer gas mixtures." International Journal of Hydrogen Energy 156 (2025): 150199. https://doi.org/10.1016/j.ijhydene.2025.150199
  7. Soudagar, Manzoore Elahi M., Hua-Rong Wei, Asif Afzal, Vikram Sundara, Yasser Fouad, Fahad Awjah Almehmadi, Sagar Shelare et al. "A Biofuel-Powered Study with Deep Learning Neural Networks and Dragonfly Algorithm: Optimizing CRDi Engine Performance with ZnO Nanoparticles and Cotton Seed Methyl Ester." Energy (2025): 137031. https://doi.org/10.1016/j.energy.2025.137031
  8. Qingyao, L. I., and Jasmine Siu Lee Lam. "Biofuel consumption and emission prediction for harbour craft using Machine learning methods." Transportation Research Part D: Transport and Environment 149 (2025): 105005. https://doi.org/10.1016/j.trd.2025.105005
  9. Li, Junhua, Haitao Wang, and Qi Dong. "Hybrid machine learning-based modeling of engine behavior using third-generation biodiesel: validation and robustness with SHAP explainability, bootstrapping, and sensitivity analysis." Applied Thermal Engineering (2025): 128502. https://doi.org/10.1016/j.applthermaleng.2025.128502
  10. Gadagi, Amith, Sneha Bandekar, Santhosh Paramasivam, Umesh Basanagouda Deshannavar, Natarajan Rajamohan, Chandrashekar Adake, Prasad G. Hegde, and Gianluca Gatto. "Enhancing Engine Performance and Sustainability: Gold Nanoparticles and Machine Learning for Biodiesel Optimization in Compression Ignition Systems." ACS omega 10, no. 40 (2025): 46634-46647. https://doi.org/10.1021/acsomega.5c03571
  11. Bikkavolu, Joga Rao, Rakesh Kumar Tota, Kodanda Ramarao Chebattina, Lakshmipathi Raju Bhagavatula, Gandhi Pullagura, and PraveenKumar Seepana. "Predicting Common Rail Direct Injection (CRDI) engine metrics using nanoparticle-enhanced pongamia pinnata biodiesel with machine learning." Emergent Materials (2025): 1-18. https://doi.org/10.1007/s42247-025-01175-9
  12. Giwa, Solomon O., and Raymond T. Taziwa. "Predicting fuel properties of nano-doped biodiesel using machine learning algorithms." International Journal of Ambient Energy 46, no. 1 (2025): 2505064. https://doi.org/10.1080/01430750.2025.2505064
  13. Uluocak, Ihsan, and Erinc Uludamar. "Comparative evaluation of machine learning models for predicting noise and vibration of a biodiesel-CNG fuelled diesel engine." Measurement 249 (2025): 117021. https://doi.org/10.1016/j.measurement.2025.117021
  14. Khan, Faisal, Ibrahim Alsaduni, Osama Khan, Mohd Parvez, Ashok Kumar Yadav, and Ümit Agbulut. "Enhancing biogas/biohydrogen utilization in dual-fuel engines using advanced machine learning algorithms." International Journal of Hydrogen Energy 112 (2025): 81-90. https://doi.org/10.1016/j.ijhydene.2025.02.119
  15. Abishek, M. S., and Sabindra Kachhap. "Sustainable synthesis of copper oxide nanoparticles using Spondias mombin and biodiesel production from Guizotia abyssinica: Engine performance, emission characteristics, and machine learning-based optimization." Fuel 392 (2025): 134804. https://doi.org/10.1016/j.fuel.2025.134804
  16. VS, Shaisundaram, Saravanakumar Sengottaiyan, Gunasekaran Raji, Kumaravel S, and Chandrasekaran M. "Machine Learning‐Driven Energy Efficiency Enhancement and Emission Reduction in Diesel Engines Using Pumpkin Seed Biodiesel Blends and CeO2 Nanoparticles." International Journal of Energy Research 2025, no. 1 (2025): 2329925. https://doi.org/10.1155/er/2329925
  17. Bukkarapu, Kiran Raj, and Anand Krishnasamy. "Applications of machine learning techniques to broaden operating envelope of biodiesel-fueled HCCI engines." International Journal of Engine Research 26, no. 6 (2025): 839-861. https://doi.org/10.1177/14680874241292695
  18. Janaki, Durga Venkatesh, P. S. Ranjit, and B. Balakrishna. "Impact of magnesium oxide nanoparticles and hydrogen enrichment on CI engine performance with Mahua oil biodiesel using machine learning." Heat and Mass Transfer 61, no. 8 (2025): 77. https://doi.org/10.1007/s00231-025-03610-3
  19. Al-Hamzawi, Hassan A. Hameed, Ali S. Abed Al Sailawi, Raad Z. Homod, Hayder I. Mohammed, and Mahmood A. Al-Shareeda. "Machine learning-enhanced optimization of exhaust gas recirculation strategies for superior diesel engine performance and emissions control: A synergistic experimental and computational study." International Journal of Hydrogen Energy 169 (2025): 151184. https://doi.org/10.1016/j.ijhydene.2025.151184
  20. Shaik, Jakeer Hussain, Naseem Khayum, and Krishna Kumar Pandey. "Analysis of combustion characteristics of a diesel engine run on ternary blends using machine learning algorithms." Environmental Progress & Sustainable Energy 44, no. 3 (2025): e14582. https://doi.org/10.1002/ep.14582
  21. Mansab, Saira, Saima Nasreen, and Kousar Parveen. "Role of Machine Learning and Artificial Intelligence in Biofuel/Bioenergy Productions." In Recent Trends in Lignocellulosic Biofuels and Bioenergy: Advancements and Sustainability Assessment, pp. 375-398. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4636-4_13
  22. Yeşilova, Kaan, Özgün Yücel, and Başak Temur Ergan. "Modeling prediction of physical properties in sustainable biodiesel-diesel-alcohol blends via experimental methods and machine learning." Processes 13, no. 7 (2025): 2310. https://doi.org/10.3390/pr13072310
  23. Nachippan, Murugu, P. Pathmanabhan, Beemkumar Nagappan, Vijay J. Upadhye, Nandagopal Kaliappan, V. Balaji, and K. Kamakshi Priya. "Machine learning predictions for enhancing engine performance and emission using aluminum oxide nano additives in castor biodiesel." Scientific Reports 15, no. 1 (2025): 36514. https://doi.org/10.1038/s41598-025-02388-y
  24. Khambhammettu, Gnana Likhitha, Venkata Sai Yaswant Geddavalasa, Jaikumar Sagari, Srinivas Vadapalli, and Satya Meher Raghavarapu. "Performance and emission characteristics of diesel engines running on nanofuel: an experimental and machine learning prediction study." Emergent Materials 8, no. 3 (2025): 1491-1508. https://doi.org/10.1007/s42247-024-00957-x
  25. Shaisundaram, V. S., P. V. Elumalai, S. Padmanabhan, U. Nalini Ramachandran, Abhishek Kumar Tripathi, Cui Yaping, B. Nagaraj Goud, and S. Prabhakar. "Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights." Scientific Reports 15, no. 1 (2025): 26457. https://doi.org/10.1038/s41598-025-04033-0
  26. Anderson, A., Sulaiman Ali Alharbi, Arunachalam Chinnathambi, Rama Raju PJ, and Beata Gavurová. "Experimental investigation and machine learning prediction of thrust, fuel consumption, and emissions for micro gas turbine engine fueled with biofuel and hydrogen-A comparative study of linear regression and LSTM model." Journal of the Taiwan Institute of Chemical Engineers (2025): 106104. https://doi.org/10.1016/j.jtice.2025.106104
  27. Kumar, K. Sunil, Abdul Razak, M. K. Ramis, Shaik Mohammad Irshad, Saiful Islam, and Anteneh Wogasso Wodajo. "Statistical and machine learning analysis of diesel engines fueled with Moringa oleifera biodiesel doped with 1-hexanol and Zr2O3 nanoparticles." Scientific Reports 15, no. 1 (2025): 7269. https://doi.org/10.1038/s41598-025-87818-7
  28. Aswathanrayan, M. S., N. Santhosh, Srikanth Holalu Venkataramana, Kurugundla Sunil Kumar, Sarfaraz Kamangar, Amir Ibrahim Ali Arabi, Sameer Algburi, Osamah J. Al-Sareji, and A. Bhowmik. "Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms." Scientific Reports 15, no. 1 (2025): 12683. https://doi.org/10.1038/s41598-025-97092-2
  29. Hao, Jiongju, Mohammad Ahmad Wadaann, and G. K. Jhanani. "Machine learning enabled real time adaptive fuel injection control for optimized engine performance." Renewable Energy (2025): 125013. https://doi.org/10.1016/j.renene.2025.125013
  30. Subramanian, Karthikeyan, Sathiyagnanam Amudhavalli Paramasivam, Damodharan Dillikannan, and Sekar SD. "Optimizing Thermal Efficiency in Diesel Engines: Predicting Performance with Ternary Blends, Variable Injection Pressures and EGR Using LSTM Machine Learning." Isı Bilimi ve Tekniği Dergisi 45, no. 2 (2025): 272-284. https://doi.org/10.47480/isibted.1642863
  31. Mohammad, Suleiman Ibrahim, Hamza Abu Owida, Asokan Vasudevan, Soumya V. Menon, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, and Fereydoon Ranjbar. "Thermophysical properties of used frying oil biodiesels blended with alcohols: Robust machine learning frameworks for density prediction." Industrial Crops and Products 236 (2025): 121916. https://doi.org/10.1016/j.indcrop.2025.121916
  32. Khayum, Naseem, Jakeer Hussain Shaik, and Yerumbu Nandakishora. "Machine learning and deep learning prediction of in-cylinder pressure and heat release rate in an NH3-fueled diesel engine." Applied Thermal Engineering (2025): 128684. https://doi.org/10.1016/j.applthermaleng.2025.128684
  33. Heeraman, Jatoth. "Artificial neural network analysis of performance and emissions for mixed biodiesel blends in a DI diesel engine." Thermal Science and Engineering Progress (2025): 104218. https://doi.org/10.1016/j.tsep.2025.104218
  34. Kumar, K. Sunil, C. Mahesh, Suresh Shetty, M. K. Ramis, Abdul Razak, Abdullah H. Alsabhan, Shamshad Alam, and Osamah J. Alsareji. "Machine learning-based prognostics and statistical optimization of the performance of Momordica charantia biodiesel blends with TiO2 nanoparticles." Biofuels (2025): 1-38.
  35. Karaoglan, Kürşat Mustafa, and Mehmet Çelik. "Preparation of nanoparticle-enriched fuels and prediction of cylinder pressure through machine learning models." Arabian Journal for Science and Engineering 50, no. 12 (2025): 9553-9581. https://doi.org/10.1007/s13369-024-09653-8
  36. Venu, Harish, V. Dhana Raju, Jayashri N. Nair, Sameer Algburi, Ali E. Anqi, Ali A. Rajhi, and Mohammed Kareemullah. "Exergy and Energy-Based Sustainability Evaluation of Diesel-Biodiesel-Ethanol Blends with Emission Forecasting using Advanced Machine Learning Models." Case Studies in Thermal Engineering (2025): 106516. https://doi.org/10.1016/j.csite.2025.106516
  37. Sharma, Prabhakar. "Gene expression programming-based model prediction of performance and emission characteristics of a diesel engine fueled with linseed oil biodiesel/diesel blends: An artificial intelligence approach." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 47, no. 1 (2025): 1385-1399. https://doi.org/10.1080/15567036.2020.1829204
  38. Ahmad, A., Yadav, A. K., & Singh, A., Enhancing waste cooking oil biodiesel yield and characteristics through machine learning, response surface methodology, and genetic algorithms for optimal utilization in CI engines. International Journal of Green Energy, 21(6), 1345-1365, (2024). https://doi.org/10.1080/15435075.2023.2253870
  39. Milivojčević, M., Čirić, D., Prezelj, J., &Murovec, J., Analysis of unsupervised learning approach for classification of vehicle fuel type using psychoacoustic features. Measurement, 227, 114318, (2024). https://doi.org/10.1016/j.measurement.2024.114318
  40. Sanjeevannavar, M. B., Banapurmath, N. R., Kumar, V. D., Sajjan, A. M., Badruddin, I. A., Vadlamudi, C., ... & Khan, T. Y. (2023). Machine learning prediction and optimization of performance and emissions characteristics of IC engine. Sustainability, 15(18), 13825. https://doi.org/10.3390/su151813825
  41. Canal, R., Riffel, F. K., &Gracioli, G., Machine learning for real-time fuel consumption prediction and driving profile classification based on ECU data. IEEE Access, (2024). https://doi.org/10.1109/ACCESS.2024.3400933
  42. Viana, D. P., de SáSó Martins, D. H., de Lima, A. A., Silva, F., Pinto, M. F., Gutiérrez, R. H., ...& Haddad, D. B., Diesel engine fault prediction using artificial intelligence regression methods. Machines, 11(5), 530, (2023). https://doi.org/10.3390/machines11050530
  43. Sivaramakrishna, V., Hussain, S., Kiran, C.R., Nair, J.N., Badruddin, I.A., Shaik, A.S., Kamangar, S., Ali, M.M. and Bashir, M.N., 2024. Experimental and simulation study of American saffron seed oil blended with diesel. Heliyon, 10(15). https://doi.org/10.1016/j.heliyon.2024.e34959
  44. Valiveti, S.R.K., Shaik, H. and Reddy, K.V.K., 2022. Analysis on impact of thermal barrier coating on piston head in CI engine using biodiesel. International Journal of Ambient Energy, 43(1), pp.3377-338 https://doi.org/10.1080/01430750.2020.1831592