Abstract
This work presents a comprehensive experimental and data-driven study on the feasibility of pine-oil-aided premixed charge compression ignition (PCCI) combustion under low-temperature combustion (LTC) conditions in a variable compression ratio (VCR) diesel engine using conventional diesel and biodiesel blends to operate as pilot fuel. Different amounts of Pine Oil (PO) were used at 16, 17.5, and 19 compression ratios with different engine loads during tests. Performance and emissions results such as BTE, BSFC, CO, HC, NOx, and smoke opacity were examined. RSM generated statistically significant quadratic models and was used for simultaneous multi-objective optimisation. The optimal operating condition is CR = 19 with 30% PO with 80% load. This yielded a peak BTE of 35.4%, a minimum BSFC of 0.25 kg/kWh, a CO level of 0.022%, an HC level of 31 ppm and a smoke opacity of 21 HSU. NOx: an increase (1120 ppm) was also observed. In the present work, nine regression models were employed in a framework for machine learning. Among various models, the Gradient Boosting Machine had the highest prediction accuracy (R2 > 0.95). SHAP-based explainable AI revealed that engine load, compression ratio, and fuel properties were the most influential on how combustion behaved. The TQM and sustainability assessment based on the Pugh matrix indicated that the use of PO to enable operating PCCI at higher compression ratios offers the best compromise of efficiency with low emissions and sustainability between the different options. These combined outcomes indicate that PO has significant potential as a renewable fuel for advanced low-carbon compression ignition engines.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Abbreviations
- ANN:
-
Artificial neural network
- ANOVA:
-
Analysis of variance
- BTE:
-
Brake thermal efficiency
- BSFC:
-
Brake specific fuel consumption
- COV:
-
Coefficient of variation
- CO:
-
Carbon monoxide
- CO₂:
-
Carbon dioxide
- CR:
-
Compression ratio
- CV10:
-
10% Microalgae biodiesel + 90% diesel (pilot fuel)
- CV20:
-
20% Microalgae biodiesel + 80% diesel (pilot fuel)
- D100:
-
Neat diesel
- DMAIC:
-
Define–measure–analyse–improve–control
- DNN:
-
Deep neural network
- EGR:
-
Exhaust gas recirculation
- ELM:
-
Extreme learning machine
- GBM:
-
Gradient boosting machine
- GUI:
-
Graphical user interface
- GPR:
-
Gaussian process regression
- HC:
-
Unburned hydrocarbons
- HSU:
-
Hartridge smoke unit
- IC:
-
Internal combustion
- KPI:
-
Key performance indicator
- LHV:
-
Lower heating value
- ML:
-
Machine learning
- NOx:
-
Oxides of nitrogen
- PO10%:
-
10% Pine oil
- PO20%:
-
20% Pine oil
- PO30%:
-
30% Pine oil
- PCCI:
-
Premixed charge compression ignition
- Q–Q Plot:
-
Quantile–quantile plot
- R2 :
-
Coefficient of determination
- RSM:
-
Response surface methodology
- SHAP:
-
SHapley Additive exPlanations
- SPI:
-
Sustainability performance index
- SVM:
-
Support vector machine
- SVR:
-
Support vector regression
- TPI:
-
TQM performance index
- TQM:
-
Total quality management
- VCR:
-
Variable compression ratio
- XGBoost:
-
Extreme gradient boosting
References
Jayakar, J., Gunasekar, N. & Elumalai, P. V. Combustion and emission analysis of hydrogen–microalgae biodiesel dual-fuel CI engine with cold EGR. J. Therm. Anal. Calorim. https://doi.org/10.1007/s10973-025-14879-1 (2025).
Gurusamy, M. & Subramanian, B. Study of PCCI engine operating on pine oil diesel blend (P50) with benzyl alcohol and diethyl ether. Fuel 335, 127121 (2023).
Allmägi, R., Ilves, R. & Olt, J., Comprehensive review of innovation in piston engine and low temperature combustion technologies. Transport. 39, 86–113 (2024).
Bharadwaz, Y. D. & Kumari, A. S. PCCI combustion of low-carbon alternative fuels: a review. J. Therm. Anal. Calorim. 148, 5179–5207 (2023).
Cao, D. N., Hoang, A. T., Luu, H. Q., Bui, V. G. & Tran, T. T. H. Effects of injection pressure on the NOx and PM emission control of diesel engine: A review under the aspect of PCCI combustion condition. Energy Sour. Part A Recover. Util. Environ. Eff. 46, 7414–7431 (2024).
Rameez, P. V. & Mohamed Ibrahim M. A comprehensive review on the utilization of hydrogen in low temperature combustion strategies: Combustion, performance and emission attributes. J. Energy Inst. 113, 101511 (2024).
Le, T. T. et al. Partially-charged advanced low-temperature combustion in diesel engine: Progress and prospects. Alex. Eng. J. 129, 373–440 (2025).
Hua, Y. Ethers and esters as alternative fuels for internal combustion engine: A review. Int. J. Engine Res. 24, 178–216 (2023).
Zhou, J., Chen, L., Zhang, R. & Zhao, W. Study on oxidation activity of hydrogenated biodiesel–ethanol–diesel blends. Processes. 12, 462 (2024).
Muniyappan, S. & Krishnaiah, R. Investigation on PCCI combustion with cetane improver as secondary fuel for hybrid engine application. Results Eng. 28, 107372 (2025).
Rahman Adib, A., Mizanur Rahman, M., Hassan, T., Ahmed, M. & Al Rifat, A. Novel biofuel blends for diesel engines: Optimizing engine performance and emissions with C. cohnii microalgae biodiesel and algae-derived renewable diesel blends. Energy Convers. Manag. X. 23, 100688 (2024).
Ihsan Shahid, M. et al. Hydrogen production techniques and use of hydrogen in internal combustion engine: A comprehensive review. Fuel 378, 132769 (2024).
Zhu, J. et al. Sooting tendencies of terpenes and hydrogenated terpenes as sustainable transportation biofuels. Proc. Combust. Inst. 39, 877–887 (2023).
CHIVU, R. M. The use of turpentine as additive for diesel oil. A review. J. Therm. Eng. 880–895. https://doi.org/10.14744/thermal.0000952 (2025).
Neupane, D. Biofuels from renewable sources, a potential option for biodiesel production. Bioengineering. 10, 29 (2022).
Jhanani, G. K., Salmen, S. H., Obaid, A., Mathimani, T. & S. & Performance and emission analysis of Scenedesmus dimorphus-based biodiesel with hydrogen as fuel in an unmodified compression ignition engine. Int. J. Hydrogen Energy. 99, 1132–1138 (2025).
Sindhu, R., Prabhat, S. T., Hiep, B. T., Chinnathambi, A. & Alharbi, S. A. Experimental assessment of cork based Botryococcus braunii microalgae blends and hydrogen in modified multicylinder diesel engine. Fuel. 359, 130468 (2024).
Veza, I., Spraggon, M., Fattah, I. M. R. & Idris, M. Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition. Results Eng. 18, 101213 (2023).
Lestari, W. D. et al. Optimization of 3D printed parameters for socket prosthetic manufacturing using the taguchi method and response surface methodology. Results Eng. 21, 101847 (2024).
Passerine, B. F. G. & Breitkreitz, M. C. Important aspects of the design of experiments and data treatment in the analytical quality by design framework for chromatographic method development. Molecules. 29, 6057 (2024).
Modi, V. et al. Machine learning and response surface optimization to enhance diesel engine performance using milk scum biodiesel with alumina nanoparticles. Sci. Rep. 15, 34243 (2025).
Lionus Leo, G. M., Jayabal, R., Kathapillai, A. & Sekar, S. Performance and emissions optimization of a dual-fuel diesel engine powered by cashew nut shell oil biodiesel/hydrogen gas using response surface methodology. Fuel 384, 133960 (2025).
Pan, X., Guan, W., Gu, J., Wang, X. & Zhao, H. Optimization of the low-load performance and emission characteristics for a heavy-duty diesel engine fueled with diesel/methanol by RSM-NSWOA. Renew. Energy. 245, 122819 (2025).
Li, J., Wang, H. & Dong, Q. Hybrid machine learning-based modeling of engine behavior using third-generation biodiesel: validation and robustness with SHAP explainability, bootstrapping, and sensitivity analysis. Appl. Therm. Eng. 281, 128502 (2025).
Kamarulzaman, M. K. & Abdullah, A. Multi-objective optimization of diesel engine performances and exhaust emissions characteristics of Hermetia illucens larvae oil-diesel fuel blends using response surface methodology. Energy Sour. Part A Recover. Util. Environ. Eff. 47, 2952–2965 (2025).
Eskandari, H., Saadatmand, H., Ramzan, M. & Mousapour, M. Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning. Appl. Energy. 366, 123314 (2024).
Alam, G. M. I. et al. Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment. Sci. Rep. 15, 3655 (2025).
Houdou, A. et al. Interpretable machine learning approaches for forecasting and predicting air pollution: A systematic review. Aerosol Air Qual. Res. 24, 230151 (2024).
Latha, K. R. & Jagwani, S. Interpretable and robust machine learning approaches for electric vehicle range prediction using SHAP values. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), 1–6 (IEEE, 2025). https://doi.org/10.1109/IACIS65746.2025.11210912
Chen, Y. et al. Machine learning-based design of target property-oriented fuels using explainable artificial intelligence. Energy 300, 131583 (2024).
Yuan, D., Tang, L., Yang, X., Xu, F. & Liu, K. Explainable machine learning prediction of vehicle CO2 emissions for sustainable energy and transport. Energies (Basel). 18, 5408 (2025).
Žvirblis, T., Čižiūnienė, K. & Matijošius, J. Application of machine learning for fuel consumption and emission prediction in a marine diesel engine using diesel and waste cooking oil. J. Mar. Sci. Eng. 13, 1328 (2025).
Psarommatis, F. & Azamfirei, V. Zero defect manufacturing: A complete guide for advanced and sustainable quality management. J. Manuf. Syst. 77, 764–779 (2024).
Pawanr, S., Sarmah, P. & Gupta, K. TQM implementation for enhancement of product and service quality: A review on developments and latest trends. J. Appl. Res. Technol. Eng. 6, 63–72 (2025).
Waseem, M. & Yusoff, Y. M. The effect of total quality management practices on supply chain performance in the automobile industry. Multidiscipl. Sci. J. 7, 2025077 (2024).
Maganga, D. P. & Taifa, I. W. R. Quality 4.0 conceptualisation: an emerging quality management concept for manufacturing industries. TQM J. 35, 389–413 (2023).
Antwi, S. & Darkwa Fentim, B. Total quality management and organizational performance: A literature review. SSRN Electron. J. https://doi.org/10.2139/ssrn.4230846 (2021).
DeHart, S. P. The Six Sigma Handbook, 4th edn. J. Qual. Technol. 47, (2015).
Jensen, W. A. Response surface methodology: Process and product optimization using designed experiments 4th edition. J. Qual. Technol. 49, (2017).
Sharma, P. et al. Experimental investigations on efficiency and instability of combustion process in a diesel engine fueled with ternary blends of hydrogen peroxide additive/biodiesel/diesel. Energy Sour. Part A Recover. Util. Environ. Eff. 44, 5929–5950 (2022).
Sunil Kumar, K. et al. Performance, combustion, and emission analysis of diesel engine fuelled with pyrolysis oil blends and n-propyl alcohol-RSM optimization and ML modelling. J. Clean. Prod. 434, 140354 (2024).
Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research and Graduate Studies at King Khalid University for funding this work under Grant No. RGP2/437/45. Authors also thank Multimedia University (MMU) for their support through the MMU IR Fund (Project ID: MMUI/220041).
Funding
The authors extend their appreciation to the Deanship of Scientific Research and Graduate Studies at King Khalid University for funding this work under Grant No. RGP2/437/45.
Author information
Authors and Affiliations
Contributions
M.A.A.: Conceptualization, RSM and statistical analysis, machine learning modeling, validation, data curation, writing-original draft. G.K.O.M.: Methodology, experimental investigation, writing-review and editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Al Awadh, M., Michael, G.K.O. Response surface and TQM-ML analysis of a PCCI engine fueled with PO and microalgae biodiesel. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40929-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-40929-1
