Optimization of injection parameters, and ethanol shares for cottonseed biodiesel fuel in diesel engine utilizing artificial neural network (ANN) and taguchi grey relation analysis (GRA)

G. Praveen Kumar Yadav; Pullarao Muvvala; R. Meenakshi Reddy

doi:10.1515/jnet-2024-0095

Article

Optimization of injection parameters, and ethanol shares for cottonseed biodiesel fuel in diesel engine utilizing artificial neural network (ANN) and taguchi grey relation analysis (GRA)

G. Praveen Kumar Yadav , Pullarao Muvvala and R. Meenakshi Reddy

Published/Copyright: March 13, 2025

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information

From the journal Journal of Non-Equilibrium Thermodynamics Volume 50 Issue 3

Abstract

The increase of fossil fuel powered industrial processes and vehicles has resulted in the exhaustion of petroleum reserves and pollution of the environment. Because of its clean-burning, renewable, and biodegradable qualities, biodiesel is becoming more and more recognized as a potential diesel fuel alternative. The present study investigates engine performance and emission characteristics of cottonseed oil (CSBD20) and diesel blends tested on single-cylinder compression ignition engine by several injection timings, injection pressures, and ethanol shares. Performance parameters such as brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), exhaust emissions such as hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO_x), carbon dioxide (CO₂), and smoke were considered as output factors, considering injection timing (IT), ethanol share (ES), injection pressure (IP) as input factors utilizing artificial neural network (ANN) and taguchi grey relation analysis (GRA). The ANN model accurately predicts the input-output relationships of ethanol and cottonseed biodiesel blends, as validated by experimental comparisons. The predicted values for BTE, BSFC, HC, CO, NO_x, and smoke show close alignment with experimental results, with marginal errors of 6.2 %, 2.8 %, 7.1 %, 4.7 %, 6.8 %, and 5.6 %, respectively, confirming its reliability. In addition, this study utilized Taguchi grey relational analysis (GRA) to find optimum engine operating conditions. The analysis revealed that the optimal engine operating conditions were IT at 27° CA bTDC, ES at 15 %, and IP at 200 bar. Furthermore, confirmation tests are also conducted at optimum operating conditions, and the revealed values are closer to taguchi GRA experiments and ANN predicted values.

Keywords: ethanol; VCR engine; taguchi; signal noise ratio; grey relational analysis; ANN

Corresponding author: G. Praveen Kumar Yadav, Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing, Kurnool, Andhra Pradesh, India; and Department of Mechanical Engineering, G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India, E-mail: gandikotapraveenkumar@gmail.com

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: G. Praveen Kumar Yadav: Conceptualization, Investigation, Methodology, Writing. Pullarao Muvvala: Review & editing, Supervision. R. Meenakshi Reddy: Review & editing, Supervision.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: All other authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

Nomenclature

CSBD20: cottonseed biodiesel
BTE: brake thermal efficiency
BSFC: brake-specific fuel consumption
HC: hydrocarbon
CO: carbon monoxide
NO_x: nitrogen oxides
CO₂: carbon dioxide
CI: compression ignition
IT: injection timing
IP: injection pressure
ES: ethanol share
ANN: artificial neural network
GRA: gray relation analysis
bTDC: before top dead center
RSM: response surface methodology
EGT: exhaust gas temperature
CR: compression ratio
BMEP: brake mean effective pressure
CRDI: common rail direct injection
DOE: design of experiments
R: coefficient of correlation
MSE: mean square error
SNR: signal noise ratio
GRG: gray relation grade
GRC: gray relation coefficient

References

[1] S. D. Shelare, et al.., “Biofuels for a sustainable future: examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production,” Energy, vol. 282, 2023, Art. no. 128874. https://doi.org/10.1016/J.ENERGY.2023.128874.Search in Google Scholar

[2] U. Rashid, F. Anwar, and G. Knothe, “Evaluation of biodiesel obtained from cottonseed oil,” Fuel Process. Technol., vol. 90, no. 9, pp. 1157–1163, 2009. https://doi.org/10.1016/J.FUPROC.2009.05.016.Search in Google Scholar

[3] A. Ashok, S. K. Gugulothu, R. V. Reddy, and B. Burra, “Influence of fuel injection timing and trade-off study on the RCCI engine characteristics of Jatropha oil-diesel blend under 1-pentanol dual-fuel strategies,” Environ. Sci. Pollut. Res., vol. 30, no. 44, pp. 98848–98857, 2023. https://doi.org/10.1007/s11356-022-22039-3.Search in Google Scholar PubMed PubMed Central

[4] B. Shadidi, H. H. A. Alizade, and G. Najafi, “The influence of diesel–ethanol fuel blends on performance parameters and exhaust emissions: experimental investigation and multi-objective optimization of a diesel engine,” Sustain, vol. 13, no. 10, p. 5379, 2021. https://doi.org/10.3390/SU13105379.Search in Google Scholar

[5] M. Vargün and A. N. Özsezen, “Evaluation of the effect of the fuel injection phase on the combustion and exhaust characteristics in a diesel engine operating with alcohol-diesel mixtures,” Energy, vol. 270, 2023, Art. no. 126975. https://doi.org/10.1016/J.ENERGY.2023.126975.Search in Google Scholar

[6] J. Fu, et al.., “Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine,” Appl. Therm. Eng., vol. 201, 2022, Art. no. 117749. https://doi.org/10.1016/J.APPLTHERMALENG.2021.117749.Search in Google Scholar

[7] I. Veza, et al.., “Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine: review of ANN for gasoline, diesel and HCCI engine,” Alexandria Eng. J., vol. 61, no. 11, pp. 8363–8391, 2022. https://doi.org/10.1016/j.aej.2022.01.072.Search in Google Scholar

[8] A. Pathak, P. K. Choudhury, and R. K. Dutta, “Taguchi-grey relational based multi-objective optimization of process parameters on the emission and fuel consumption characteristics of A VCR petrol engine,” Mater. Today: Proc., vol. 5, no. 2, pp. 4702–4710, 2018. https://doi.org/10.1016/J.MATPR.2017.12.042.Search in Google Scholar

[9] R. Kumar Rai and R. Rekha Sahoo, “Taguchi-Grey method optimization of VCR engine performance and heat losses by using Shorea robusta biodiesel fuel,” Fuel, vol. 281, 2020, Art. no. 118399. https://doi.org/10.1016/j.fuel.2020.118399.Search in Google Scholar

[10] A. N. Kumar, et al.., “Decanol proportional effect prediction model as additive in palm biodiesel using ANN and RSM technique for diesel engine,” Energy, vol. 213, 2020, Art. no. 119072. https://doi.org/10.1016/j.energy.2020.119072.Search in Google Scholar

[11] M. Mariappan, M. S. Panithasan, and G. Venkadesan, “Pyrolysis plastic oil production and optimisation followed by maximum possible replacement of diesel with bio-oil/methanol blends in a CRDI engine,” J. Cleaner Prod., vol. 312, 2021, Art. no. 127687. https://doi.org/10.1016/j.jclepro.2021.127687.Search in Google Scholar

[12] S. Park, J. Cho, J. Park, and S. Song, “Numerical study of the performance and NOx emission of a diesel-methanol dual-fuel engine using multi-objective Pareto optimization,” Energy, vol. 124, pp. 272–283, 2017. https://doi.org/10.1016/j.energy.2017.02.029.Search in Google Scholar

[13] A. K. Das, M. R. Padhi, D. Hansdah, and A. K. Panda, “Optimization of engine parameters and ethanol fuel additive of a diesel engine fuelled with waste plastic oil blended diesel, process integr,” Optim. Sustain., vol. 4, no. 4, pp. 465–479, 2020. https://doi.org/10.1007/s41660-020-00134-7.Search in Google Scholar

[14] D. Kashyap, S. Das, and P. Kalita, “Exploring the efficiency and pollutant emission of a dual fuel CI engine using biodiesel and producer gas: an optimization approach using response surface methodology,” Sci. Total Environ., vol. 773, 2021, Art. no. 145633. https://doi.org/10.1016/j.scitotenv.2021.145633.Search in Google Scholar PubMed

[15] T. S. Singh, et al.., “Optimization of performance and emission parameters of direct injection diesel engine fuelled with microalgae Spirulina (L.) – response surface methodology and full factorial method approach,” Fuel, vol. 285, 2021, Art. no. 119103. https://doi.org/10.1016/j.fuel.2020.119103.Search in Google Scholar

[16] Y. Heng Teoh, et al.., “Optimization of engine out responses with different biodiesel fuel blends for energy transition,” Fuel, vol. 318, 2022, Art. no. 123706. https://doi.org/10.1016/j.fuel.2022.123706.Search in Google Scholar

[17] Z. Said, et al.., “Optimization of combustion, performance, and emission characteristics of a dual-fuel diesel engine powered with microalgae-based biodiesel/diesel blends and oxyhydrogen,” Fuel, vol. 326, 2022, Art. no. 124987. https://doi.org/10.1016/J.FUEL.2022.124987.Search in Google Scholar

[18] R. Manimaran, T. Mohanraj, M. Venkatesan, R. Ganesan, and D. Balasubramanian, “A computational technique for prediction and optimization of VCR engine performance and emission parameters fuelled with Trichosanthes cucumerina biodiesel using RSM with desirability function approach,” Energy, vol. 254, 2022, Art. no. 124293. https://doi.org/10.1016/J.ENERGY.2022.124293.Search in Google Scholar

[19] P. Sankar, M. Thangavelu, V. Moorthy, S. Mahaboob Subhani, and R. Manimaran, “Prediction and optimization of diesel engine characteristics for various fuel injection timing: operated by third generation green fuel with alumina nano additive,” Sustainable Energy Technol. Assess., vol. 53, 2022, Art. no. 102751. https://doi.org/10.1016/J.SETA.2022.102751.Search in Google Scholar

[20] G. P. K. Yadav, P. Muvvala, and R. M. Reddy, “Study of performance, emission characteristics, and parametric optimization of compression ignition engine using biofuels: a review,” Biofuels, vol. 15, no. 9, pp. 1–18, 2024. https://doi.org/10.1080/17597269.2024.2340178.Search in Google Scholar

[21] K. C. Khaire, V. S. Moholkar, and A. Goyal, “Bioconversion of sugarcane tops to bioethanol and other value added products: an overview,” Mater. Sci. Energy Technol., vol. 4, pp. 54–68, 2021. https://doi.org/10.1016/J.MSET.2020.12.004.Search in Google Scholar

[22] P. Karin, et al.., “Influence of ethanol-biodiesel blends on diesel engines combustion behavior and particulate matter physicochemical characteristics,” Case Stud. Chem. Environ. Eng., vol. 6, 2022, Art. no. 100249. https://doi.org/10.1016/J.CSCEE.2022.100249.Search in Google Scholar

[23] N. Yilmaz, “Comparative analysis of biodiesel–ethanol–diesel and biodiesel–methanol–diesel blends in a diesel engine,” Energy, vol. 40, pp. 210–213, 2012. https://doi.org/10.1016/J.ENERGY.2012.01.079.Search in Google Scholar

[24] W. N. Maawa, R. Mamat, G. Najafi, and L. P. H. De Goey, “Performance, combustion, and emission characteristics of a CI engine fueled with emulsified diesel-biodiesel blends at different water contents,” Fuel, vol. 267, 2020, Art. no. 117265. https://doi.org/10.1016/j.fuel.2020.117265.Search in Google Scholar

[25] P. Shrivastava, T. N. Verma, and A. Pugazhendhi, “An experimental evaluation of engine performance and emisssion characteristics of CI engine operated with Roselle and Karanja biodiesel,” Fuel, vol. 254, 2019, Art. no. 115652. https://doi.org/10.1016/J.FUEL.2019.115652.Search in Google Scholar

[26] R. L. Patel and C. D. Sankhavara, “Biodiesel production from Karanja oil and its use in diesel engine: a review,” Renewable Sustainable Energy Rev., vol. 71, pp. 464–474, 2017. https://doi.org/10.1016/J.RSER.2016.12.075.Search in Google Scholar

[27] T. Vadivelu, L. Ramanujam, R. Ravi, S. K. Vijayalakshmi, and M. Ezhilchandran, “An exploratory study of direct injection (DI) diesel engine performance using CNSL—ethanol biodiesel blends with hydrogen,” Energies, vol. 16, no.1, p. 415, 2023, (2022). https://doi.org/10.3390/EN16010415.Search in Google Scholar

[28] A. Sethin, Y. M. Oo, J. Thawornprasert, and K. Somnuk, “Effects of blended diesel-biodiesel fuel on emissions of a common rail direct injection diesel engine with different exhaust gas recirculation rates,” ACS Omega, vol. 9, no. 22, pp. 20906–20918, 2024. https://doi.org/10.1021/ACSOMEGA.3C10125/ASSET/IMAGES/LARGE/AO3C10125_0005.JPEG.Search in Google Scholar

[29] H. W. Wu and Z. Y. Wu, “Using Taguchi method on combustion performance of a diesel engine with diesel/biodiesel blend and port-inducting H2,” Appl. Energy, vol. 104, pp. 362–370, 2013. https://doi.org/10.1016/J.APENERGY.2012.10.055.Search in Google Scholar

[30] S. Sayyed, R. K. Das, and K. Kulkarni, “Performance assessment of multiple biodiesel blended diesel engine and NOx modeling using ANN,” Case Stud. Therm. Eng., vol. 28, 2021, Art. no. 101509. https://doi.org/10.1016/j.csite.2021.101509.Search in Google Scholar

[31] F. Soltani, M. Hajian, D. Toghraie, A. Gheisari, N. Sina, and A. Alizadeh, “Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs,” Case Stud. Therm. Eng., vol. 26, 2021, Art. no. 101122. https://doi.org/10.1016/J.CSITE.2021.101122.Search in Google Scholar

[32] Y. Li, M. Jia, X. Han, and X. S. Bai, “Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA),” Energy, vol. 225, 2021, Art. no. 120331. https://doi.org/10.1016/J.ENERGY.2021.120331.Search in Google Scholar

[33] V. C. Liyanaarachchi, G. K. S. H. Nishshanka, M. Sakarika, P. H. V. Nimarshana, T. U. Ariyadasa, and M. Kornaros, “Artificial neural network (ANN) approach to optimize cultivation conditions of microalga Chlorella vulgaris in view of biodiesel production,” Biochem. Eng. J., vol. 173, 2021, Art. no. 108072. https://doi.org/10.1016/J.BEJ.2021.108072.Search in Google Scholar

[34] S. Pitchaiah, D. Juchelková, R. Sathyamurthy, and A. E. Atabani, “Prediction and performance optimisation of a DI CI engine fuelled diesel–Bael biodiesel blends with DMC additive using RSM and ANN: energy and exergy analysis,” Energy Convers. Manage., vol. 292, 2023, Art. no. 117386. https://doi.org/10.1016/j.enconman.2023.117386.Search in Google Scholar

[35] M. Şen, “Optimization of performance and emission of a diesel engine fueled with isopropyl alcohol Blends: a comparative ANN-GA and RSM-HCO application,” Eng. Sci. Technol. Int J., vol. 55, 2024, Art. no. 101733. https://doi.org/10.1016/j.jestch.2024.101733.Search in Google Scholar

[36] B. R. Hosamani, S. Abbas Ali, and V. Katti, “Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: artificial neural network approach,” Alexandria Eng. J., vol. 60, no. 6, pp. 837–844, 2021. https://doi.org/10.1016/J.AEJ.2020.10.012.Search in Google Scholar

[37] G. Alemayehu, D. Firew, R. B. Nallamothu, A. Wako, and R. Gopal, “Operating parameters optimization for lower emissions in diesel engine with PCCI-DI mode using Taguchi and grey relational analysis,” Heliyon, vol. 8, 2022, Art. no. e09679. https://doi.org/10.1016/J.HELIYON.2022.E09679.Search in Google Scholar PubMed PubMed Central

[38] S. Roy, A. K. Das, and R. Banerjee, “Application of Grey–Taguchi based multi-objective optimization strategy to calibrate the PM–NHC–BSFC trade-off characteristics of a CRDI assisted CNG dual-fuel engine,” J. Nat. Gas Sci. Eng., vol. 21, pp. 524–531, 2014. https://doi.org/10.1016/J.JNGSE.2014.09.022.Search in Google Scholar

[39] O. O. Agboola, et al.., “Optimization of heat treatment parameters of medium carbon steel quenched in different media using Taguchi method and grey relational analysis,” Heliyon, vol. 6, 2020, Art. no. e04444. https://doi.org/10.1016/J.HELIYON.2020.E04444.Search in Google Scholar PubMed PubMed Central

[40] R. Elumalai, et al.., “Experimental investigation and gray relational optimization of engine parameters to improve the output characteristics of an ammonia biodiesel powered dual fuel combustion engine,” Case Stud. Therm. Eng., vol. 56, 2024, Art. no. 104197. https://doi.org/10.1016/J.CSITE.2024.104197.Search in Google Scholar

[41] M. Gul, et al.., “Multi-objective-optimization of process parameters of industrial-gas-turbine fueled with natural gas by using Grey-Taguchi and ANN methods for better performance,” Energy Rep., vol. 6, pp. 2394–2402, 2020. https://doi.org/10.1016/J.EGYR.2020.08.002.Search in Google Scholar

[42] I. Yalagudige Dharmegowda, L. Madarakallu Muniyappa, A. B. Suresh, M. P. Gowdru Chandrashekarappa, and N. B. Pradeep, “Optimization for waste coconut and fish oil derived biodiesel with MgO nanoparticle blend: grey relational analysis, grey wolf optimization, driving training based optimization and election based optimization algorithm,” Fuel, vol. 338, 2023, Art. no. 127249. https://doi.org/10.1016/J.FUEL.2022.127249.Search in Google Scholar

[43] A. R. Patil, S. A. Patil, R. Patil, A. M. Pawar, V. N. Chougule, and K. AboRas, “Validation of effect of composite additive on optimized combustion characteristics of CI engine using AHP and GRA method,” Heliyon, vol. 10, 2024, Art. no. e34216. https://doi.org/10.1016/J.HELIYON.2024.E34216.Search in Google Scholar PubMed PubMed Central

[44] R. Raj, D. Kumar Singh, and J. Vachan Tirkey, “Performance simulation and optimization of SI engine fueled with peach biomass-based producer gas and propane blend,” Therm. Sci. Eng. Prog., vol. 41, 2023, Art. no. 101816. https://doi.org/10.1016/J.TSEP.2023.101816.Search in Google Scholar

[45] M. Muqeem, A. F. Sherwani, M. Ahmad, and Z. A. Khan, “Taguchi based grey relational analysis for multi response optimisation of diesel engine performance and emission parameters,” Int. J. Heavy Veh. Syst., vol. 27, pp. 441–460, 2020. https://doi.org/10.1504/IJHVS.2020.109287.Search in Google Scholar

[46] S. S. Mishra, T. Mohapatra, S. S. Sahoo, and P. Mishra, “Overall performance investigation and optimization of a multi-fuel operated compression ignition engine using coupled taguchi and grey relational analysis,” ACS Omega, vol. 7, pp. 33216–33232, 2022. https://doi.org/10.1021/ACSOMEGA.2C03566/ASSET/IMAGES/MEDIUM/AO2C03566_M016.GIF.Search in Google Scholar

[47] S. Vellaiyan and K. S. Amirthagadeswaran, “Taguchi-Grey relational-based multi-response optimization of the water-in-diesel emulsification process,” J. Mech. Sci. Technol., vol. 30, pp. 1399–1404, 2016. https://doi.org/10.1007/S12206-016-0247-X/METRICS.Search in Google Scholar

[48] M. V. A. R. Bahubalendruni, et al., “Optimization of performance and emission parameters of biodiesel with additives using taguchi and grey relational analysis,” Eng. Proc., vol. 66, p. 15, 2024. https://doi.org/10.3390/ENGPROC2024066015.Search in Google Scholar

[49] M. K. Balki, M. Temur, S. Erdoğan, M. Sarıkaya, and C. Sayin, “The determination of the best operating parameters for a small SI engine fueled with methanol gasoline blends,” Sustainable Mater. Technol., vol. 30, 2021, Art. no. e00340. https://doi.org/10.1016/J.SUSMAT.2021.E00340.Search in Google Scholar

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/jnet-2024-0095).

Received: 2024-10-15

Accepted: 2025-03-03

Published Online: 2025-03-13

Published in Print: 2025-07-28

You are currently not able to access this content.

Supplementary Material Details

Articles in the same Issue

https://doi.org/10.1515/jnet-2024-0095

Keywords for this article

ethanol; VCR engine; taguchi; signal noise ratio; grey relational analysis; ANN