SpringerLink
Log in

Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

As researchers sought for new methods to decrease noxious emissions and improve engine performance, they discovered biodiesel as a promising biofuel. However, traditional study methodologies were deemed inadequate, prompting the need for computational methods to offer numerical solutions. This approach was seen as a creative and practical solution to the problem at hand. In response to the limitations of conventional modeling approaches, researchers turned towards the innovative solution of using machine-learning techniques as data processing systems. This creative approach has proven effective in addressing a broad variety of technical and scientific concerns, particularly in fields where traditional modeling approaches have fallen short of expectations. This review discusses using machine learning algorithms for predicting biodiesel performance and emissions with nanoparticles. Researchers have solved these problems with the application of machine learning to anticipate engine efficiency and emissions. The machine-learning algorithm predicts engine performance very precisely, proving its efficacy. Nanotechnology and biodiesel engine technologies are quickly advancing, making this review vital. Previous studies have examined nanoparticles' influence on engine performance and emissions. This review uniquely focuses on the application of machine learning techniques. Through the utilization of machine-learning algorithms, it is possible for gaining deeper understanding of intricate connections existing between the properties of nanoparticles and the behavior of engines. This methodology provides extensive comprehension of an impact of nanoparticles upon performance and emissions of biodiesel engines, hence enabling a development of more effectual and sustainable engine designs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

My manuscript has no associate data.

References

  1. Liu P, Zhang Y (2023) Optimization of biodiesel production from oil using a novel green catalyst via development of a predictive model. Arab J Chem 16:1. https://doi.org/10.1016/j.arabjc.2023.104785

    Article  Google Scholar 

  2. Ağbulut Ü, Gürel AE, Sarıdemir S (2021) Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning algorithms. Energy. https://doi.org/10.1016/j.energy.2020.119076

    Article  Google Scholar 

  3. Alex Y, Earnest J, Raghavan A, Roy RG, Koshy CP (2022) Study of engine performance and emission characteristics of diesel engine using cerium oxide nanoparticles blended orange peel oil methyl ester. Energy Nexus 8:100150. https://doi.org/10.1016/j.nexus.2022.100150

    Article  Google Scholar 

  4. Sankar P, Thangavelu M, Moorthy V et al (2022) Prediction and optimization of diesel engine characteristics for various fuel injection timing: operated by third generation green fuel with alumina nano additive. Sustain Energy Technol Assess 53:102751. https://doi.org/10.1016/j.seta.2022.102751

    Article  Google Scholar 

  5. Khan O, Khan MZ, Ahmad N et al (2020) Performance and emission analysis on palm oil derived biodiesel coupled with aluminum oxide nanoparticles. Mater Today 46:6781–6786

    Google Scholar 

  6. Hoang AT, Tabatabaei M, Aghbashlo M et al (2021) Rice bran oil-based biodiesel as a promising renewable fuel alternative to petrodiesel: a review. Renew Sustain Energy Rev 135:110204

    Article  Google Scholar 

  7. Sakthivadivel D, Ganesh Kumar P, Prabakaran R et al (2022) A neem oil-based biodiesel with DEE enriched ethanol and Al2O3 nano additive: an experimental investigation on the diesel engine performance. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2022.102021

    Article  Google Scholar 

  8. Sivamani S, Udayakumar M, Sellappan N (2023) Prediction of single cylinder direct injection diesel engine performance fuelled with lemon peel oil biodiesel using artificial neural network. Mater Today 80:456–463. https://doi.org/10.1016/j.matpr.2022.10.197

    Article  Google Scholar 

  9. Vinoth Arul Raj J, Praveen Kumar R, Vijayakumar B et al (2021) Modelling and process optimization for biodiesel production from nannochloropsis salina using artificial neural network. Bioresour Technol. https://doi.org/10.1016/j.biortech.2021.124872

    Article  Google Scholar 

  10. Sarkar RD, Singh HB, Kalita MC (2021) Enhanced lipid accumulation in microalgae through nanoparticle-mediated approach, for biodiesel production: a mini-review. Heliyon. https://doi.org/10.1016/j.heliyon.2021.e08057

    Article  Google Scholar 

  11. Chen R, Zhao B, He T et al (2024) Study on coupling transient mixed lubrication and time-varying wear of main bearing in actual operation of low-speed diesel engine. Tribol Int 191:109159. https://doi.org/10.1016/j.triboint.2023.109159

    Article  Google Scholar 

  12. Prajapati DK, Katiyar JK, Prakash C (2023) Machine learning approach for the prediction of mixed lubrication parameters for different surface topographies of non-conformal rough contacts. Ind Lubr Tribol 75:1022–1030. https://doi.org/10.1108/ILT-04-2023-0121

    Article  Google Scholar 

  13. Kaushik Y, Verma V, Saxena KK et al (2022) Effect of Al2O3 nanoparticles on performance and emission characteristics of diesel engine fuelled with diesel-neem biodiesel blends. Sustainability 14:7913

    Article  Google Scholar 

  14. Subramaniam S, Raju N, Ganesan A et al (2022) Artificial intelligence technologies for forecasting air pollution and human health: a narrative review. Sustainability 14:9951

    Article  Google Scholar 

  15. Zhang X, Li H, Sekar M et al (2023) Machine learning algorithms for a diesel engine fuelled with biodiesel blends and hydrogen using LSTM networks. Fuel. https://doi.org/10.1016/j.fuel.2022.126292

    Article  Google Scholar 

  16. Waghmare SN, Shelare SD, Tembhurkar CK, Jawalekar SB (2020) Pyrolysis system for environment-friendly conversion of plastic waste into fuel. In: Singh S, Prakash C, Ramakrishna S, Krolczyk G (eds) Lecture notes in mechanical engineering. Springer, Singapore, pp 131–138

    Google Scholar 

  17. Ahmed SF, Rafa N, Mofijur M et al (2021) Biohydrogen production from biomass sources: metabolic pathways and economic analysis. Front Energy Res 9:753878. https://doi.org/10.3389/FENRG.2021.753878/BIBTEX

    Article  Google Scholar 

  18. U.S. Energy Information Administration (2016) International Energy Outlook 2016

  19. U.S. Energy Information Administration (2021) International Energy Outlook 2021 Narrative. Washington

  20. Huang Z, Lyu Z, Luo P et al (2023) Effects of methanol–ammonia blending ratio on performance and emission characteristics of a compression ignition engine. J Mar Sci Eng 11:2388. https://doi.org/10.3390/jmse11122388

    Article  Google Scholar 

  21. Nair A, Ramkumar P, Mahadevan S et al (2022) Machine learning for prediction of heat pipe effectiveness. Energies 15:3276. https://doi.org/10.3390/en15093276

    Article  Google Scholar 

  22. Sharma P (2020) 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 A. https://doi.org/10.1080/15567036.2020.1829204

    Article  Google Scholar 

  23. Kamal Abdelbasset W, Alrawaili SM, Elsayed SH et al (2022) Optimization of heterogeneous catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods. Arab J Chem. https://doi.org/10.1016/j.arabjc.2022.103915

    Article  Google Scholar 

  24. Jathar LD, Nikam K, Awasarmol UV et al (2024) A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning. Heliyon 10:e25407. https://doi.org/10.1016/j.heliyon.2024.e25407

    Article  Google Scholar 

  25. Tominov R, Vakulov Z, Kazantsev V, et al (2023) Multilevel resistive switching in thin oxide films for neuromorphic systems of artificial intillegence: simulation & experimental investigation. In: 2023 7th scientific school dynamics of complex networks and their applications (DCNA). pp 273–276

  26. Dhahad HA, Hasan AM, Chaichan MT, Kazem HA (2022) Prognostic of diesel engine emissions and performance based on an intelligent technique for nanoparticle additives. Energy. https://doi.org/10.1016/j.energy.2021.121855

    Article  Google Scholar 

  27. Babbar A, Prakash C, Singh S et al (2020) Application of hybrid nature-inspired algorithm: single and bi-objective constrained optimization of magnetic abrasive finishing process parameters. J Mater Res Technol 9:7961–7974. https://doi.org/10.1016/j.jmrt.2020.05.003

    Article  Google Scholar 

  28. Fangfang F, Alagumalai A, Mahian O (2021) Sustainable biodiesel production from waste cooking oil: ANN modeling and environmental factor assessment. Sustain Energy Technol Assess. https://doi.org/10.1016/j.seta.2021.101265

    Article  Google Scholar 

  29. Cui Y, Su W, **ng Y et al (2023) Experimental and simulation evaluation of CO2/CO separation under different component ratios in blast furnace gas on zeolites. Chem Eng J 472:144579. https://doi.org/10.1016/j.cej.2023.144579

    Article  Google Scholar 

  30. Jathar LD, Ganesan S, Awasarmol U et al (2023) Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle. Environ Pollut 326:121474. https://doi.org/10.1016/j.envpol.2023.121474

    Article  Google Scholar 

  31. Ansari MO, Chattopadhyaya S, Ghose J et al (2022) Productivity enhancement by prediction of liquid steel breakout during continuous casting process in manufacturing of steel slabs in steel plant using artificial neural network with backpropagation algorithms. Materials (Basel) 15:670

    Article  Google Scholar 

  32. Aghbashlo M, Shamshirband S, Tabatabaei M et al (2016) The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste. Energy 94:443–456. https://doi.org/10.1016/j.energy.2015.11.008

    Article  Google Scholar 

  33. Singh NK, Singh Y, Sharma A, Rahim EA (2020) Prediction of performance and emission parameters of Kusum biodiesel based diesel engine using neuro-fuzzy techniques combined with genetic algorithm. Fuel. https://doi.org/10.1016/j.fuel.2020.118629

    Article  Google Scholar 

  34. Oraegbunam JC, Ishola NB, Sotunde BA et al (2023) Sandbox oil biodiesel production modeling and optimization with neural networks and genetic algorithm. Green Technol Sustain 1:100007. https://doi.org/10.1016/j.grets.2022.100007

    Article  Google Scholar 

  35. Verma V, Sharma SK (2022) Critical analysis of existing punjabi grammar checker and a proposed hybrid framework involving machine learning and rule-base criteria. ACM Trans Asian Low-Resource Lang Inf Process 21:1–27. https://doi.org/10.1145/3514237

    Article  Google Scholar 

  36. Chandrakant Nikam K, Jathar L, Shelare SD et al (2023) Parametric analysis and optimization of 660 MW supercritical power plant. Energy 280:128165. https://doi.org/10.1016/j.energy.2023.128165

    Article  Google Scholar 

  37. Banerjee P, Laha R, Dikshit MK et al (2022) A study on the performance of various predictive models based on artificial neural network for backward metal flow forming process. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-022-01079-6

    Article  Google Scholar 

  38. Yao F, Qin Z, Wang X et al (2023) The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance. Heliyon 9:e16160. https://doi.org/10.1016/j.heliyon.2023.e16160

    Article  Google Scholar 

  39. Mishra SK, Dahiya S, Gangil B et al (2022) Mechanical, morphological, and tribological characterization of novel walnut shell-reinforced polylactic acid-based biocomposites and prediction based on artificial neural network. Biomass Convers Biorefinery. https://doi.org/10.1007/s13399-022-03670-z

    Article  Google Scholar 

  40. Suhaib Kamran S, Haleem A, Bahl S et al (2022) Artificial intelligence and advanced materials in automotive industry: potential applications and perspectives. Mater Today Proc 62:4207–4214. https://doi.org/10.1016/j.matpr.2022.04.727

    Article  Google Scholar 

  41. Ajala OO, Oke EO, Odejobi OJ et al (2023) Artificial neuro-fuzzy intelligent prediction of techno-economic parameters of computer-aided scale-up for palm kernel oil based biodiesel production. Clean Chem Eng 5:100098. https://doi.org/10.1016/j.clce.2023.100098

    Article  Google Scholar 

  42. Taheri-Garavand A, Heidari-Maleni A, Mesri-Gundoshmian T, Samuel OD (2022) Application of artificial neural networks for the prediction of performance and exhaust emissions in IC engine using biodiesel-diesel blends containing quantum dot based on carbon doped. Energy Convers Manag X. https://doi.org/10.1016/j.ecmx.2022.100304

    Article  Google Scholar 

  43. Kumar R, Channi AS, Kaur R et al (2023) Exploring the intricacies of machine learning-based optimization of electric discharge machining on squeeze cast TiB2/AA6061 composites: Insights from morphological, and microstructural aspects in the surface structure analysis of recast layer formation a. J Mater Res Technol 26:8569–8603. https://doi.org/10.1016/j.jmrt.2023.09.127

    Article  Google Scholar 

  44. Hosamani BR, Abbas Ali S, Katti V (2021) Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: artificial neural network approach. Alexandria Eng J 60:837–844. https://doi.org/10.1016/j.aej.2020.10.012

    Article  Google Scholar 

  45. Wong KI, Wong PK, Cheung CS, Vong CM (2013) Modeling and optimization of biodiesel engine performance using advanced machine learning methods. Energy 55:519–528

    Article  Google Scholar 

  46. Abdelbasset WK, Elkholi SM, Jade Catalan Opulencia M et al (2022) Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil: development of multiple machine-learning computational techniques for optimization. Arab J Chem. https://doi.org/10.1016/j.arabjc.2022.103843

    Article  Google Scholar 

  47. Zhou L, Song Y, Ji W, Wei H (2022) Machine learning for combustion. Energy AI. https://doi.org/10.1016/j.egyai.2021.100128

    Article  Google Scholar 

  48. Sharma P, Sharma AK (2021) AI-based prognostic modeling and performance optimization of CI engine using biodiesel-diesel blendsin

  49. Ghanbari M, Mozafari-Vanani L, Dehghani-Soufi M, Jahanbakhshi A (2021) Effect of alumina nanoparticles as additive with diesel–biodiesel blends on performance and emission characteristic of a six-cylinder diesel engine using response surface methodology (RSM). Energy Convers Manag X. https://doi.org/10.1016/j.ecmx.2021.100091

    Article  Google Scholar 

  50. Zandie M, Ng HK, Gan S et al (2023) Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends. Energy. https://doi.org/10.1016/j.energy.2022.125425

    Article  Google Scholar 

  51. Bukkarapu KR, Krishnasamy A (2022) Predicting engine fuel properties of biodiesel and biodiesel-diesel blends using spectroscopy based approach. Fuel Process Technol 230:107227. https://doi.org/10.1016/j.fuproc.2022.107227

    Article  Google Scholar 

  52. Sakthivel G, Sivaraja CM, Ikua BW (2019) Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA. Energy 166:287–306. https://doi.org/10.1016/j.energy.2018.10.023

    Article  Google Scholar 

  53. Samuel OD, Okwu MO, Oyejide OJ et al (2020) Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer. Fuel. https://doi.org/10.1016/j.fuel.2020.118701

    Article  Google Scholar 

  54. Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energy 74:640–647. https://doi.org/10.1016/j.renene.2014.08.075

    Article  Google Scholar 

  55. Elkelawy M, El SEA, Bastawissi HA, Shams MM (2022) A comprehensive review on the effects of diesel / biofuel blends with nanofluid additives on compression ignition engine by response surface methodology. Energy Convers Manag X 14:100177. https://doi.org/10.1016/j.ecmx.2021.100177

    Article  Google Scholar 

  56. Khan SH (2020) Green nanotechnology for the environment and sustainable development. In: Lichtfouse E (ed) Green materials for wastewater treatment. Springer, Cham, pp 13–46

    Chapter  Google Scholar 

  57. Hu J, Zou Y, Zhao Y (2024) Robust operation of hydrogen-fueled power-to-gas system within feasible operating zone considering carbon-dioxide recycling process. Int J Hydrogen Energy 58:1429–1442. https://doi.org/10.1016/j.ijhydene.2024.01.337

    Article  Google Scholar 

  58. Liu Y, Sayed BT, Sivaraman R et al (2023) Novel and robust machine learning model to optimize biodiesel production from algal oil using CaO and CaO/Al2O3 as catalyst: sustainable green energy. Environ Technol Innov. https://doi.org/10.1016/j.eti.2023.103018

    Article  Google Scholar 

  59. Brahma S, Nath B, Basumatary B et al (2022) Biodiesel production from mixed oils: a sustainable approach towards industrial biofuel production. Chem Eng J Adv 10:100284. https://doi.org/10.1016/j.ceja.2022.100284

    Article  Google Scholar 

  60. Fei M, Zhang Z, Zhao W et al (2024) Optimal power distribution control in modular power architecture using hydraulic free piston engines. Appl Energy 358:122540. https://doi.org/10.1016/j.apenergy.2023.122540

    Article  Google Scholar 

  61. Sharma P (2021) Artificial intelligence-based model prediction of biodiesel-fueled engine performance and emission characteristics: a comparative evaluation of gene expression programming and artificial neural network. Heat Transf 50:5563–5587. https://doi.org/10.1002/htj.22138

    Article  Google Scholar 

  62. Simsek S, Uslu S, Simsek H (2022) Response surface methodology-based parameter optimization of single-cylinder diesel engine fueled with graphene oxide dosed sesame oil/diesel fuel blend. Energy AI. https://doi.org/10.1016/j.egyai.2022.100200

    Article  Google Scholar 

  63. Soudagar MEM, Shelare S, Marghade D et al (2024) Optimizing IC engine efficiency: a comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning. Energy Convers Manag 307:118337. https://doi.org/10.1016/j.enconman.2024.118337

    Article  Google Scholar 

  64. Jana DK, Bhattacharjee S, Roy S et al (2022) The optimization of biodiesel production from waste cooking oil catalyzed by ostrich-eggshell derived CaO through various machine learning approaches. Clean Energy Syst. https://doi.org/10.1016/j.cles.2022.100033

    Article  Google Scholar 

  65. Hosseini SH, Taghizadeh-Alisaraei A, Ghobadian B, Abbaszadeh-Mayvan A (2020) Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends. Renew Energy 149:951–961. https://doi.org/10.1016/j.renene.2019.10.080

    Article  Google Scholar 

  66. Ngige GA, Ovuoraye PE, Igwegbe CA et al (2023) RSM optimization and yield prediction for biodiesel produced from alkali-catalytic transesterification of pawpaw seed extract: thermodynamics, kinetics, and multiple linear regression analysis. Digit Chem Eng. https://doi.org/10.1016/j.dche.2022.100066

    Article  Google Scholar 

  67. Liu L, Mei Q, Jia W (2022) A flexible diesel spray model for advanced injection strategy. Fuel 314:122784. https://doi.org/10.1016/j.fuel.2021.122784

    Article  Google Scholar 

  68. Li P, Luo S, Zhang L et al (2024) Progress, challenges, and prospects of spent lithium-ion batteries recycling: a review. J Energy Chem 89:144–171. https://doi.org/10.1016/j.jechem.2023.10.012

    Article  Google Scholar 

  69. Liu L, Wu Y, Wang Y et al (2022) Exploration of environmentally friendly marine power technology-ammonia/diesel stratified injection. J Clean Prod 380:135014. https://doi.org/10.1016/j.jclepro.2022.135014

    Article  Google Scholar 

  70. Bai X, Xu M, Li Q, Yu L (2022) Trajectory-battery integrated design and its application to orbital maneuvers with electric pump-fed engines. Adv Space Res 70:825–841. https://doi.org/10.1016/j.asr.2022.05.014

    Article  Google Scholar 

  71. Gupta KK, Kalita K, Ghadai RK et al (2021) Machine learning-based predictive modelling of biodiesel production-a comparative perspective. Energies. https://doi.org/10.3390/en14041122

    Article  Google Scholar 

  72. Sumayli A, Alshahrani SM (2023) Modeling and prediction of biodiesel production by using different artificial intelligence methods: Multi-layer perceptron (MLP), Gradient boosting (GB), and Gaussian process regression (GPR). Arab J Chem. https://doi.org/10.1016/j.arabjc.2023.104801

    Article  Google Scholar 

  73. Sharma V, Kalam Hossain A, Ahmed A, Rezk A (2022) Study on using graphene and graphite nanoparticles as fuel additives in waste cooking oil biodiesel. Fuel 328:125270. https://doi.org/10.1016/j.fuel.2022.125270

    Article  Google Scholar 

  74. Aghbashlo M, Peng W, Tabatabaei M et al (2021) Machine learning technology in biodiesel research: a review. Prog Energy Combust Sci 85:100904. https://doi.org/10.1016/j.pecs.2021.100904

    Article  Google Scholar 

  75. Yugandharsai R, Jayaraman J, Reddy S (2021) Effects of injection pressure on performance & emission characteristics of CI engine using graphene oxide additive in bio-diesel blend. Mater Today Proc 44:3716–3722. https://doi.org/10.1016/j.matpr.2020.11.253

    Article  Google Scholar 

  76. Ozor PA, Aigbodion VS, Sukdeo NI (2023) Modified calcium oxide nanoparticles derived from oyster shells for biodiesel production from waste cooking oil. Fuel Commun 14:100085. https://doi.org/10.1016/j.jfueco.2023.100085

    Article  Google Scholar 

  77. Riyadi TWB, Spraggon M, Herawan SG et al (2023) Biodiesel for HCCI engine: prospects and challenges of sustainability biodiesel for energy transition. Results Eng 17:100916. https://doi.org/10.1016/j.rineng.2023.100916

    Article  Google Scholar 

  78. Loo DL, Teoh YH, How HG et al (2023) Effect of nanoparticles additives on tribological behaviour of advanced biofuels. Fuel. https://doi.org/10.1016/j.fuel.2022.126798

    Article  Google Scholar 

  79. Torregrosa AJ, Broatch A, Olmeda P, Dreif A (2021) Assessment of the improvement of internal combustion engines cooling system using nanofluids and nanoencapsulated phase change materials. Int J Engine Res 22:1939–1957. https://doi.org/10.1177/1468087420917494

    Article  Google Scholar 

  80. Ampah JD, Yusuf AA, Agyekum EB et al (2022) Progress and recent trends in the application of nanoparticles as low carbon fuel additives—a state of the art review. Nanomaterials 12:1515. https://doi.org/10.3390/nano12091515

    Article  Google Scholar 

  81. El-Masry JF, Bou-Hamdan KF, Abbas AH, Martyushev DA (2023) A comprehensive review on utilizing nanomaterials in enhanced oil recovery applications. Energies 16:691. https://doi.org/10.3390/en16020691

    Article  Google Scholar 

  82. Singh N, Kaushal R (2020) Outcomes of advanced biodiesel with nanoparticle additives on performance of CI engines. Mater Today 44:4612–4620

    Google Scholar 

  83. Jit Sarma C, Sharma P, Bora BJ et al (2023) Improving the combustion and emission performance of a diesel engine powered with mahua biodiesel and TiO2 nanoparticles additive. Alexandria Eng J 72:387–398. https://doi.org/10.1016/j.aej.2023.03.070

    Article  Google Scholar 

  84. Shaisundaram VS, Chandrasekaran M, Muraliraja R et al (2020) Investigation of tamarind seed oil biodiesel with aluminium oxide nanoparticle in CI engine. Mater Today 37:1417–1421

    Google Scholar 

  85. Bitire SO, Jen TC (2023) An optimization study on a biosynthesized nano-particle and its effect on the performance-emission characteristics of a diesel engine fueled with parsley biodiesel blend. Energy Rep 9:2185–2200. https://doi.org/10.1016/j.egyr.2023.01.041

    Article  Google Scholar 

  86. Bitire SO, Jen TC (2023) Modulation of the best conditions for improved engine performance and reduced exhaust emissions using an eco-friendly nano additive in parsley biodiesel blend. Environ Technol Innov. https://doi.org/10.1016/j.eti.2023.103062

    Article  Google Scholar 

  87. Sateesh KA, Yaliwal VS, Banapurmath NR et al (2023) Effect of MWCNTs nano-additive on a dual-fuel engine characteristics utilizing dairy scum oil methyl ester and producer gas. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2022.102661

    Article  Google Scholar 

  88. Firew D, Nallamothu RB, Alemayehu G, Gopal R (2022) Performance and emission evaluation of CI engine fueled with ethanol diesel emulsion using NiZnFe2O4 nanoparticle additive. Heliyon. https://doi.org/10.1016/j.heliyon.2022.e11639

    Article  Google Scholar 

  89. Al-Kheraif AA, Syed A, Elgorban AM et al (2021) Experimental assessment of performance, combustion and emission characteristics of diesel engine fuelled by combined non-edible blends with nanoparticles. Fuel 295:120590. https://doi.org/10.1016/j.fuel.2021.120590

    Article  Google Scholar 

  90. Gad MS, Ağbulut Ü, Afzal A et al (2023) A comprehensive review on the usage of the nano-sized particles along with diesel/biofuel blends and their impacts on engine behaviors. Fuel 339:127364. https://doi.org/10.1016/j.fuel.2022.127364

    Article  Google Scholar 

  91. Bitire SO, Jen TC (2022) The role of a novel green synthesized nanoparticles added parsley biodiesel blend on the performance-emission characteristics of a diesel engine. S Afr J Chem Eng 41:161–175. https://doi.org/10.1016/j.sajce.2022.06.007

    Article  Google Scholar 

  92. Belkhode PN, Ganvir VN, Shende AC, Shelare SD (2022) Utilization of waste transformer oil as a fuel in diesel engine. Mater Today 49:262–268. https://doi.org/10.1016/j.matpr.2021.02.008

    Article  Google Scholar 

  93. Mujtaba MA, Muk Cho H, Masjuki HH et al (2021) Effect of alcoholic and nano-particles additives on tribological properties of diesel–palm–sesame–biodiesel blends. Energy Rep 7:1162–1171. https://doi.org/10.1016/j.egyr.2020.12.009

    Article  Google Scholar 

  94. Kuniyil M, Shanmukha Kumar JV, Adil SF et al (2021) Production of biodiesel from waste cooking oil using ZnCuO/N-doped graphene nanocomposite as an efficient heterogeneous catalyst. Arab J Chem. https://doi.org/10.1016/j.arabjc.2020.102982

    Article  Google Scholar 

  95. Sultana N, Hossain SMZ, Abusaad M et al (2022) Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches. Fuel. https://doi.org/10.1016/j.fuel.2021.122184

    Article  Google Scholar 

  96. Alahmer A, Rezk H, Aladayleh W et al (2022) Modeling and optimization of a compression ignition engine fueled with biodiesel blends for performance improvement. Mathematics. https://doi.org/10.3390/math10030420

    Article  Google Scholar 

  97. Liang CM, Wang CC, Huang KJ, Yang CF (2023) Production of biohydrogen and green platform compound 2, 5-furandicarboxylic acid using rice straw hydrolysate. Biochem Eng J 197:108993. https://doi.org/10.1016/j.bej.2023.108993

    Article  Google Scholar 

  98. Jiang D, Yue T, Zhang Z et al (2021) A strategy for successive feedstock reuse to maximize photo-fermentative hydrogen production of Arundo donax L. Bioresour Technol 329:124878. https://doi.org/10.1016/j.biortech.2021.124878

    Article  Google Scholar 

  99. Li Z, Deng L, Lu J et al (2010) Enzymatic synthesis of fatty acid methyl esters from crude rice bran oil with immobilized Candida sp. 99–125. Chin J Chem Eng 18:870–875. https://doi.org/10.1016/S1004-9541(09)60141-5

    Article  Google Scholar 

  100. Atabani AE, Silitonga AS, Badruddin IA et al (2012) A comprehensive review on biodiesel as an alternative energy resource and its characteristics. Renew Sustain Energy Rev 16:2070–2093. https://doi.org/10.1016/j.rser.2012.01.003

    Article  Google Scholar 

  101. Liu H, Huang Y, Yuan H et al (2018) Life cycle assessment of biofuels in China: status and challenges. Renew Sustain Energy Rev 97:301–322. https://doi.org/10.1016/j.rser.2018.08.052

    Article  Google Scholar 

  102. Marghade DT, Bhange VP, Gabhane JW (2022) Aquatic weeds as bioenergy feedstock. In: Guldhe A, Singh B (eds) Novel feedstocks for biofuels production. Springer, Singapore, pp 191–217

    Chapter  Google Scholar 

  103. Almohana AI, Almojil SF, Kamal MA et al (2022) Theoretical investigation on optimization of biodiesel production using waste cooking oil: machine learning modeling and experimental validation. Energy Rep 8:11938–11951. https://doi.org/10.1016/j.egyr.2022.08.265

    Article  Google Scholar 

  104. Pourhoseini SH, Ghodrat M (2021) Experimental investigation of the effect of Al2O3 nanoparticles as additives to B20 blended biodiesel fuel: flame characteristics, thermal performance and pollutant emissions. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2021.101292

    Article  Google Scholar 

  105. Yusuf AA, Inambao FL, Ampah JD (2022) The effect of biodiesel and CeO2 nanoparticle blends on CRDI diesel engine: a special focus on combustion, particle number, PM2.5 species, organic compound and gaseous emissions. J King Saud Univ Eng Sci. https://doi.org/10.1016/j.jksues.2021.12.003

    Article  Google Scholar 

  106. Yusri IM, Abdul Majeed APP, Mamat R et al (2018) A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel. Renew Sustain Energy Rev 90:665–686

    Article  Google Scholar 

  107. Pawar C, Balekumeri S, Motgi N, et al (2023) A comprehensive look at the aluminum oxide nano-additive effects on engine performance and emissions. AIP Conf Proc

  108. Sanjeevannavar MB, Banapurmath NR, Kumar VD et al (2023) Machine learning prediction and optimization of performance and emissions characteristics of IC engine. Sustainability. https://doi.org/10.3390/su151813825

    Article  Google Scholar 

  109. Awogbemi O, Von Kallon DV (2023) Application of machine learning technologies in biodiesel production process—a review. Front Energy Res. https://doi.org/10.3389/fenrg.2023.1122638

    Article  Google Scholar 

  110. Shelare SD, Belkhode PN, Nikam KC et al (2023) 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 282:128874. https://doi.org/10.1016/j.energy.2023.128874

    Article  Google Scholar 

  111. Gupta A, Kumar R, Maurya A et al (2023) A comparative study of the impact on combustion and emission characteristics of nanoparticle-based fuel additives in the internal combustion engine. Energy Sci Eng. https://doi.org/10.1002/ese3.1614

    Article  Google Scholar 

  112. Kumar R, Lalnundiki V, Shelare SD et al (2024) An investigation of the environmental implications of bioplastics: recent advancements on the development of environmentally friendly bioplastics solutions. Environ Res 244:117707. https://doi.org/10.1016/j.envres.2023.117707

    Article  Google Scholar 

  113. Belkhode PN, Ganvir VN, Shelare SD et al (2022) Experimental investigation on treated transformer oil (TTO) and its diesel blends in the diesel engine. Energy Harvest Syst 9:75–81. https://doi.org/10.1515/ehs-2021-0032

    Article  Google Scholar 

  114. Esonye C, Onukwuli OD, Ofoefule AU, Ogah EO (2019) Multi-input multi-output (MIMO) ANN and Nelder-Mead’s simplex based modeling of engine performance and combustion emission characteristics of biodiesel-diesel blend in CI diesel engine. Appl Therm Eng 151:100–114. https://doi.org/10.1016/j.applthermaleng.2019.01.101

    Article  Google Scholar 

  115. Soudagar MEM, Kiong TS, Jathar L et al (2024) Perspectives on cultivation and harvesting technologies of microalgae, towards environmental sustainability and life cycle analysis. Chemosphere 353:141540. https://doi.org/10.1016/j.chemosphere.2024.141540

    Article  Google Scholar 

  116. **ng Y, Zheng Z, Sun Y, Alikhani MA (2021) A review on machine learning application in biodiesel production studies. Int J Chem Eng 1:1–12. https://doi.org/10.1155/2021/2154258

    Article  Google Scholar 

  117. Kumar S, Jain S, Kumar H (2021) Application of adaptive neuro-fuzzy inference system and response surface methodology in biodiesel synthesis from jatropha–algae oil and its performance and emission analysis on diesel engine coupled with generator. Energy. https://doi.org/10.1016/j.energy.2021.120428

    Article  Google Scholar 

  118. Moubayed A, Ahmed T, Haque A, Shami A (2020) Machine learning towards enabling spectrum-as-a-service dynamic sharing. In 2020 IEEE Canadian conference on electrical and computer engineering (CCECE), IEEE. pp 1–6

  119. Ghazal TM, Hasan MK, Abdullah SN, Bakar KA, Al-Dmour NA, Said RA, Abdellatif TM, Moubayed A, Alzoubi HM, Alshurideh M, Alomoush W (2023) Machine learning-based intrusion detection approaches for secured internet of things. In: Alshurideh M (ed) The effect of information technology on business and marketing intelligence systems. Springer, Cham, pp 2013–2036

    Chapter  Google Scholar 

  120. Farhat P, Arisdakessian S, Wahab OA, Mourad A, Ould-Slimane H (2022) Machine learning based container placement in on-demand clustered fogs. In 2022 international wireless communications and mobile computing (IWCMC). IEEE, pp 1250–1255

  121. Daoura O, El Hassan N, Boutros M, Casale S, Massiani P, Launay F (2022) Effect of impregnation with ammonia vs silica support textural properties on Ni nanoparticle catalysts for dry reforming of methane. ACS Appl Nano Mater 5(12):18048–18059

    Article  Google Scholar 

  122. Daoura O, Boutros M, Kaydouh M-N, Massiani P, Launay F, El Hassan N (2021) Supported nickel nanocatalysts for the dry reforming of methane: effect of SBA-15’s pore sizes on the catalytic performances of nickel nanoparticles. In: Piumetti M, Bensaid S (eds) Nanostructured catalysts for environmental applications. Springer, Cham, pp 113–126

    Chapter  Google Scholar 

  123. El Hassan N, Jabbour K, Fakeeha AH, Nasr Y, Naeem MA, Alreshaidan SB, Al-Fatesh AS (2023) Production of carbon nanomaterials and syngas from biogas reforming and decomposition on one-pot mesoporous nickel alumina catalysts. Alexandria Eng J 63:143–155

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/411/45.

Funding

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/411/45.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Srinivas University College of Engineering Technology, Mangaluru, 574146, India

    Chetan Pawar, B. Shreeprakash & Beekanahalli Mokshanatha

  2. Department of Mechanical Engineering, Dr D Y Patil Institute of Engineering, Management and Research, Akrudi, Pune, 411044, India

    Chetan Pawar, Keval Chandrakant Nikam & Nitin Motgi

  3. Department of Mechanical Engineering, Army Institute of Technology, Pune, 411015, India

    Laxmikant D. Jathar

  4. Department of Mechanical Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, 440019, India

    Sagar D. Shelare

  5. Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India

    Sagar D. Shelare & Shubham Sharma

  6. Department of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, Lebanon

    Shubham Sharma

  7. Department of Mechanical Engineering, Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, 201306, India

    Shashi Prakash Dwivedi

  8. Department of Mechanical Engineering, Faculty of Engineering and Technology, Jain (Deemed-to-Be) University, Bengaluru, Karnataka, 560069, India

    Pardeep Singh Bains

  9. Department of Mechanical Engineering, Vivekananda Global University, Jaipur, Rajasthan, 303012, India

    Pardeep Singh Bains

  10. Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia, Boris Yeltsin, 19 Mira Street, 620002, Ekaterinburg, Russia

    Abhinav Kumar

  11. Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia

    Mohamed Abbas

Authors
  1. Chetan Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Shreeprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beekanahalli Mokshanatha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keval Chandrakant Nikam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nitin Motgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laxmikant D. Jathar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sagar D. Shelare
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shubham Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shashi Prakash Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pardeep Singh Bains
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Abhinav Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mohamed Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, CP, SB, BM, KCN, NM, LDJ, Sagar Shelare (SS), Shubham Sharma (SS); methodology, CP, SB, BM, KCN, NM, LDJ, Sagar Shelare (SS), Shubham Sharma (SS); formal analysis, CP, SB, BM, KCN, NM, LDJ, Sagar Shelare (SS), Shubham Sharma (SS); investigation, CP, SB, BM, KCN, NM, LDJ, Sagar Shelare (SS), Shubham Sharma (SS); writing—original draft preparation, CP, SB, BM, KCN, NM, LDJ, Sagar Shelare (SS), Shubham Sharma (SS); writing—review and editing, Shubham Sharma (SS), SPD, PSB, AK, MA; supervision, Shubham Sharma (SS), SPD, PSB, AK, MA; project administration, Shubham Sharma (SS), SPD, PSB, AK, MA; funding acquisition, Shubham Sharma (SS), SPD, PSB, AK, MA. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Shubham Sharma.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Consent to Publish

All authors have read and approved this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pawar, C., Shreeprakash, B., Mokshanatha, B. et al. Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10144-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11831-024-10144-0

Search

Navigation