Combined application of ANN prediction and RSM optimization of performance and emission parameters of a diesel engine using diesel-biodiesel-propanol fuel blends

Abstract

In this study, an artificial neural network (ANN) was used to estimated the performance and exhaust emission parameters of a diesel engine running on diesel, biodiesel, and propanol fuel mixtures. In addition, the parameters estimated by ANN were tried determining the optimum operating parameter by using Response Surface Methodology (RSM). In the experimental study, propanol was added in 3 different ratios (5%, 10% and 20%) into 100% diesel, 80% diesel and 20% biodiesel fuel blends. In addition, engine tests, were made at 5 different engine speeds with 400 min-1 intervals between 1000 min-1 and 2600 min-1 revolutions at full load. In addition, HC (Hydrocarbon), CO (Carbon Monoxide), NOX (Nitrogen oxides) and Smoke emissions were measured during in the working. ANN model was developed for estimation of engine output parameters depending on fuel mixture ratios and engine speed. In the ANN results, the regression coefficients (R2) of the proposed model were found to be between 0.924 and 0.99. When the obtained ANN results were compared with the experimental results, it was seen that the maximum mean relative error (MRE) was 6.895%. It has been shown that the applied model can predict with a low error rate. The RSM results showed that the optimum operating parameters were 2034-min-1 engine speed, 74.667% diesel, 11.36% biodiesel and 15% propanol fuel mixture. In addition, in the validation tests of the model where the desirability was 0.7833%, the highest error rate was obtained as 7.37% as a result of NOX. As a result of the study, it was seen that RSM supported ANN is a good method for estimating diesel engine parameters working with diesel/biodiesel/propanol mixtures and determining optimum operating parameters.

Keywords

• ANN
• Biodiesel
• Emission
• Engine Performance
• Propanol
• RSM

References

1. 1. Aydın, M., Uslu, S and Çelik, M.B., Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization. Fuel, 2020. 269: p. 117472.
2. 2. Çelik M.B. and Şimşek, D., The determination of optimum injection pressure in an engine fuelled with soybean biodiesel/diesel blend. Thermal Science, 2014. 18(1): p. 229-238.
3. 3. Koçak, M.S., Ileri, E. and Utlu, Z., Experimental study of emission parameters of biodiesel fuels obtained from canola, hazelnut, and waste cooking oils. Energy & Fuels, 2007. 21(6): p. 3622-3626.
4. 4. Ozer, S. and Doğan, B., Thermodynamic analyzes in a compression ignition engine using fuel oil diesel fuel blends. Thermal Science, 2022, 26(4): p.3079-3088.
5. 5. Sahin, F., Halis, S., Yıldırım, E., Altın, M., Balaban, F., Solmaz, H. and Yücesu, H.S., Effects of premixed ratio on engine operation range and emissions of a reactivity controlled compression ignition engine. SAE International Journal of Fuels and Lubricants, 2023, 16(2): p. 169-179.
6. 6. Vural, E. and Serkan, Ö., The investigation of effect of the ceramic coatings with bond-layer coated on piston and valve surface on engine performance of a diesel engine. International Advanced Researches and Engineering Journal, 2020, 4(2): p. 87-93.
7. 7. Akcay, M., Özer, S. and Satılmış, G., Analytical Formulation for Diesel Engine Fueled with Fusel Oil/Diesel Blends. Journal of Scientific & Industrial Research, 2022, 81(7): p.712-719.
8. 8. Ertugrul, I., Ulkir, O. Ozer, S. and Ozel, S., Analysis of thermal barrier coated pistons in the COMSOL and the effects of their use with water+ ethanol doped biodiesel. Thermal Science, 26(4-A): p. 2981-2989.

9. 9 Serkan, Ö., Vural, E. and Binici, M., Taguchi method for investigation of the effect of TBC coatings on NiCr bond-coated diesel engine on exhaust gas emissions. International Advanced Researches and Engineering Journal, 2020, 4(1): p. 14-20.
10. 10. Vural, E., Özer, S., Özel, S. and Binici, M., Analyzing the effects of hexane and water blended diesel fuels on emissions and performance in a ceramic-coated diesel engine by Taguchi optimization method. Fuel, 2023, 344: p. 128105.
11. 11. Bhale, P.V., Deshpande, N.V., and Thombre, S.B., Improving the low temperature properties of biodiesel fuel. Renewable Energy, 2009, 34(3): p. 794-800.
12. 12. Moser, B.R., Influence of blending canola, palm, soybean, and sunflower oil methyl esters on fuel properties of biodiesel. Energy & Fuels, 2008, 22(6): p. 4301-4306.
13. 13. Ileri, E., Karaoglan, A.D. and Atmanli, A., Response surface methodology based prediction of engine performance and exhaust emissions of a diesel engine fuelled with canola oil methyl ester. Journal of Renewable and Sustainable Energy, 2013, 5(3): p. 033132.
14. 14. Ma, Q., Zhang, Q., Liang, J. and Yang, C., The performance and emissions characteristics of diesel/biodiesel/alcohol blends in a diesel engine. Energy Reports, 2021, 7: p. 1016-1024.
15. 15. Özkan, M., Comparative study of the effect of biodiesel and diesel fuel on a compression ignition engine’s performance, emissions, and its cycle by cycle variations. Energy & Fuels, 2007, 21(6): p. 3627-3636.
16. 16. Şimşek, D. and Çolak, N.Y., Biyodizel/Propanol yakıt karışımlarının dizel motor emisyonlarına etkisinin incelenmesi. El-Cezeri Journal of Science and Engineering, 2019, 6(1): p. 166-174.
17. 17. Aydin, H. and Ilkılıc, C., Effect of ethanol blending with biodiesel on engine performance and exhaust emissions in a CI engine. Applied Thermal Engineering, 2010, 30(10): p. 1199-1204.
18. 18. Atmanli, A., Effects of a cetane improver on fuel properties and engine characteristics of a diesel engine fueled with the blends of diesel, hazelnut oil and higher carbon alcohol. Fuel, 2016, 172: p. 209-217.
19. 19. Liang, J., Zhang, Q., Chen, Z. and Zheng, Z., The effects of EGR rates and ternary blends of biodiesel/n-pentanol/diesel on the combustion and emission characteristics of a CRDI diesel engine. Fuel, 2021, 286: p. 119297.
20. 20. Cornils, B., Handbook of Commercial Catalysts. Heterogeneous Catalysts. By Howard F. Rase. 2004, Wiley Online Library.
21. 21. Liu, K.H., Atiyeh, K., Stevenson, B. S., Tanner, R. S., Wilkins, M.R. and Huhnke, R.L., Continuous syngas fermentation for the production of ethanol, n-propanol and n-butanol. Bioresource Technology, 2014, 151: p. 69-77.
22. 22. Kumar, B.R. and Saravanan, S., Use of higher alcohol biofuels in diesel engines: A review. Renewable and Sustainable Energy Reviews, 2016, 60: p. 84-115.
23. 23. Venkata Rao, K. and Murthy, P., Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. Journal of Intelligent Manufacturing, 2018, 29(7): p. 1533-1543.
24. 24. Xu, Z., Kang, Y. and Lv, W., Analysis and prediction of vehicle exhaust emission using ANN. 36th Chinese Control Conference. 2017. IEEE. p. 4029-4033.
25. 25. Abuhabaya, A., Ali, J., Fieldhouse, J., Brown, R. and Andrijanto, E., The optimisation of bio-diesel production from Sunflower oil using RSM and its effect on engine performance and emissions. 36th Chinese Control Conference. 2011. IEEE. p. 310-314.
26. 26. Baranitharan, P. Ramesh, K. and Sakthivel, R., Measurement of performance and emission distinctiveness of Aegle marmelos seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine using ANN and RSM. Measurement, 2019, 144: p. 366-380.
27. 27. Ghanbari, M., Mozafari-Vanani, L., Dehghani-Soufi, M. and Jahanbakhshi, A., 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 Conversion and Management: X, 2021, 11: p. 100091.
28. 28. Rao, K.P., Babu, T.V., Anuradha, G., and Rao, B.V.A., IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egyptian Journal of Petroleum, 2017, 26(3): p. 593-600.
29. 29. Yilmaz, N., Atmanli, A., Hall, M. J. and Vigil, F. M., Determination of the optimum blend ratio of diesel, waste oil derived biodiesel and 1-pentanol using the response surface method. Energies, 2022, 15(14): p. 5144.
30. 30. Caligiuri, C., Bietresato, M., Algieri, A., Baratieri, M. and Renzi, M., Experimental Investigation and RSM Modeling of the Effects of Injection Timing on the Performance and NOx Emissions of a Micro-Cogeneration Unit Fueled with Biodiesel Blends. Energies, 2022, 15(10): p. 3586.
31. 31. Ong, M.Y., Nomanbhay, S., Kusumo, F., Raja Shahruzzaman, R.M.H. and Shamsuddin, A.H., Modeling and optimization of microwave-based bio-jet fuel from coconut oil: Investigation of Response Surface Methodology (RSM) and Artificial Neural Network Methodology (ANN). Energies, 2021, 14(2): p. 295.
32. 32. Simsek, S. and Uslu, S., Determination of a diesel engine operating parameters powered with canola, safflower and waste vegetable oil based biodiesel combination using response surface methodology (RSM). Fuel, 2020, 270: p. 117496.
33. 33. Kumar, B.R., Saravanan, S., Rana, D. and Nagendran, A., Combined effect of injection timing and exhaust gas recirculation (EGR) on performance and emissions of a DI diesel engine fuelled with next-generation advanced biofuel–diesel blends using response surface methodology. Energy Conversion and Management, 2016, 123: p. 470-486.
34. 34. Uysal, A. and Bayir, R., Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network. Journal of Zhejiang University Scıence C, 2013, 14: p. 941-952.
35. 35. Saritas M.M., and Yasar, A., Performance analysis of ANN and Naive Bayes classification algorithm for data classification. International Journal Of Intelligent Systems and Applications in Engineering, 2019, 7(2): p. 88-91.
36. 36. Hao, D., Mehra, R.K., Luo, S., Nie, Z., Ren, X. and Fanhua, M., 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, 2020, 45(8): p. 5309-5325.
37. 37. Zou, J., Han, Y. and So, S.S., Overview of Artificial Neural Networks. 2008, 458: Humana Press.
38. 38. Uslu, S. and Celik, M.B., Performance and exhaust emission prediction of a SI engine fueled with I-amyl alcohol-gasoline blends: an ANN coupled RSM based optimization. Fuel, 2020, 265: p. 116922.
39. 39. Bayir, R. and Soylu, E., Real time determination of rechargeable batteries’ type and the state of charge via cascade correlation neural network. Elektronika Ir Elektrotechnika, 2018, 24(1): p. 25-30.
40. 40. Oğuz, H., Sarıtas, I., and Baydan, H.E., Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 2010, 37(9): p. 6579-6586.
41. 41. Tasdemir, S., Saritas, I., Ciniviz, M. and Allahverdi, N., Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine. Expert Systems with Applications, 2011, 38(11): p. 13912-13923.
42. 42. Singh, Y., Sharma, A., Singh, G.K., Singla, A. and Singh, N.K., Optimization of performance and emission parameters of direct injection diesel engine fuelled with pongamia methyl esters-response surface methodology approach. Industrial Crops and Products, 2018, 126: p. 218-226.
43. 43. Karagöz, M., ANN based prediction of engine performance and exhaust emission responses of a CI engine powered by ternary blends. International Journal of Automotive Science And Technology, 2020, 4(3): p. 180-184.
44. 44. Yusaf, T.F., Buttsworth, D., Saleh, K.H., and Yousif, B., CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Applied Energy, 2010, 87(5): p. 1661-1669.
45. 45. Ramesh, K., Alwarsamy, T., and Jayabal, S., Prediction of cutting process parameters in boring operations using artificial neural networks. Journal of Vibration and Control, 2015, 21(6): p. 1043-1054.
46. 46. Kurtgoz, Y., Karagoz, M. and Deniz, E., Biogas engine performance estimation using ANN. Engineering SScience and Technology, an International Journal, 2017, 20(6): p. 1563-1570.
47. 47. Krishnamoorthi, M., Malayalamurthi, R. and Shameer, P.M., RSM based optimization of performance and emission characteristics of DI compression ignition engine fuelled with diesel/aegle marmelos oil/diethyl ether blends at varying compression ratio, injection pressure and injection timing. Fuel, 221: p. 283-297.
48. 48. Awada, O.I., Mamat, R., Obed M. A. Azmi, W.H., Kadirgama, K., Yusri, I.M., Leman, A.M. and Yusaf, T., Response surface methodology (RSM) based multi-objective optimization of fusel oil-gasoline blends at different water content in SI engine. Energy Conversion and Management, 2017, 150: p. 222-241.
49. 49. Shameer, P.M. and Ramesh, K., Influence of antioxidants on fuel stability of Calophyllum inophyllum biodiesel and RSM-based optimization of engine characteristics at varying injection timing and compression ratio. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2017, 39: p. 4251-4273.
50. 50. Dubey, A., Prasad, R.S., Singh, J.K., and Nayyar, A., Optimization of diesel engine performance and emissions with biodiesel-diesel blends and EGR using response surface methodology (RSM). Cleaner Engineering and Technology, 2022, 8: p. 100509.
51. 51. Simsek S. and Uslu, S., Investigation of the effects of biodiesel/2-ethylhexyl nitrate (EHN) fuel blends on diesel engine performance and emissions by response surface methodology (RSM). Fuel, 2020, 275: p. 118005.
52. 52. Simsek, S., Uslu, S., and Simsek, H., Response surface methodology-based parameter optimization of single-cylinder diesel engine fueled with graphene oxide dosed sesame oil/diesel fuel blend. Energy and AI, 2022, 10: p. 100200.