Experimental Analysis and Parametric Optimization of Linseed Oil Methyl Ester Blend-Fueled Variable Compression Ratio Diesel Engine | ||
| Renewable Energy Research and Applications | ||
| دوره 4، شماره 2، مهر 2023، صفحه 183-198 اصل مقاله (812.13 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22044/rera.2022.12394.1177 | ||
| نویسندگان | ||
| Ganesh S Warkhade1؛ Ganesh Babu Katam* 2؛ Veeresh Babu Alur3 | ||
| 1Department of Mechanical Engineering, Dr. Babasaheb Ambedkar Technological, Lonere (MS), India. | ||
| 2Department of Mechanical Engineering, National Institute of Technology Andhra Pradesh, TadepalliGudem (AP), India. | ||
| 3Department of Mechanical Engineering, National Institute of Technology, Warangal-506004, Telangana, India | ||
| چکیده | ||
| This paper analyses the VCR (variable compression ratio) engine's performance, combustion, and emission output responses. The experimental results were modelled using the Grey Taguchi method (GTM) for input parameters of compression ratio, load, and fuel blends. The objective is to find the optimal combination of input parameters in the minimum number of experiments for minimum emission, better performance, and combustion parameters. The Taguchi’s L9 orthogonal array with GTM is used to get the optimum combination of input parameters. The Taguchi was used to analyze the S/N ratio of experimental data and the gray-based method for optimization of multi-objective to single-objective optimization by assigning the suitable weighting factor to each response. The S/N ratio analysis of grey relational grade (GRG) shows the fuel B10, CR 16, and load at 100% of the optimal input factor level. This optimal level is further confirmed by the TOPSIS method. The analysis of variance (ANOVA) for input to GRG shows the highest influencing factor is the load with a 52.82% contribution, followed by CR at 28.38%, and fuel at 10.52%. The confirmatory results show an improvement of 56.1%. The novelty of this experimentation was to study feasibility of existing engine for alternative fuel with slight modification. At above optimal conditions, this biodiesel can be used efficiently in an unmodified compression ignition engine. | ||
| کلیدواژهها | ||
| Biodiesel؛ Characteristics؛ Optimization؛ GTM؛ TOPSIS | ||
| مراجع | ||
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[1] IEA, “World energy outlook 2007,” 2007.
[2] E. M. Shahid and Y. Jamal, “Production of biodiesel: A technical review,” Renew. Sustain. Energy Rev., vol. 15, no. 9, pp. 4732–4745, 2011, doi: 10.1016/j.rser.2011.07.079.
[3] K. R. Bandyopadhyay, “Biofuel promotion in India for transport: exploring the grey areas,” Energy Resour. Inst. New Delhi, India, p. 12, 2015.
[4] G. B. Katam, V. B. A, M. M. K, and G. S. Warkhade, “Review on Algae for Biodiesel fuel production, its Characteristics comparison with other and their Impact on Performance, combustion and emissions of Diesel engine,” World J. Eng., vol. 14, no. 2, pp. 127–138, Feb. 2017, doi: 10.1108/WJE-06-2016-0012.
[5] M. of N. & R. Energy, “National Policy on Biofuels,” 2017.
[6] H. K. Amarnath and P. Prabhakaran, “A study on the thermal performance and emissions of a variable compression ratio diesel engine fuelled with karanja biodiesel and the optimization of parameters based on experimental data,” Int. J. Green Energy, vol. 9, no. 8, pp. 841–863, 2012, doi: 10.1080/15435075.2011.647167.
[7] Y. Datta Bharadwaz, B. Govinda Rao, V. Dharma Rao, and C. Anusha, “Improvement of biodiesel methanol blends performance in a variable compression ratio engine using response surface methodology,” Alexandria Eng. J., vol. 55, no. 2, pp. 1201–1209, 2016, doi: 10.1016/j.aej.2016.04.006.
[8] I. M. Yusri, R. Mamat, W. H. Azmi, A. I. Omar, M. A. Obed, and A. I. M. Shaiful, “Application of response surface methodology in optimization of performance and exhaust emissions of secondary butyl alcohol-gasoline blends in SI engine,” Energy Convers. Manag., vol. 133, pp. 178–195, 2017, doi: 10.1016/j.enconman.2016.12.001.
[9] K. S. P. Ravikumar, “Optimization of operational parameters on performance and emissions of a diesel engine using biodiesel,” Int. J. Environ. Sci. Technol., vol. 11, no. 4, pp. 949–958, 2013, doi: 10.1007/s13762-013-0273-5.
[10] Z. Win, R. P. Gakkhar, S. C. Jain, and M. Bhattacharya, “Parameter optimization of a diesel engine to reduce noise, fuel consumption, and exhaust emissions using response surface methodology,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng., vol. 219, no. 10, pp. 1181–1192, 2005, doi: 10.1243/095440705X34919.
[11] V. H. Wilson, “Optimization of Diesel Engine Parameters Using Taguchi Method and Design of Evolution,” vol. XXXIV, no. 4, pp. 423–428, 2012.
[12] 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, no. Apr. 1, pp. 362–370, 2013, doi: 10.1016/j.apenergy.2012.10.055.
[13] A. H. N. Logothetis, “Characterizing and optimizing multi-response processes by the taguchi method,” Qual. Reliab. Eng. Int., vol. 4, no. October 1987, pp. 159–169, 1988.
[14] M. Gul, A. N. Shah, Y. Jamal, and I. Masood, “Multi-variable optimization of diesel engine fuelled with biodiesel using grey-Taguchi method,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 38, no. 2. pp. 621–632, 2016. doi: 10.1007/s40430-015-0312-x.
[15] S. D. Jadhav and M. S. Tandale, “Part load and full load multi-objective performance optimization of a single-cylinder diesel engine operating on Mangifera indica biodiesel as biofuel,” vol. 7269, no. November, 2016, doi: 10.1080/17597269.2016.1249740.
[16] H. W. Wu and Z. Y. Wu, “Combustion characteristics and optimal factors determination with Taguchi method for diesel engines port-injecting hydrogen,” Energy, vol. 47, no. 1, pp. 411–420, 2012, doi: 10.1016/j.energy.2012.09.027.
[17] G. Sakthivel et al., “Multi-criteria decision modelling approach for biodiesel blend selection based on GRA–TOPSIS analysis,” Int. J. Ambient Energy, vol. 35, no. 3, pp. 139–154, Jul. 2014, doi: 10.1080/01430750.2013.789984.
[18] J. M. Alonso, F. Alvarruiz, J. M. Desantes, L. Hernndez, V. Hernndez, and G. Molt, “Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions,” IEEE Trans. Evol. Comput., vol. 11, no. 1, pp. 46–55, Feb. 2007, doi: 10.1109/TEVC.2006.876364.
[19] N. Maheshwari, C. Balaji, and A. Ramesh, “A nonlinear regression based multi-objective optimization of parameters based on experimental data from an IC engine fueled with biodiesel blends,” Biomass and Bioenergy, vol. 35, no. 5, pp. 2171–2183, 2011, doi: 10.1016/j.biombioe.2011.02.031.
[20] T. Ganapathy, K. Murugesan, and R. P. Gakkhar, “Performance optimization of Jatropha biodiesel engine model using Taguchi approach,” Appl. Energy, vol. 86, no. 11, pp. 2476–2486, 2009, doi: 10.1016/j.apenergy.2009.02.008.
[21] D. Wu and H. Gao, “An Adaptive Particle Swarm Optimization for Engine Parameter Optimization,” Proc. Natl. Acad. Sci. India Sect. A Phys. Sci., 2016, doi: 10.1007/s40010-016-0320-y.
[22] R. P. Sivaramakrishnan Kaliamoorthy, “INVESTIGATION ON PERFORMANCE AND EMISSIONS OF A BIODIESEL ENGINE THROUGH OPTIMIZATION TECHNIQUES,” vol. 17, no. 1, pp. 179–193, 2013, doi: 10.2298/TSCI120105151K.
[23] G. Sakthivel and M. Ilangkumaran, “Optimisation of compression ignition engine performance with fishoil biodiesel using Taguchi-Fuzzy approach,” Int. J. Ambient Energy, vol. 38, no. 2, pp. 146–160, 2017, doi: 10.1080/01430750.2015.1074613.
[24] 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 aCRDI assisted CNG dual-fuel engine,” J. Nat. Gas Sci. Eng., vol. 21, pp. 524–531, 2014, doi: 10.1016/j.jngse.2014.09.022.
[25] Z. Y. Wu, H. W. Wu, and C. H. Hung, “Applying Taguchi method to combustion characteristics and optimal factors determination in diesel/biodiesel engines with port-injecting LPG,” Fuel, vol. 117, no. PART A, pp. 8–14, 2014, doi: 10.1016/j.fuel.2013.09.005.
[26] M. K. Yesilyurt and C. Cesur, “A statistical optimization attempt by applying the Taguchi technique for the optimum transesterification process parameters in the production of biodiesel from Papaver somniferum L. seed oil,” Fuel, vol. 329, p. 125406, 2022, doi: https://doi.org/10.1016/j.fuel.2022.125406.
[27] T. C. Taguchi, Genichi and Elsayed, Elsayed A and Hsiang, Quality engineering in production systems, 173rd ed. New York: McGraw-Hill, 1989.
[28] N. Senthilkumar, T. Tamizharasan, and V. Anandakrishnan, “An hybrid Taguchi-grey relational technique and cuckoo search algorithm for multi-criteria optimization in hard turning of AISI D3 steel,” J. Adv. Eng. Res., vol. 1, no. 1, pp. 16–31, 2014.
[29] R. S. Rao, C. G. Kumar, R. S. Prakasham, and P. J. Hobbs, “The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal,” Biotechnol. J., vol. 3, no. 4, pp. 510–523, 2008.
[30] J. L. Deng, “Introduction to Grey system theory,” J. Grey Syst., vol. 1, no. 1, pp. 1–24, 1989.
[31] C. C. Tsao, “Grey – Taguchi method to optimize the milling parameters of aluminum alloy,” pp. 41–48, 2009, doi: 10.1007/s00170-007-1314-3.
[32] H. Pervez, M. S. Mozumder, and A. H. I. Mourad, “Optimization of injection molding parameters for HDPE/TiO2 nanocomposites fabrication with multiple performance characteristics using the Taguchi method and grey relational analysis,” Materials (Basel)., vol. 9, no. 8, 2016, doi: 10.3390/ma9080710.
[33] Y. S. Tarng, S. C. Juang, and C. H. Chang, “The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing,” J. Mater. Process. Technol., vol. 128, no. 1–3, pp. 1–6, 2002, doi: 10.1016/S0924-0136(01)01261-4.
[34] Y. Kuo, T. Yang, and G.-W. Huang, “The use of a grey-based Taguchi method for optimizing multi-response simulation problems,” Eng. Optim., vol. 40, no. 6, pp. 517–528, 2008, doi: 10.1080/03052150701857645.
[35] A. V. S. Raju, “Optimization of Diesel Engine Parameters for Performance, Combustion and Emission Parameters using Taguchi and Grey Relational Analysis,” Glob. J. Res. Eng. A Mech. Mech. Eng., vol. 16, no. 3, 2016.
[36] P. K. Sahu and S. Pal, “Multi-response optimization of process parameters in friction stir welded AM20 magnesium alloy by Taguchi grey relational analysis,” J. Magnes. Alloy., vol. 3, no. 1, pp. 36–46, 2015, doi: 10.1016/j.jma.2014.12.002.
[37] A. Sanayei, S. F. Mousavi, and A. Yazdankhah, “Group decision making process for supplier selection with VIKOR under fuzzy environment,” Expert Syst. Appl., vol. 37, no. 1, pp. 24–30, 2010.
[38] G. H. Tzeng and S. H. Tasur, “The multiple criteria evaluation of grey relation model,” J. Grey Syst., vol. 6, no. 2, pp. 87–108, 1994.
[39] M.-F. Chen and G.-H. Tzeng, “Combining grey relation and TOPSIS concepts for selecting an expatriate host country,” Math. Comput. Model., vol. 40, no. 13, pp. 1473–1490, 2004.
[40] S. Jindal and B. L. Salvi, “Sustainability aspects and optimization of linseed biodiesel blends for compression ignition engine,” J. Renew. Sustain. Energy, vol. 43111, no. 4, pp. 1–10, 2012, doi: 10.1063/1.4737922.
[41] P. N. Krishnan and D. Vasudevan, “Performance , Combustion and Emission Characteristics of Variable Compression Ratio Engine Fuelled with Biodiesel,” Int. J. ChemTech Res., vol. 7, no. 1, pp. 234–245, 2015.
[42] J. B. Holman, Experimental Techniques for Engineers. New York: McGraw-Hill New York, 1988.
[43] P. J. P. J. Ross, Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design. 1996.
[44] S. V. Mohan and P. C. Mouli, “Assessment of aerosol (PM 10) and trace elemental interactions by Taguchi experimental design approach,” Ecotoxicol. Environ. Saf., vol. 69, no. 3, pp. 562–567, 2008.
[45] D. Mukhopadhyay, J. P. Sarkar, and S. Dutta, “Optimization of process factors for the efficient generation of biogas from raw vegetable wastes under the direct influence of plastic materials using Taguchi methodology,” Desalin. Water Treat., vol. 51, no. 13–15, pp. 2781–2790, 2013.
[46] J. Moran, E. Granada, J. L. Míguez, and J. Porteiro, “Use of grey relational analysis to assess and optimize small biomass boilers,” Fuel Process. Technol., vol. 87, no. 2, pp. 123–127, 2006.
[47] C.-P. Fung, “Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis,” Wear, vol. 254, no. 3, pp. 298–306, 2003.
[48] S. Opricovic and G. H. Tzeng, “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS,” Eur. J. Oper. Res., vol. 156, no. 2, pp. 445–455, 2004, doi: 10.1016/S0377-2217(03)00020-1.
[49] S. Zeyaeyan, E. Fattahi, A. Ranjbar, and M. Vazifedoust, “Classification of Rainfall Warnings Based on the TOPSIS Method,” Climate, vol. 5, no. 2, p. 33, 2017.
[50] S. O. N. Raj and S. Prabhu, “Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode,” Int. J. Mach. Mach. Mater., vol. 19, no. 1, pp. 76–94, 2017.
[51] Y. Wind and T. L. Saaty, “Marketing applications of the analytic hierarchy process,” Manage. Sci., vol. 26, no. 7, pp. 641–658, 1980.
[52] G. Gamst, L. S. Meyers, and A. J. Guarino, Analysis of variance designs: A conceptual and computational approach with SPSS and SAS. Cambridge University Press, 2008.
[53] D. C. Montgomery and G. C. Runger, Applied statistics and probability for engineers. John Wiley & Sons, 2010.
[54] R. A. Fisher, The design of experiments. Oliver And Boyd; Edinburgh; London, 1937.
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