Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA

Open Access

Year : 2024 | Volume :11 | Special Issue : 11 | Page : 12-27
By

    S. Lalhriatpuia

  1. Neeraj Budhraja

  2. Kiran Pal

  1. Research Scholar, Department of Mechanical Engineering, Delhi Technological University, Delhi, India
  2. Research Scholar, Department of Mechanical Engineering, Delhi Technological University, Delhi, India
  3. Assistant Professor, Department of Mathematics, DITE DSEU Okhla Campus II, Delhi Skill & Entrepreneurship, Delhi, India

Abstract

In this study, the liquid metal nanocomposites were investigated using artificial neural network (ANN)
prediction capabilities for Compression Ignition (CI) engine performance. The independent input
variables selected were load (20-100%), Liquid-metal nanocomposites Doped Rate (NDR, 0-50 ppm),
and Biogas Flow Rate (BFR, 0.5-1.0 kg/h). The Central Composite Face-Centered Design (CCFCD)
was used in conjunction with the selected input variables and output parameters to assist in the
preparation of the Design of Experiment (DOE). The proportion of error for the ANN projected
output responses is determined for each run in the DOE. ANN model’s predictions exhibited a good
coefficient of determination (R2), minimal Root Mean Square error (RMSE), and low Mean Absolute
deviation (MAD), indicating accurate and reliable prediction capability. A response that was
optimum according to the ANN model’s optimization occurred at 74.5% load, 10.55 ppm NDR, and
0.656 kg/h BFR. The optimization response concludes that combining Liquid-metal nanocomposites
and biogas contributes positively to diesel engine performances.

Keywords: ANN, Nanocomposites, Biogas, Performance, Emission

[This article belongs to Special Issue under section in Journal of Polymer and Composites(jopc)]

How to cite this article: S. Lalhriatpuia, Neeraj Budhraja, Kiran Pal , Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA jopc 2024; 11:12-27
How to cite this URL: S. Lalhriatpuia, Neeraj Budhraja, Kiran Pal , Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA jopc 2024 {cited 2024 Feb 23};11:12-27. Available from: https://journals.stmjournals.com/jopc/article=2024/view=133536

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References

  1. Fathi M, Ganji DD, Jahanian O. Intake charge temperature effect on performance characteristics of direct injection low-temperature combustion engines. J Therm Anal Calorim. 2020; 139: 2447–2454. doi: 10.1007/s10973-019-08515-y.
  2. Budhraja N, Pal A, Mishra RS. Parameter optimization for enhanced biodiesel yield from Linum usitatissimum oil through solar energy assistance. Biomass Convers Biorefin. 2022. doi: 10.1007/s13399-022-03649-w.
  3. Barik D, Sivalingam M. Performance and Emission Characteristics of a Biogas Fueled DI Diesel Engine. SAE Technical Paper 2013-01-2507. 2013. doi: 10.4271/2013-01-2507.
  4. Barik D, Murugan S. Experimental investigation on the behavior of a DI diesel engine fueled with raw biogas–diesel dual fuel at different injection timing. Journal of the Energy Institute. 2016; 89 (3): 373–388. doi: 10.1016/j.joei.2015.03.002.
  5. Mahla SK, Singla V, Sandhu SS, Dhir A. Studies on biogas-fuelled compression ignition engine under dual fuel mode. Environmental Science and Pollution Research. 2018; 25: 9722–9729. doi: 10.1007/s11356-018-1247-4.
  6. Aklouche FZ, Loubar K, Bentebbiche A, Awad S, Tazerout M. Experimental investigation of the equivalence ratio influence on combustion, performance and exhaust emissions of a dual fuel diesel engine operating on synthetic biogas fuel. Energy Convers Manag. 2017; 152: 291–299. doi: 10.1016/j.enconman.2017.09.050.
  7. Swami Nathan S, Mallikarjuna JM, Ramesh A. An experimental study of the biogas–diesel HCCI mode of engine operation. Energy Convers Manag. 2010; 51 (7): 1347–1353. doi: 10.1016/j.enconman.2009.09.008.
  8. Ambarita H. Performance and emission characteristics of a small diesel engine run in dual-fuel (diesel-biogas) mode. Case Studies in Thermal Engineering. 2017; 10: 179–191. doi: 10.1016/j.csite.2017.06.003.
  9. Barik D, Murugan S. Investigation on combustion performance and emission characteristics of a DI (direct injection) diesel engine fueled with biogas–diesel in dual fuel mode. Energy. 2014; 72: 760–771. doi: 10.1016/j.energy.2014.05.106.
  10. Paramashivaiah BM, Banapurmath NR, Rajashekhar CR, Khandal SV. Studies on Effect of Graphene Nanoparticles Addition in Different Levels with Simarouba Biodiesel and Diesel Blends on Performance, Combustion and Emission Characteristics of CI Engine. Arab J Sci Eng. 2018; 43: 4793–4801. doi: 10.1007/s13369-018-3121-6.
  11. Sathiamurthi P, Karthi Vinith KS, Sivakumar A. Performance and emission test in CI engine using magnetic fuel conditioning with nano additives. International Journal of Recent Technology and Engineering. 2019; 8 (3): 7823–7826. doi: 10.35940/ijrte.C6213.098319.
  12. Sadhik Basha J. An Experimental Analysis of a Diesel Engine Using Alumina Nanoparticles Blended Diesel Fuel. SAE Technical Paper 2014-01-1391. 2014. doi: 10.4271/2014-01-1391.
  13. Sahoo RR, Jain A. Experimental analysis of nanofuel additives with magnetic fuel conditioning for diesel engine performance and emissions. Fuel. 2019; 236: 365–372. doi: 10.1016/j.fuel.2018.09.027.
  14. Gumus S, Ozcan H, Ozbey M, Topaloglu B. Aluminum oxide and copper oxide nanodiesel fuel properties and usage in a compression ignition engine. Fuel. 2016; 163: 80–87. doi: 10.1016/j.fuel.2015.09.048.
  15. Lenin MA, Swaminathan MR, Kumaresan G. Performance and emission characteristics of a DI diesel engine with a nanofuel additive. Fuel. 2013; 109: 362–365. doi: 10.1016/j.fuel.2013.03.042.
  16. Fangsuwannarak K, Triratanasirichai K. Effect of metalloid compound and bio-solution additives on biodiesel engine performance and exhaust emissions. Am J Appl Sci. 2013; 10 (10): 1201–1213. doi: 10.3844/ajassp.2013.1201.1213.
  17. Liu J, Yang J, Sun P, Ji Q, Meng J, Wang P. Experimental investigation of in-cylinder soot distribution and exhaust particle oxidation characteristics of a diesel engine with nano-CeO2 catalytic fuel. Energy. 2018; 161: 17–27. doi: 10.1016/j.energy.2018.07.108.
  18. Karki S, Gohain MB, Yadav D, Ingole PG. Nanocomposite and bio-nanocomposite polymeric materials/membranes development in energy and medical sector: A review. International Journal of Biological Macromolecules. 2021; 193: 2121-2139. doi: 10.1016/j.ijbiomac.2021.11.044.
  19. Malakooti MH, Bockstaller MR, Matyjaszewski K, Majidi C. Liquid metal nanocomposites. Nanoscale Advances. 2020; 2 (7): 2668-2677. doi: 10.1039/D0NA00148A
  20. Tetteh EK, Rathilal S. Effects of a polymeric organic coagulant for industrial mineral oil wastewater treatment using response surface methodology (RSM). Water SA. 2018; 44 (2): 155-161. doi: 10.4314/wsa.v44i2.02.
  21. Ghanbari M, Mozafari-Vanani L, Dehghani-Soufi M, 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: 100091. doi: 10.1016/j.ecmx.2021.100091.
  22. Mahla SK, Safieddin Ardebili SM, Mostafaei M, Dhir A, Goga G, Chauhan BS. Multi-objective optimization of performance and emissions characteristics of a variable compression ratio diesel engine running with biogas-diesel fuel using response surface techniques. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2020: 1–18. doi: 10.1080/15567036.2020.1813847.
  23. Lalhriatpuia S, Pal A. Performance and Emissions Analysis of a Dual Fuel Diesel Engine with Biogas as Primary Fuel, In: Kumar A, Pal A, Kachhwaha SS, Jain PK, editors., Singapore: Springer Singapore. 2021, 327–339. doi: 10.1007/978-981-15-9678-0_29.
  24. Budhraja N, Pal A, Jain M, Mishra RS. Comparative Analysis of the Engine Emissions from CI Engine Using Diesel–Biodiesel–Ethanol Blends. In: Kumar A, Pal A, Kachhwaha SS, Jain PK, editors., Singapore: Springer Singapore. 2021, 363–370. doi: 10.1007/978-981-15-9678-0_32.
  25. Pradeep T, GuhaRay A, Bardhan A, Samui P, Kumar S, Armaghani DJ. Reliability and Prediction of Embedment Depth of Sheet pile Walls Using Hybrid ANN with Optimization Techniques. Arab J Sci Eng. 2022; 47: 12853–12871. doi: 10.1007/s13369-022-06607-w.
  26. Tayyab M, Ahmad S, Akhtar MJ, Sathikh PM, Singari RanganathM. Prediction of mechanical properties for acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling using artificial neural network and genetic algorithm. Int J Comput Integr Manuf. 2023; 36 (9): 1295-1312. doi: 10.1080/0951192X.2022.2104462.

Special Issue Open Access Original Research
Volume 11
Special Issue 11
Received December 1, 2023
Accepted January 4, 2024
Published February 23, 2024