access icon free Modelling of biodiesel blend using optimised deep belief network: blending waste cooking oil methyl ester with tyre pyrolysis oil

© The Institution of Engineering and Technology
Received 07/08/2019, Accepted 04/06/2020, Revised 22/02/2020, Published 21/07/2020

This study introduces a new biodiesel blend as an alternative for diesel using waste cooking oil methyl ester by adding tyre pyrolysis oil and cerium oxide. Despite the conventional biodiesel blending models, this study made an effort to efficiently measure the prediction rate of these blended fuels by modelling through the deep belief network (DBN). To attain the accurate prediction, this study moves on with the new logic of optimal tuning of the count of hidden neurons in DBN. The optimal selection is carried out by introducing a new algorithm named lioness updated crow search algorithm (LCSA), which hybrids the concept of the lion algorithm (LA) and crow search algorithm (CSA). Finally, the proposed work is analysed and compared over other conventional models with respect to emission analysis and error analysis. From the analysis, the proposed model in terms of mean deviation (MD) measure has gained betterment and is 75.57, 17.71, 85.55, and 74.19% better than grey wolf optimiser (GWO), whale optimisation algorithm (WOA), LA, and CSA, respectively. For the mean absolute error measure, the implemented model is 42.38, 24.42, 43.53 and 36.72% improved than GWO, WOA, LA, and CSA, respectively.

Inspec keywords: petroleum; combustion; vegetable oils; tyres; pyrolysis; belief networks; diesel engines; biofuel; blending

Other keywords: blended fuels; biodiesel blend; optimised deep belief network; biodiesel blending changes; petroleum diesel; blend percentages; particulate matter; conventional biodiesel blending models; waste cooking oil methyl ester; exhaust emissions; tyre pyrolysis oil; crow search algorithm

Subjects: Biotechnology industry; Fuel processing industry; Industrial processes; Optimisation techniques; Products and commodities; Engineering materials; Engines; Environmental issues

References

    1. 1)
      • 13. Pardo, F., Rosas, J.M., Santos, A., et al: ‘Remediation of a biodiesel blend-contaminated soil by using a modified Fenton process’, Environ. Sci. Pollut. Res., 2014, 21, (21), pp. 12198–12207.
    2. 2)
      • 4. Best, R.J., Kennedy, J.M., Morrow, D.J., et al: ‘Steady-state and transient performance of biodiesel-fueled compression-ignition-based electrical generation’, IEEE Trans. Sustain. Energy, 2011, 2, (1), pp. 20–27.
    3. 3)
      • 23. Gaonkar, N., Vaidya, R.G.: ‘A simple model to predict the biodiesel blend density as simultaneous function of blend percent and temperature’, Environ. Sci. Pollut. Res., 2016, 23, (10), pp. 9260–9264.
    4. 4)
      • 9. Shirneshan, A., Samani, B. H., Ghobadian, B.: ‘Optimization of biodiesel percentage in fuel mixture and engine operating conditions for diesel engine performance and emission characteristics by artificial bees colony algorithm’, Fuel, 2016, 184, pp. 518–526.
    5. 5)
      • 16. Bhattacharyya, A., Rajanikanth, B.: ‘Discharge plasma combined with bauxite residue for biodiesel exhaust cleaning: A case study on NOx removal’, IEEE Trans. Plasma Sci., 2015, 43, (6), pp. 1974–1982.
    6. 6)
      • 40. Datta, A., Mandal, B.K.: ‘A numerical study on the performance, combustion and emission parameters of a compression ignition engine fuelled with diesel, palm stearin biodiesel and alcohol blends’, Clean Technol. Environ. Policy, 2017, 19, (1), pp. 157–173.
    7. 7)
      • 25. Sivaramakrishnan, R., Incharoensakdi, A.: ‘Production of methyl ester from two microalgae by two-step transesterification and direct transesterification’, Environ. Sci. Pollut. Res., 2017, 24, (5), pp. 4950–4963.
    8. 8)
      • 46. Askarzadeh, A.: ‘A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm’, Comput. Struct., 2016, 169, pp. 1–12.
    9. 9)
    10. 10)
      • 48. Avinash, A., Murugesan, A.: ‘Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment’, 2018, 216, pp. 322–329.
    11. 11)
      • 15. Mukherjee, D.S., Rajanikanth, B.S.: ‘DBD plasma based ozone injection in a biodiesel exhaust and estimation of NOx reduction by dimensional analysis approach’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (6), pp. 3267–3274.
    12. 12)
      • 12. Rashedul, H.K., Kalam, M.A., Masjuki, H.H., et al: ‘Attempts to minimize nitrogen oxide emission from diesel engine by using antioxidant-treated diesel-biodiesel blend’, Environ. Sci. Pollut. Res., 2017, 24, (10), pp. 9305–9313.
    13. 13)
      • 36. Alagu, K., Nagappan, B., Jayaraman, J., et al: ‘Impact of antioxidant additives on the performance and emission characteristics of C.I engine fuelled with B20 blend of rice bran biodiesel’, Environ. Sci. Pollut. Res., 2018, 25, (18), pp. 17634–17644.
    14. 14)
      • 50. Asgari, S., Sahari, M.A., Barzegar, M.: ‘Practical modeling and optimization of ultrasound-assisted bleaching of olive oil using hybrid artificial neural network-genetic algorithm technique’, Comput. Electron. Agric., 2017, 140, pp. 422–432.
    15. 15)
      • 49. Selvaraj, R., Moorthy, I.G., Kumar, R.V., et al: ‘Microwave mediated production of FAME from waste cooking oil: modelling and optimization of process parameters by RSM and ANN approach’, Fuel, 2019, 2371, pp. 40–49.
    16. 16)
      • 43. Tang, B., Liu, X., Lei, J., et al: ‘Deepchart: combining deep convolutional networks and deep belief networks in chart classification’, Signal Process., 2016, 124, pp. 156–161.
    17. 17)
      • 38. Su, P.H., Geng, P., Wei, L., et al: ‘PM and PAHs emissions of ship auxiliary engine fuelled with waste cooking oil biodiesel and marine gas oil’, IET Intell. Transp. Syst., 2018, 13, (1), pp. 218–227.
    18. 18)
      • 2. Albahri, T.A.: ‘Developing correlations for the properties of petroleum fuels and their fractions’, Fluid Phase Equilib., 2012, 315, pp. 113–125.
    19. 19)
      • 20. Barbari, M., Conti, L., Rossi, G., et al: ‘Supply of wood as environmental enrichment material to post-weaning piglets’, Agronomy Res., 2017, 15, (2), pp. 313–321.
    20. 20)
      • 39. Barros, R.W.S., Guerrero, J.R.H., Dutra, J.C.C.: ‘Experimental evaluation of the use of cottonseed biodiesel and mixtures with commercial diesel engine generator’, IEEE Latin Am. Trans., 2018, 16, (2), pp. 489–496.
    21. 21)
      • 32. Singh, G., Jain, V.K., Singh, A.: ‘Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system’, Energy Environ., 2018, 29, (7), pp. 1073–1097.
    22. 22)
      • 41. Chaudhary, A., Gupta, A., Kumar, S., et al: ‘Pool fires of jatropha biodiesel and their blends with petroleum diesel’, Exp. Therm Fluid Sci., 2019, 101, pp. 175–185.
    23. 23)
      • 18. Ge, J.C., Kim, H.Y., Yoon, S.K., et al: ‘Reducing volatile organic compound emissions from diesel engines using canola oil biodiesel fuel and blends’, Fuel, 2018, 218, pp. 266–274.
    24. 24)
      • 11. Sadrolhosseini, A.R., Noor, A.S.M., Mehdipour, L.A., et al: ‘Application of thermal lens technique to measure the thermal diffusivity of biodiesel blend’, Opt. Rev., 2015, 22, (2), pp. 289–293.
    25. 25)
      • 33. Nipanikar, S.I., Deepthi, H.: ‘Enhanced whale optimization algorithm and wavelet transform for image steganography’, Multimedia Research (MR), 2019, 2, (3), pp. 23–32.
    26. 26)
      • 3. Wagner, E.P., Lambert, P.D., Moyle, T.M., et al: ‘Diesel vehicle performance on unaltered waste soybean oil blended with petroleum fuels’, Fuel, 2013, 107, pp. 757–765.
    27. 27)
      • 42. Tamizhdurai, P., Sakthinathan, S., Chen, S.-M., et al: ‘Environmentally friendly synthesis of CeO2 nanoparticles for the catalytic oxidation of benzyl alcohol to benzaldehyde and selective detection of nitrite’, Sci. Rep., 2017, 7, pp. 1–23.
    28. 28)
      • 35. Ramesha, D.K., Kumara, G.P., Lalsaheb Mohammed, A.V.T., et al: ‘An experimental study on usage of plastic oil and B20 algae biodiesel blend as substitute fuel to diesel engine’, Environ. Sci. Pollut. Res., 2016, 23, (10), pp. 9432–9439.
    29. 29)
      • 21. Raheman, H., Jena, P.C., Jadav, S.S.: ‘Performance of a diesel engine with blends of biodiesel (from a mixture of oils) and high-speed diesel’, Int. J. Energy Environ. Eng., 2013, 4, (1), p. 6.
    30. 30)
      • 34. Rahman, A.: ‘Effect of induction hydroxy and hydrogen along with algal biodiesel blend in a CI engine: a comparison of performance and emission characteristics’, Environ. Sci. Pollut. Res., 2019, 26, (10), pp. 9552–9560.
    31. 31)
      • 22. Zhao, J., Wang, J ‘Adaptive observer for joint estimation of oxygen fractions and blend level in biodiesel fueled engines’, IEEE Trans. Control Syst. Technol., 2015, 23, (1), pp. 80–90.
    32. 32)
      • 30. Ponticorvo, M., Rega, A., Ferdinando, A. D., et al: ‘Approaches to embed bio-inspired computational algorithms in educational and serious games’. Proc. of the 1st Int. Workshop on Cognition and Artificial Intelligence for Human-Centred Design, 2017, Vol. 2099, pp. 21–26.
    33. 33)
      • 28. Thomas, R., Rangachar, M.J.S.: ‘Hybrid optimization based DBN for face recognition using low-resolution images’, Multimed. Res. (MR), 2019, 1, (1), pp. 1–11.
    34. 34)
      • 45. Boothalingam, R.: ‘Optimization using lion algorithm: a biological inspiration from lion's social behavior’, Evol. Intell., 2018, 11, (1–2), pp. 31–52.
    35. 35)
      • 37. de Moura, R.R., Dias, A.N., Granjão, V.F.: ‘Ednei gilberto primel, marcelo gonçalves montes D'Oca, ‘determination of acylglycerols and glycerol in castor:soybean biodiesel blend produced by a base/acid-catalyzed process’, J. Am. Oil Chem. Soc., 2015, 92, (11–12), pp. 1555–1565.
    36. 36)
      • 24. Brasil, H., Pereira, P., Corrêa, J., et al: ‘Erratum to: preparation of hydrotalcite–hydroxyapatite material and its catalytic activity for transesterification of soybean oil’, Catal. Lett., 2017, 147, (11), pp. 2901–2901.
    37. 37)
      • 52. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 46–61.
    38. 38)
      • 51. Aghbashlo, M., Hosseinpour, S., Tabatabaei, M., et al: ‘Multi-objective exergetic and technical optimization of a piezoelectric ultrasonic reactor applied to synthesize biodiesel from waste cooking oil (WCO) using soft computing techniques’, Fuel, 2019, 2351, pp. 100–112.
    39. 39)
      • 17. Ge, J.C., Kim, H.Y., Yoon, S.K., et al: ‘Optimization of palm oil biodiesel blends and engine operating parameters to improve performance and PM morphology in a common rail direct injection diesel engine’, Fuel, 2020, 260, p. 116326.
    40. 40)
      • 26. Na, S., Kong, B., Choi, C., et al: ‘Transesterification and compatibilization in the blends of bisphenol-A polycarbonate and poly(trimethylene terephthalate)’, Macromol. Res., 2005, 13, (2), pp. 88–95.
    41. 41)
      • 47. Mirjalili, S., Lewis, A.: ‘The whale optimization algorithm’, Adv. Eng. Softw., 2016, 95, pp. 51–67.
    42. 42)
      • 29. Mahammad Shareef, S.K., Srinivasa Rao, R: ‘Optimal reactive power dispatch under unbalanced conditions using hybrid swarm intelligence’, Comput. Electr. Eng., 2018, 69, pp. 183–193.
    43. 43)
      • 27. Lecocq, J.: ‘Further observations on the transesterification reactions of ethylene phosphate with 2-amino alcohols’, Experientia, 1966, 22, (6), pp. 361–362.
    44. 44)
      • 44. Mannepalli, K., Sastry, P.N., Suman, M.: ‘A novel adaptive fractional deep belief networks for speaker emotion recognition’, Alexandria Eng. J., 2017, 56, (4), pp. 485–497.
    45. 45)
      • 10. Shirneshan, A., Almassi, M., Ghobadian, B., et al: ‘Response surface methodology (RSM) based optimization of biodiesel-diesel blends and investigation of their effects on diesel engine operating conditions and emission characteristics’, Environ. Eng. Manage. J., 2016, 15, (12), pp. 2771–2780.
    46. 46)
      • 8. Shirneshan1, A. R., Almassi, M., Ghobadian, B., et al: ‘Investigating the effects of biodiesel from waste cooking oil and engine operating conditions on the diesel engine performance by response surface methodology’, Trans. Mech. Eng., 2014, 38, (M2), pp. 289–301.
    47. 47)
      • 19. Boman, C., Ohman, M., Nordin, A.: ‘Trace element enrichment and behavior in wood pellet production and combustion processes’, Energy Fuels, 2006, 20, (3), pp. 993–1000.
    48. 48)
      • 1. Ammar, S.H., Kareem, Y.S., Ali, A.D.: ‘Photocatalytic oxidative desulfurization of liquid petroleum fuels using magnetic CuO–Fe3O4 nanocomposites’, J. Environ. Chem. Eng., 2018, 6, (6), pp. 6780–6786.
    49. 49)
      • 31. Malhotra, J., Bakal, J.: ‘Grey wolf optimization based clustering of hybrid fingerprint for efficient de-duplication’, Multi-agent and Grid Syst., 2018, 14, (2), pp. 145–160.
    50. 50)
      • 14. Ge, J.C., Yoon, S.K., Choi, N.J.: ‘Using canola oil biodiesel as an alternative fuel in diesel engines: A review’, Appl. Sci., 2017, 7, (9), p. 881.
    51. 51)
      • 5. Sarah, A.G., Rajanikanth, B.S.: ‘NOx reduction from biodiesel exhaust by plasma induced ozone injection supported by lignite waste adsorption’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (4), pp. 1–9.
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