Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

IInternational Journal of Engineering Research in Mechanical and Civil Engineering (IJERMCE)

Monthly Journal for Mechanical and Civil Engineering

ISSN : 2456-1290 (Online)

Estimation of output parameters for 4-stroke c i engine using artificial neural network with pongamia and biogas as a fuel

Author : Manjunath B B 1 Dr. Putta Boregowda B 2 Dr. Candrashaker R 3

Date of Publication :11th May 2017

Abstract: This paper identifies the technical feasibility of using Pongamia (Honge) oil and Biogas under Dual-fuel mode. This technology can be applied in rural area for electricity generation in developing countries. The use of Honge oil and Biogas is considered as sustainable energy supply, when both are produced locally. The experiment is carried out to study the performance of diesel engine (CI Engine) under dual-fuel mode, which is carried out on 5KW diesel generator set. The esterified honge oil (EHO) blends with diesel and bottled biogas was used for experimentation, and the gas is directly added to inlet air by modifying the induction manifold. The experiment is carried out for varying lamp loads. The engine shows considerably high thermal efficiency for EHO and biogas combination. The mechanical efficiency was improved than diesel biogas operation. One more point noticed that introduction of biogas drastically reduces EHO blends and diesel consumption. The part of paper also describes application of Artificial Neural Network (ANN) to estimate Thermal efficiency and BSFC of the engine, from comparison and observation it clears that ANN estimates close to experimental value when 90% of data is at training set. Estimated Thermal efficiency and BSFC using Artificial Neural Network correlates well with measured value. The mass flow rate, speed temperature, fuel consumption rate and time are used as a input parameters.

Reference :

    1. Rene alvarez, Saul villca, Gunnar Liden, "Biogas production from ilama and cow manure at high altitude", Biomass & Bioenergy, vol30(2006) pp 66-75.
    2. Shanta sathyanarayan, Paresh Murkete, Ramkant,"Biogas production enhancement by Brassica compestries amendment in cattle dung digesters", Biomass & Bioenergy, vol32(2008) pp 210-215.
    3. Karina ribeiro salomon, Electo Eduardo Silva Lara, "Estimate of the electric energy generating potential for different sources of biogas in brazil", Biomass & Bioenergy, vol33(2009) pp 1101-1107.
    4. Pal Borejesson & Maria Berglund,"Environmental systems analysis of biogas systems-Part2; The Environmental impact of replacing various reference systems", Biomass & Bioenergy, vol31(2007) pp 326-344.
    5. Harold Keener & Jay Martin, [2009]," A study of Biogas utilization efficiency highlighting Internal combustion Electrical Generator units", Thesis, The Ohio State University.
    6. Kiani MK, Ghobadian B, Tavakoli T, Nikbakht AM, Najafi G. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol–gasoline blends. Energy 2010;34:65– 9.
    7. Ghobadian B, Barat G, Rahimi H, Nikbakht AM, Najafi G, Yusaf T. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew Energ 2009;4:976–82.
    8. Yusaf TF, Buttsworth DR, Saleh KH, Yousif BF. CNG–diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Appl Energ 2010;87:1661–9.
    9. Togun NK, Baysec S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Appl Energ 2010;87:349–55.
    10. Lucas A, Duran A, Carmona M, Lapuerta M. Modeling diesel particulate emissions with neural networks. Fuel 2001;80:539–48.

Recent Article