Open Access Journal

ISSN : 2456-1290 (Online)

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

Monthly Journal for Computer Science and Engineering

Open Access Journal

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

Monthly Journal for Mechanical and Civil Engineering

ISSN : 2456-1290 (Online)

Call For Paper : Vol 11, Issue 05, May 2024
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.

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