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Research Paper | Mechanical Engineering | India | Volume 4 Issue 11, November 2015

Performance and Emissions Characteristics of Cotton Seed Oil Biodiesel Blend in CI Engine using Artificial Neural Network (Back Propagation)

R. Ramachandra ^[7] | V. Pandu Rangadu

Abstract: Unique the ascent in cost and utilization of petroleum items and their impacts on the industrialization and modernization of the world have been one of the key issues of the specialists. CI (Diesel) engine, one of the parts in light of the fossil fuel, is a prime issue for tree huggers and financial experts. To defeat this issue and as a substitute for diesel, bio fuel is a superior alternative to ration the constrained store of fossil energizes, for example, petroleum, coal and common gas. Biodiesel, which is created from assortment of vegetable oils and creature fat through transesterification, has a considerable measure of specialized focal points over fossil fills, for example, lower general fumes discharge and harmfulness, biodegradability, inference from a renewable and residential feed stock and immaterial sulphur content. This paper manages simulated neural system (ANN) demonstrating of a diesel engine utilizing variable Cotton seed oil mixes to foresee the engine execution. To obtain information for preparing and testing the proposed ANN, a Single chamber, four-stroke diesel engine was fuelled with mixed diesel and worked at distinctive engine speeds and loads. The exploratory results uncovered that mix of cotton seed oil with diesel fuel give better engine execution. Utilizing a percentage of the trial information for preparing, an ANN model was created taking into account standard Back-Propagation algorithm for the engine. Examination of the exploratory information by the ANN demonstrating that there is a decent connection between the anticipated information came about because of the ANN and with the deliberate ones. Subsequently, the ANN turned out to be an attractive expectation system in the assessment of the tried diesel engine parameters.

Keywords: Artificial Neural Network, BP - Brake Power, BSFC - Brake Specific Fuel Consumption, MSE - Mean square Error, CO - Cotton seed Oil, BTE Brake warm efficiency, Emissions CO, HC, NOx, SMOKE and EGT

Edition: Volume 4 Issue 11, November 2015,

Pages: 1343 - 1349

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