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Evaluation of Emission Characteristics and Performance of Pomegranate Ethanol Blended S. I. Engine using Artificial Neural Network and Rule Learner Classifier

Evaluation of Emission Characteristics and Performance of Pomegranate Ethanol Blended S. I.... Waste pomegranate fruit is one of the new sources of ethanol. Using four different ethanol mixes, the emission performance of S.I. engine was measured at varied running speeds. Ethanol mixed gasoline enhances the quality of engine exhaust emissions, except for nitrogen oxides. Engine performance was found optimum using a 15% ethanol blend and a 1500 rpm speed. The emission characteristics were further examined using artificial neural network and rule learner classifiers. Experiments yielded data sets in which emission characteristics of engines were mapped in relation to engine speed and ethanol/petrol mixtures. These datasets were utilised to train artificial neural networks and rule learner classifiers to establish relationships among emission characteristics, speeds, and ethanol combinations. Both models were tested, and the rule learner classifier was found to be more accurate than the artificial neural network. Emission characteristics, speed, and ethanol combinations can all be correlated using the proposed rule learner algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India):Series A Springer Journals

Evaluation of Emission Characteristics and Performance of Pomegranate Ethanol Blended S. I. Engine using Artificial Neural Network and Rule Learner Classifier

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Publisher
Springer Journals
Copyright
Copyright © The Institution of Engineers (India) 2022
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-022-00639-z
Publisher site
See Article on Publisher Site

Abstract

Waste pomegranate fruit is one of the new sources of ethanol. Using four different ethanol mixes, the emission performance of S.I. engine was measured at varied running speeds. Ethanol mixed gasoline enhances the quality of engine exhaust emissions, except for nitrogen oxides. Engine performance was found optimum using a 15% ethanol blend and a 1500 rpm speed. The emission characteristics were further examined using artificial neural network and rule learner classifiers. Experiments yielded data sets in which emission characteristics of engines were mapped in relation to engine speed and ethanol/petrol mixtures. These datasets were utilised to train artificial neural networks and rule learner classifiers to establish relationships among emission characteristics, speeds, and ethanol combinations. Both models were tested, and the rule learner classifier was found to be more accurate than the artificial neural network. Emission characteristics, speed, and ethanol combinations can all be correlated using the proposed rule learner algorithm.

Journal

Journal of The Institution of Engineers (India):Series ASpringer Journals

Published: May 3, 2022

Keywords: Biofuel; Emission characteristics; Rule learner; Artificial neural network; Decision tree algorithm; Solid waste management

References