Please use this identifier to cite or link to this item:
Title: Prediction of engine performance and emissions with Manihot glaziovii bioethanol − Gasoline blended using extreme learning machine
Authors: Sebayang, A.H. 
Masjuki, H.H. 
Ong, H.C. 
Dharma, S. 
Silitonga, A.S. 
Kusumo, F. 
Milano, J. 
Issue Date: 2017
Abstract: Bioethanol can potentially replace gasoline because of its lower exhaust emissions. The purpose of this study was to investigate the engine performance and exhaust emissions of Manihot glaziovii bioethanol–gasoline blends at different blend ratios (5%, 10%, 15%, and 20%). Tests were performed on a single-cylinder, four-stroke spark-ignition engine with engine speed was varied from 1600 to 3400 rpm, and the properties of the Manihot glaziovii bioethanol–gasoline blends were measured and analysed. The vapour pressure increased for fuel blends with low concentrations of bioethanol due to the oxygen within the bioethanol molecules and the contribution of the flame speed which can enhance the combustion and improved the engine efficiency. In addition, the engine torque, brake power, and brake-specific fuel consumption (BSFC) were measured, as well as the carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide emissions. For a fuel blend containing 20% bioethanol at an engine speed of 3200 rpm, the BSFC decreased, with maximum values of 270.7 g/kWh. The CO and HC emissions were lower for the Manihot glaziovii bioethanol–gasoline blends. In addition, an extreme learning machine (ELM) model was developed for application in the automotive and industrial sectors. This tool reduces the cost, time, and effort associated with experimental data. The blend ratio of the bioethanol–gasoline blends and the engine speed were used as the input data of the model, and the engine performance and exhaust emissions parameters were used as the output data. The coefficient of determination (R2) was within a range of 0.980–1.000, and the mean absolute percentage error was within a range of 0.411%−2.782% for all the parameters. The results indicate that the ELM model is capable of predicting the engine performance and exhaust emissions of bioethanol–gasoline fuel blends. © 2017 Elsevier Ltd
DOI: 10.1016/j.fuel.2017.08.102
Appears in Collections:UNITEN Scholarly Publication

Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.