Online Big-Data Monitoring and Assessment Framework for Internal Combustion Engine with Various Biofuels

Abstract

As the primary power source for automobiles, the internal combustion (IC) engines have been widely used and served millions of people worldwide. With increasingly stringent environmental regulations, biofuels have been obtained more attentions and are being used as alternative fuel to power IC engines. However, there are currently no standard solutions or well-established monitoring and assessment methods that can effectively evaluate the IC engine’s performance with biofuels. The expectation for biofuels is to keep the engine’s lifetime as long as the conventional fuels, or even longer. Otherwise, their usage would be unnecessary because they would reduce the lifecycle of the engine and also cause more waste and pollution. To address this challenge, we initially designed two biofuels: waste cooking oil biofuel (WCOB) and lamb fat biofuel (LFB). Then we proposed an online big-data monitoring and assessment framework for IC engines operating with various types of fuel. We conducted comprehensive experiments and comparisons based on the proposed framework. The results indicate that LFB performs best under all the performance indicators.

Publication DOI: https://doi.org/10.53941/ijamm.2023.100001
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Materials Research (AIMR)
College of Engineering & Physical Sciences > Aston Advanced Materials
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Additional Information: Copyright (c) 2023 Ming Zhang, Vikas Sharma, Zezhong Wang, Yu Jia, Abul Kalam Hossain, Yuchun Xu Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication ISSN: 2653-777X
Last Modified: 23 Apr 2024 07:26
Date Deposited: 03 Oct 2023 07:11
Full Text Link:
Related URLs: https://www.sci ... 5-paper216.html (Publisher URL)
PURE Output Type: Article
Published Date: 2023-06-01
Published Online Date: 2023-05-30
Accepted Date: 2023-04-26
Authors: Zhang, Ming (ORCID Profile 0000-0001-5202-5574)
Sharma, Vikas
Wang, Zezhong
Jia, Yu (ORCID Profile 0000-0001-9640-1666)
Hossain, Abul Kalam (ORCID Profile 0000-0002-8713-8058)
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)

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