Predictive modeling of engine emissions using machine learning: A review. (2021)

• Record Type: Journal Article
• Authors: Khurana, Shivansh; Saxena, Shubham; Jain, Sanyam; Dixit, Ankur
• Is Part Of: Materials today. Volume 38(2021)Supplement Part 1
• Page Start: 280
• Page End: 284
• Publication Date: 2021
• Subjects: Predictive modelling -- Engine emissions -- Bio-fuels -- Machine learning -- Artificial intelligence; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.07.204
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The increase in population has led to increase in demand of automobiles across the globe. Concerns about the pollutants emitted from an engine are growing periodically. The paper has tried to show an exploratory review of the various methodologies adopted for accurately measuring and analysing exhaust engine emissions. The paper has the objective of showing the main conclusions of the recent research performed since last decade (2008 to 2020) for the above mentioned topic with the help of artificial intelligence methodologies. This review addresses an important application of artificial intelligence that can be applied in measuring the engine emissions. The measurement of these engine emissions are mandatory for every automobile company. This task is usually achieved by repeated testing of automobile which is not a cost efficient process. These process usually involves expensive test rigs installation. But predictive modeling for accurate testing of emissions can be used as a digital/virtual tool. Hence there is an extensive scope of research in this field. The paper has tried to showcase emerging trend of advanced machine learning algorithms that can easily help in generating emission data in a simple manner.