ANN virtual sensors for emissions prediction and control
Wai Kean Yap and
Vishy Karri
Applied Energy, 2011, vol. 88, issue 12, 4505-4516
Abstract:
This paper demonstrates the use of artificial neural networks virtual sensors in emissions prediction and control for a gasoline engine. Tailpipe emissions and engine parameters were first measured experimentally to form a comprehensive database for network training and testing. Individual predictive models were constructed using the optimization layer-by-layer neural network. Simulation results demonstrated that the networks, as virtual sensors, can accurately predict the engine parameters and emissions quantitatively and qualitatively with RMS errors below 9%. The second part of this paper then presents a virtual sensor control model which is the combination of the two individual emissions and engine predictive models developed previously. The main objective of this part is to control the exhaust emissions within the desired limits by predicting optimum engine parameters with the use of artificial neural network virtual sensors. Results showed that the emissions levels were successfully controlled within the defined limits, with maximum tolerance of 6%. This first part of this paper demonstrated that with the use of artificial neural network virtual sensors, emissions and engine parameters can be accurately predicted. Hence with accurate virtual sensors, emissions were then controlled within the desired limits by optimizing the engine parameters. This proposed work demonstrated a viable and accurate methodology in emissions predictive and control. By applying virtual sensor models, the need additional, cumbersome and costly measuring and monitoring devices can be eliminated.
Keywords: Virtual sensor; Artificial neural networks; Emissions predictive control (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261911003357
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:88:y:2011:i:12:p:4505-4516
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2011.05.040
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).