NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis

Guoyang Wang, Omar I. Awad, Shiyu Liu, Shijin Shuai and Zhiming Wang

Energy, 2020, vol. 198, issue C

Abstract: Information on Nitrogen oxide (NOx) concentrations play a significant role in aftertreatment systems. In this work, a method to estimate NOx emissions by means of mutual information (MI) and back propagation neural network (BPNN) was introduced. All measured signals were ranked by MI value and the most significant parameters were classified according to their physical meanings. The model inputs were selected by analysis of the classified groups. The forecasting model was developed by the BPNN algorithm to predict raw NOx emissions and NO mass flow rate (MFR) before Selective catalytic reduction (SCR) with selected input variables. Ranking, classification, selection, and training were carried out under steady-state conditions and the world harmonized stationary cycle. The verified BPNN network could well predict raw NOx emissions and NO MFR before SCR. Compared to static map prediction, the mean absolute deviation and root mean square error of BPNN are reduced by about 15%, which also indicated that the MI-based feature selection method was effective. The proposed approach is a generic approach for NOx emission prediction, which could also reduce the requirement for expert knowledge on feature selection, has a lower computational cost, and could be used in engine and aftertreatment control system of real driving vehicle.

Keywords: Heavy-duty diesel engine; Nitrogen oxide (NOx) emission estimation; Correlation analysis; Mutual information; Back propagation neural network (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220303935
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:energy:v:198:y:2020:i:c:s0360544220303935

DOI: 10.1016/j.energy.2020.117286

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().