Title: Prediction modelling of exhaust characteristics of a marine engine for SCR urea dosing calibration

Authors: Zhuo Zhang; Mingwei Shi; Zibin Yin; Defeng Wu; Leyang Dai

Addresses: School of Marine Engineering, Jimei University, Xiamen, Fujian, China; Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering, Xiamen, China ' School of Marine Engineering, Jimei University, Xiamen, Fujian, China; Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering, Xiamen, China ' School of Marine Engineering, Jimei University, Xiamen, Fujian, China; Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering, Xiamen, China ' School of Marine Engineering, Jimei University, Xiamen, Fujian, China; Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering, Xiamen, China ' School of Marine Engineering, Jimei University, Xiamen, Fujian, China; Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering, Xiamen, China

Abstract: The International Maritime Organisation (IMO) issued Annex VI of the MARPOL Convention to control the serious exhaust pollution of marine diesel engines, specifying the NOx emission limitation requirements. In this paper, the exhaust characteristics of a marine diesel engine were tested, and the exhaust characteristic model was established by a BP neural network, which has been verified via learning ability and generalisation ability. The relative errors of the exhaust flow, NOx concentration and exhaust temperature prediction are within 6%, which can be used to predict the exhaust performance of a marine diesel engine in steady state. The calibration for urea dosing of an SCR system was based on an ammonia-nitrogen ratio of 1:1, whose data are predicted by the exhaust characteristic model.

Keywords: marine engine; exhaust characteristics; BP neural network; modelling; SCR system.

DOI: 10.1504/IJCAT.2020.104687

International Journal of Computer Applications in Technology, 2020 Vol.62 No.2, pp.116 - 128

Received: 14 Dec 2018
Accepted: 21 Apr 2019
Published online: 28 Jan 2020 *

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