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To reduce NOx emission of marine engines, an engine parameter optimisation method combining response surface method (RSM), Bayesian neural network and non-dominated sequence genetic algorithm (NSGA-III) is proposed. First, a simulation model of a marine four-stroke engine with an external exhaust gas recirculation (EGR) system was established and validated in AVL-BOOST software. Then, the five parameters of EGR valve opening, ignition advance angle, compression ratio (CR), intake valve opening (IVO) and exhaust valve closing (EVC) are selected as the decision parameters, and the three parameters of engine power, brake specific fuel consumption (BSFC) and NOx emission are taken as the target parameters to be optimised. In Design-Expert software, the response surface model is established by using the experimental design approach of central composite design (CCD), and the impact of decision parameters on target parameters are discussed by using response surface analysis. NSGA-III was used for the multi-objective optimisation of parameters, and the accuracy of NSGA-III was improved by the Bayesian neural network. The optimal solution is selected and verified by the technique for order of preference by similarity to the ideal solution (TOPSIS) approach. The results show that when the EGR valve opening is 47.5%, ignition advance angle is −18.99°CA, CR is 14.995, IVO is 15.27°CA and EVC is 7.68°CA, compared with the standard setting, the optimised power is increased by 4.17%, BSFC is reduced by 4.27%, and NOx emissions are reduced by 12.37%. Therefore, the combination of the Bayesian neural network and NSGA-III multi-objective optimisation can improve engine performance while reducing fuel consumption and NOx emissions.
Qu et al. (Wed,) studied this question.