A comparison study and multi-objective optimization of a DICI engine fueled with petroleum diesel and Fischer-Tropsch diesel

Fischer-Tropsch diesel (FT100) from indirect coal-to-liquid is a promising alternative fuel for diesel engines due to its potential of improved emissions and enhanced engine performances. The combustion and emission characteristics of a direct injection compression ignition (DICI) engine fueled with FT100 and petroleum diesel (PD100) are systematically investigated under different injection timing ( IT ), common-rail pressure ( P cr ) and brake mean effective pressure ( BMEP ). The experimental results show that, with the same engine control parameters, FT100 exhibits better fuel economy, milder combustion and lower soot emission compared to PD100. To achieve higher efficiency and cleaner combustion, further optimizations of control strategy for the FT100 engine are conducted. First, mathematical models for fuel economy, brake specific NOx ( BSNOx ) and soot are constructed by back propagation (BP) neural network. These models are fully verified for reproducibility and generalization ability. Subsequently, non-dominated sorting genetic algorithm (NSGA-II) is applied to realize multi-objective optimization of fuel economy and emissions. The results show that FT100 can operate and achieve multi-objective optimization under a wider range of IT and more decreased P cr compared to PD100. Specifically, better fuel economy, lower BSNOx and soot emissions for FT100 could be achieved when the control parameters are set as follows: IT from 4.5 to 12.5 °CA BTDC, P cr from 90 to 95 MPa, and BMEP from 0.725 to 0.950 MPa. Compared to PD100, the averagely optimal BSFC , BSNOx and K of FT100 could be decreased by 1.6 %, 25.3 % and 59.0 % at 0.88 MPa BMEP . This work provides a more suitable control strategy for DICI engine manufacturers using FT100, which helps to satisfy stricter emissions standards and increasing demand for sustainable fuel usage. • FT100 has better fuel economy, mild combustion and lower emission than PD100. • BP neural network and NSGA-II could be combined to realize engine optimization. • FT100 achieves better fuel economy and Soot-NOx reduction with optimized parameters. • P cr of FT100 should be increased to achieve better optimization at higher BMEP .