Multiobjective Optimization of Diesel Particulate Filter Regeneration Conditions Based on Machine Learning Combined with Intelligent Algorithms

Table 4

Partial experimental data.

	Speed (r/min)	Torque (Nm)	Preinjection quantity (mg)	Rear injection 1 quantity (mg)	Rear injection 2 quantity (mg)	Rear injection 2 timing (°CA)	Main injection timing (°CA)	Preinjection timing (°CA)	Intake demand (mg/cyc)	T4 (°C)	T5 (°C)
1	1000	20	1.66	7.26	2	−33.19	0.044	11.426	681	308.16	502.86
2	1400	180	1.84	4.10	0.00	0.00	10.50	39.13	1409.0	514.66	600.06
3	1600	360	1.60	3.20	2.50	−33.00	−4.00	8.50	980.0	415.26	488.66
4	1800	20	1.60	8.00	5.00	−29.99	−2.00	11.01	650.0	305.76	598.86
…	…	…
597	2800	45	1.88	3.16	8.76	−40.30	1.69	15.97	775.0	485.06	582.86
598	2600	270	2.10	4.30	3.00	−29.99	9.01	28.02	1440.0	510.66	608.26
599	2400	225	1.84	5.70	2.10	−26.77	10.00	34.30	1520.0	492.06	607.26
600	2800	360	1.86	3.98	5.20	−29.99	6.00	20.81	522.0	406.46	609.26