Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine | Science and Technology for Energy Transition (STET)

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Table 9

Difference of test, ANN and RSM findings for CO and HC emission.

Trial No.	Input			Responses
	Input			CO (%)					HC (ppm)
	Engine speed	Acetone ratio	Ignition advance	Test	ANN	RSM	% Error/ANN	% Error/RSM	Test	ANN	RSM	% Error/ANN	% Error/RSM
1	1200	0	10	2.859	2.861	2.864	0.074	0.175	227	227.58	233.76	0.256	2.978
2	1800	0	10	0.370	0.383	0.380	3.489	2.703	114	118.68	103.86	4.104	8.895
3	1200	0	14	2.640	2.812	2.760	6.516	4.545	211	214.88	203.36	1.840	3.621
4	1500	0	14	1.774	1.789	1.616	0.818	8.918	174	180.13	146.54	3.521	15.782
5	1800	0	14	0.415	0.397	0.483	4.290	16.280	166	114.36	143.34	31.110	13.651
6	1500	0	18	1.758	1.752	1.598	0.318	9.090	168	166.50	166.96	0.892	0.619
7	1800	0	18	0.450	0.439	0.533	2.388	18.400	127	116.67	103.70	8.134	18.346
8	1500	5	10	1.066	1.089	1.236	2.197	15.971	156	145.89	161.38	6.479	3.446
9	1800	5	10	0.361	0.371	0.372	2.659	2.978	133	122.92	118.50	7.581	10.902
10	1200	5	14	2.186	2.183	2.083	0.140	4.732	185.6	188.85	168.85	1.749	9.025
11	1500	5	14	1.183	1.091	1.189	7.745	0.495	142	121.72	132.30	14.284	6.835
12	1200	5	18	1.631	1.677	1.872	2.823	14.773	162	183.04	190.71	12.986	17.722
13	1500	5	18	1.161	1.118	1.103	3.674	4.991	118	112.92	124.10	4.308	5.165
14	1800	5	18	0.446	0.381	0.388	14.516	12.971	122	126.93	101.10	4.044	17.131
15	1200	10	10	1.847	1.801	1.852	2.507	0.271	154	158.05	169.18	2.629	9.857
16	1800	10	10	0.397	0.444	0.370	11.726	6.801	115	98.85	120.34	14.040	4.643
17	1200	10	14	1.780	1.818	1.612	2.135	9.461	149	142.07	141.54	4.649	5.007
18	1500	10	14	0.675	0.988	0.968	46.409	43.467	130	120.06	125.25	7.646	3.654
19	1200	10	18	1.346	1.380	1.333	2.550	0.981	186	181.05	164.78	2.662	11.409
20	1800	10	18	0.354	0.575	0.350	62.515	1.130	105	109.34	115.70	4.129	10.190

Overall error (%)							8.974	8.957				6.852	8.944

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