Artificial neural network modeling of p-cresol photodegradation

BMC Chemistry

Table 1 The independent variables as input, actual and predicated of p -cresol photodegradation as output for training, testing and validation set

Run	Irradiation time (min)	pH	Photocatalyst (g)	P-cresol (ppm)	Photodegradation (%)
					Actual	Predicted
Training Data
1	300	6	2	100	62.479	63.176
2	240	7.5	1.5	125	53.094	53.327
3	180	9	2	100	40.124	39.348
4	300	9	2	100	74.538	74.800
5	120	7.5	1.5	75	48.465	49.289
6	360	7.5	1.5	75	92.229	93.719
7	180	6	1	50	45.636	45.549
8	300	6	1	50	65.062	65.131
9	180	9	1	50	69.848	69.680
10	240	7.5	1.5	25	95.866	96.360
11	180	9	1	100	57.887	57.755
12	300	6	2	50	92.261	91.860
13	180	6	1	100	36.513	37.256
14	300	6	1	100	55.227	54.659
15	180	6	2	50	63.546	63.619
16	180	9	2	50	75.796	76.247
17	240	7.5	0.5	75	70.055	70.197
18	240	7.5	2.5	75	84.811	85.001
19	300	9	1	100	78.207	78.287
20	240	4.5	1.5	75	25.717	25.788
21	240	10.5	1.5	75	55.993	56.588
22	180	6	2	100	35.462	34.380
23	300	9	1	50	85.148	84.930
24	300	9	2	50	96.684	96.131
25	240	7.5	1.5	75	92.555	89.420
Testing Data
26	150	7	1.5	75	58.750	58.462
27	210	8	1.5	50	85.330	91.185
28	240	7.5	1.5	50	98.000	96.208
29	180	7.49	1.5	75	68.802	71.687
30	240	7.5	1	75	92.083	88.658
31	270	8	1.5	75	88.967	90.029
Validation Data
32	180	7	1.5	75	65.778	74.244
33	240	7.5	1.5	50	92.716	97.141
34	180	8	1.5	75	68.218	78.871
35	240	7.49	1	60	81.217	87.489
36	180	7.49	1.5	50	85.233	92.214
37	210	7.49	1.5	75	81.474	87.439
38	240	7.5	1.5	75	88.000	91.143

ISSN: 2661-801X