Prediction and Comparison of Ash Fusion Temperatures Based on BP Neural Network and Least Squares Support Vector Machine

doi:10.12096/j.2096-4528.pgt.20003

Abstract

Abstract:

To predict the slagging on heating surface of coal-fired boilers, BP neural network (BPNN) and least squares support vector machine (LSSVM) prediction models were established to predict ash fusion temperature, deformation temperature (DT) and softening temperature (ST). The models take ash metal oxide, SO₃ content of ash and slagging evaluation indexes as independent variables, and take DT and ST as dependent variables. Regression analysis and error analysis show that when predicting DT with a large number of samples, the prediction accuracy of the two models is similar, and the confidence of prediction is over 95%. The correlation coefficients are both about 0.92, and the average relative errors are about 3.4%. When predicting ST with less samples, LSSVM model is better with a correlation coefficient of 0.950 52, which is higher than 0.904 26 of BPNN model. The average relative error is 4.98%, and the number of large error points is less than the BPNN model. Therefore, LSSVM model can predict DT and ST of fly ash more accurately.

Key words: BP neural network (BPNN), least squares support vector machine (LSSVM), ash fusion point, ash composition, slagging evaluation index

CLC Number:

TK 223

Hao SHI, Haiping XIAO, Yanpeng LIU. Prediction and Comparison of Ash Fusion Temperatures Based on BP Neural Network and Least Squares Support Vector Machine[J]. Power Generation Technology, 2022, 43(1): 139-146.

Figures/Tables 11

References 16

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序号	DT/℃	ST/℃	w(SiO₂)/%	w(Al₂O₃)/%	w(Fe₂O₃)/%	w(TiO₂)/%	w(CaO)/%	w(MgO)/%	w(Na₂O)/%	w(K₂O)/%	w(SO₃)/%
1	1 250	1 268	49.09	16.59	11.11	0.81	7.58	1.90	0.44	1.40	7.26
2	1 206	1 210	32.65	12.07	9.36	0.49	23.86	3.38	0.84	0.71	13.22
3	1 278	1 314	61.42	16.92	7.52	4.76	0.71	1.24	0.48	0.09	4.68
4	1 472	1 485	48.19	25.80	4.32	1.28	6.90	1.02	0.50	0.81	5.25
5	1 476	1 486	50.51	21.42	4.29	1.33	4.50	0.64	0.50	0.73	4.76
6	1 270	1 320	56.29	23.66	8.86	0.58	5.10	0.96	0.37	1.03	2.40
7	1 457	1 471	52.56	21.51	4.48	1.35	5.14	0.70	0.49	0.78	4.92

序号	DT/℃	ST/℃	w(SiO₂)/%	w(Al₂O₃)/%	w(Fe₂O₃)/%	w(TiO₂)/%	w(CaO)/%	w(MgO)/%	w(Na₂O)/%	w(K₂O)/%	w(SO₃)/%
1	1 250	1 268	49.09	16.59	11.11	0.81	7.58	1.90	0.44	1.40	7.26
2	1 206	1 210	32.65	12.07	9.36	0.49	23.86	3.38	0.84	0.71	13.22
3	1 278	1 314	61.42	16.92	7.52	4.76	0.71	1.24	0.48	0.09	4.68
4	1 472	1 485	48.19	25.80	4.32	1.28	6.90	1.02	0.50	0.81	5.25
5	1 476	1 486	50.51	21.42	4.29	1.33	4.50	0.64	0.50	0.73	4.76
6	1 270	1 320	56.29	23.66	8.86	0.58	5.10	0.96	0.37	1.03	2.40
7	1 457	1 471	52.56	21.51	4.48	1.35	5.14	0.70	0.49	0.78	4.92

序号	DT/℃	w(SiO₂)/%	w(Al₂O₃)/%	w(Fe₂O₃)/%	w(TiO₂)/%	w(CaO)/%	w(MgO)/%	w(Na₂O)/%	w(K₂O)/%	w(SO₃)/%
1	1 310	53.26	33.22	4.04	1.24	3.04	1.90	0.21	0.71	1.10
2	1 290	53.26	32.62	4.48	1.28	3.27	1.68	0.20	0.72	1.52
3	1 173	44.43	14.30	10.00	0.89	17.16	2.03	0.82	1.52	9.56
4	1 179	43.87	16.33	9.41	0.73	15.74	2.10	1.19	1.16	9.44
…	…	…	…	…	…	…	…	…	…	…
88	1 140	38.60	11.40	13.53	0.71	19.15	2.61	0.89	1.00	11.27
89	1 170	49.18	18.60	9.13	0.96	15.57	0.84	0.80	1.24	2.65
90	1 460	54.66	28.65	6.70	0.98	4.16	0.52	0.69	1.86	0.85
91	1 280	56.25	25.02	5.20	1.00	5.04	1.56	0.54	1.74	2.32

序号	DT/℃	w(SiO₂)/%	w(Al₂O₃)/%	w(Fe₂O₃)/%	w(TiO₂)/%	w(CaO)/%	w(MgO)/%	w(Na₂O)/%	w(K₂O)/%	w(SO₃)/%
1	1 310	53.26	33.22	4.04	1.24	3.04	1.90	0.21	0.71	1.10
2	1 290	53.26	32.62	4.48	1.28	3.27	1.68	0.20	0.72	1.52
3	1 173	44.43	14.30	10.00	0.89	17.16	2.03	0.82	1.52	9.56
4	1 179	43.87	16.33	9.41	0.73	15.74	2.10	1.19	1.16	9.44
…	…	…	…	…	…	…	…	…	…	…
88	1 140	38.60	11.40	13.53	0.71	19.15	2.61	0.89	1.00	11.27
89	1 170	49.18	18.60	9.13	0.96	15.57	0.84	0.80	1.24	2.65
90	1 460	54.66	28.65	6.70	0.98	4.16	0.52	0.69	1.86	0.85
91	1 280	56.25	25.02	5.20	1.00	5.04	1.56	0.54	1.74	2.32

序号	DT/℃	碱酸比	硅铝比	沾污指数	硅铝和
1	1 310	0.11	1.60	0.02	86.48
2	1 290	0.12	1.63	0.02	85.88
3	1 173	0.53	3.11	0.43	58.73
4	1 179	0.49	2.69	0.58	60.20
…	…	…	…	…	…
88	1 140	0.73	3.39	1.39	50.00
89	1 170	0.40	2.64	0.82	67.78
90	1 460	0.17	1.91	0.43	83.31
91	1 280	0.17	2.25	0.39	81.27