化工学报 ›› 2019, Vol. 70 ›› Issue (12): 4872-4880.DOI: 10.11949/0438-1157.20190299
收稿日期:
2019-04-10
修回日期:
2019-09-18
出版日期:
2019-12-05
发布日期:
2019-12-05
通讯作者:
侯锐钢
作者简介:
王涛(1995—),男,硕士,基金资助:
Tao WANG(),Jun WANG,Diyu ZHAO,Yujian LIU,Ruigang HOU()
Received:
2019-04-10
Revised:
2019-09-18
Online:
2019-12-05
Published:
2019-12-05
Contact:
Ruigang HOU
摘要:
通过腐蚀条件下玻璃纤维增强塑料老化前后宏观、微观形貌及力学性能的变化对复合材料使用寿命的影响因素进行分析,分析表明,腐蚀条件下玻璃纤维增强塑料使用寿命受温度、时间和腐蚀介质浓度三种因素影响。结合玻璃纤维增强塑料的弯曲强度保留率建立结构为3-10-1的三层BP神经网络模型对复合材料使用寿命进行预测。通过预测数据和实测数据的对比及误差分析,并随机抽取6组检验数据进行检测,结果表明,所建立的BP神经网络模型得到的预测值与实测值具有较好的拟合度。
中图分类号:
王涛, 王俊, 赵迪宇, 刘育建, 侯锐钢. 基于BP神经网络的玻璃纤维增强塑料腐蚀条件下的寿命预测[J]. 化工学报, 2019, 70(12): 4872-4880.
Tao WANG, Jun WANG, Diyu ZHAO, Yujian LIU, Ruigang HOU. Life prediction of glass fiber reinforced plastics based on BP neural network under corrosion condition[J]. CIESC Journal, 2019, 70(12): 4872-4880.
图5 不同酸浓度腐蚀条件下玻璃纤维增强塑料弯曲强度保留率随时间、温度变化曲线
Fig.5 Variation curves of flexural strength retention rate of composites with time and temperature under different acid concentration corrosion conditions
No. | Time/d | Temperature/℃ | Acid concentration | Predictive | Actual/% | Normalization | Relative error/% |
---|---|---|---|---|---|---|---|
1 | 0 | 25 | 2 | 1.0852 | 100.00 | 1.0000 | 8.5200 |
2 | 1 | 25 | 2 | 0.9979 | 98.05 | 0.9701 | 2.8633 |
3 | 3 | 25 | 2 | 1.0185 | 95.76 | 0.9352 | 8.9111 |
4 | 7 | 25 | 2 | 0.8758 | 90.60 | 0.8563 | 2.2706 |
5 | 14 | 25 | 2 | 0.8830 | 89.80 | 0.8441 | 4.6071 |
6 | 21 | 25 | 2 | 0.9103 | 90.31 | 0.8520 | 6.8467 |
7 | 90 | 25 | 2 | 0.8744 | 86.33 | 0.7911 | 10.5340 |
8 | 180 | 25 | 2 | 0.6320 | 85.73 | 0.7820 | -19.1824 |
9 | 360 | 25 | 2 | 0.7403 | 85.30 | 0.7753 | -4.5143 |
10 | 0 | 55 | 2 | 0.9764 | 100.00 | 1.0000 | -2.3610 |
11 | 1 | 55 | 2 | 0.9353 | 97.86 | 0.9673 | -3.3083 |
12 | 3 | 55 | 2 | 0.9257 | 95.32 | 0.9285 | -0.2992 |
13 | 7 | 55 | 2 | 0.8226 | 88.94 | 0.8309 | -1.0029 |
14 | 14 | 55 | 2 | 0.8050 | 85.97 | 0.7856 | 2.4751 |
15 | 21 | 55 | 2 | 0.7986 | 84.28 | 0.7597 | 5.1188 |
16 | 60 | 55 | 2 | 0.6584 | 73.83 | 0.6001 | 9.7206 |
17 | 90 | 55 | 2 | 0.7251 | 71.10 | 0.5584 | 29.8466 |
18 | 180 | 55 | 2 | 0.5093 | 68.44 | 0.5177 | -1.6097 |
19 | 360 | 55 | 2 | 0.3993 | 61.96 | 0.4187 | -4.6440 |
20 | 0 | 80 | 2 | 0.9611 | 100.00 | 1.0000 | -3.8889 |
21 | 1 | 80 | 2 | 0.5951 | 84.41 | 0.7618 | -21.879 |
22 | 3 | 80 | 2 | 0.6647 | 78.24 | 0.6674 | -0.4162 |
23 | 7 | 80 | 2 | 0.5673 | 72.23 | 0.5756 | -1.4477 |
24 | 14 | 80 | 2 | 0.4948 | 68.21 | 0.5142 | -3.7814 |
25 | 21 | 80 | 2 | 0.4491 | 66.49 | 0.4880 | -7.9697 |
26 | 30 | 80 | 2 | 0.5171 | 64.58 | 0.4588 | 12.7147 |
27 | 60 | 80 | 2 | 0.4637 | 59.45 | 0.3804 | 8.5200 |
28 | 90 | 80 | 2 | 0.3371 | 55.53 | 0.3205 | 2.8633 |
29 | 180 | 80 | 2 | 0.2072 | 47.87 | 0.2033 | 8.9111 |
30 | 360 | 80 | 2 | 0.0386 | 40.91 | 0.0970 | 2.2706 |
表1 低浓度酸腐蚀条件下神经网络预测结果与实测值
Table 1 Prediction results and measured values of neural networks under low concentration acid corrosion
No. | Time/d | Temperature/℃ | Acid concentration | Predictive | Actual/% | Normalization | Relative error/% |
---|---|---|---|---|---|---|---|
1 | 0 | 25 | 2 | 1.0852 | 100.00 | 1.0000 | 8.5200 |
2 | 1 | 25 | 2 | 0.9979 | 98.05 | 0.9701 | 2.8633 |
3 | 3 | 25 | 2 | 1.0185 | 95.76 | 0.9352 | 8.9111 |
4 | 7 | 25 | 2 | 0.8758 | 90.60 | 0.8563 | 2.2706 |
5 | 14 | 25 | 2 | 0.8830 | 89.80 | 0.8441 | 4.6071 |
6 | 21 | 25 | 2 | 0.9103 | 90.31 | 0.8520 | 6.8467 |
7 | 90 | 25 | 2 | 0.8744 | 86.33 | 0.7911 | 10.5340 |
8 | 180 | 25 | 2 | 0.6320 | 85.73 | 0.7820 | -19.1824 |
9 | 360 | 25 | 2 | 0.7403 | 85.30 | 0.7753 | -4.5143 |
10 | 0 | 55 | 2 | 0.9764 | 100.00 | 1.0000 | -2.3610 |
11 | 1 | 55 | 2 | 0.9353 | 97.86 | 0.9673 | -3.3083 |
12 | 3 | 55 | 2 | 0.9257 | 95.32 | 0.9285 | -0.2992 |
13 | 7 | 55 | 2 | 0.8226 | 88.94 | 0.8309 | -1.0029 |
14 | 14 | 55 | 2 | 0.8050 | 85.97 | 0.7856 | 2.4751 |
15 | 21 | 55 | 2 | 0.7986 | 84.28 | 0.7597 | 5.1188 |
16 | 60 | 55 | 2 | 0.6584 | 73.83 | 0.6001 | 9.7206 |
17 | 90 | 55 | 2 | 0.7251 | 71.10 | 0.5584 | 29.8466 |
18 | 180 | 55 | 2 | 0.5093 | 68.44 | 0.5177 | -1.6097 |
19 | 360 | 55 | 2 | 0.3993 | 61.96 | 0.4187 | -4.6440 |
20 | 0 | 80 | 2 | 0.9611 | 100.00 | 1.0000 | -3.8889 |
21 | 1 | 80 | 2 | 0.5951 | 84.41 | 0.7618 | -21.879 |
22 | 3 | 80 | 2 | 0.6647 | 78.24 | 0.6674 | -0.4162 |
23 | 7 | 80 | 2 | 0.5673 | 72.23 | 0.5756 | -1.4477 |
24 | 14 | 80 | 2 | 0.4948 | 68.21 | 0.5142 | -3.7814 |
25 | 21 | 80 | 2 | 0.4491 | 66.49 | 0.4880 | -7.9697 |
26 | 30 | 80 | 2 | 0.5171 | 64.58 | 0.4588 | 12.7147 |
27 | 60 | 80 | 2 | 0.4637 | 59.45 | 0.3804 | 8.5200 |
28 | 90 | 80 | 2 | 0.3371 | 55.53 | 0.3205 | 2.8633 |
29 | 180 | 80 | 2 | 0.2072 | 47.87 | 0.2033 | 8.9111 |
30 | 360 | 80 | 2 | 0.0386 | 40.91 | 0.0970 | 2.2706 |
No. | Time/d | Temperature/℃ | Acid concentration/( | Predictive | Actual/% | Normalization | Relative error/% |
---|---|---|---|---|---|---|---|
1 | 0 | 25 | 7 | 1.0952 | 100.00 | 1.0000 | 9.5200 |
2 | 1 | 25 | 7 | 0.9116 | 92.40 | 0.8839 | 3.1428 |
3 | 3 | 25 | 7 | 0.9183 | 89.20 | 0.8350 | 9.9804 |
4 | 7 | 25 | 7 | 0.8498 | 88.90 | 0.8304 | 2.3416 |
5 | 14 | 25 | 7 | 0.8030 | 84.56 | 0.7641 | 5.0897 |
6 | 21 | 25 | 7 | 0.7970 | 82.90 | 0.7387 | 7.8969 |
7 | 30 | 25 | 7 | 0.7632 | 79.05 | 0.6799 | 12.2574 |
8 | 90 | 25 | 7 | 0.6997 | 70.53 | 0.5497 | 27.2883 |
9 | 180 | 25 | 7 | 0.4409 | 65.70 | 0.4759 | -7.3552 |
10 | 360 | 25 | 7 | 0.3636 | 59.90 | 0.3872 | -6.0972 |
11 | 0 | 55 | 7 | 0.9680 | 100.00 | 1.0000 | -3.2000 |
12 | 1 | 55 | 7 | 0.8495 | 90.33 | 0.8523 | -0.3259 |
13 | 3 | 55 | 7 | 0.8342 | 89.70 | 0.8426 | -0.9890 |
14 | 7 | 55 | 7 | 0.6828 | 80.51 | 0.7022 | -2.7690 |
15 | 14 | 55 | 7 | 0.5506 | 73.14 | 0.5895 | -6.5970 |
16 | 21 | 55 | 7 | 0.5210 | 72.47 | 0.5793 | -10.070 |
17 | 60 | 55 | 7 | 0.2904 | 64.47 | 0.4571 | -36.461 |
18 | 90 | 55 | 7 | 0.4428 | 64.08 | 0.4511 | -1.8474 |
19 | 180 | 55 | 7 | 0.3361 | 55.28 | 0.3167 | 6.1404 |
20 | 360 | 55 | 7 | 0.2794 | 50.30 | 0.2405 | 16.1678 |
21 | 0 | 80 | 7 | 1.1667 | 100.00 | 1.0000 | 16.6667 |
22 | 1 | 80 | 7 | 0.7461 | 83.20 | 0.7433 | 0.3737 |
23 | 3 | 80 | 7 | 0.7081 | 80.35 | 0.6997 | 1.1909 |
24 | 7 | 80 | 7 | 0.6623 | 76.63 | 0.6429 | 3.0246 |
25 | 14 | 80 | 7 | 0.4315 | 65.34 | 0.4704 | -8.2681 |
26 | 21 | 80 | 7 | 0.4051 | 64.89 | 0.4635 | -12.586 |
27 | 30 | 80 | 7 | 0.3395 | 62.23 | 0.4228 | -19.708 |
28 | 60 | 80 | 7 | 0.3006 | 55.32 | 0.3172 | -5.2538 |
29 | 180 | 80 | 7 | 0.0759 | 39.78 | 0.0798 | -4.8751 |
30 | 360 | 80 | 7 | -0.0583 | 34.56 | 0 | — |
表2 高浓度酸腐蚀条件下神经网络预测结果与实测值
Table 2 Prediction results and measured values of neural networks under the condition of high concentration acid corrosion
No. | Time/d | Temperature/℃ | Acid concentration/( | Predictive | Actual/% | Normalization | Relative error/% |
---|---|---|---|---|---|---|---|
1 | 0 | 25 | 7 | 1.0952 | 100.00 | 1.0000 | 9.5200 |
2 | 1 | 25 | 7 | 0.9116 | 92.40 | 0.8839 | 3.1428 |
3 | 3 | 25 | 7 | 0.9183 | 89.20 | 0.8350 | 9.9804 |
4 | 7 | 25 | 7 | 0.8498 | 88.90 | 0.8304 | 2.3416 |
5 | 14 | 25 | 7 | 0.8030 | 84.56 | 0.7641 | 5.0897 |
6 | 21 | 25 | 7 | 0.7970 | 82.90 | 0.7387 | 7.8969 |
7 | 30 | 25 | 7 | 0.7632 | 79.05 | 0.6799 | 12.2574 |
8 | 90 | 25 | 7 | 0.6997 | 70.53 | 0.5497 | 27.2883 |
9 | 180 | 25 | 7 | 0.4409 | 65.70 | 0.4759 | -7.3552 |
10 | 360 | 25 | 7 | 0.3636 | 59.90 | 0.3872 | -6.0972 |
11 | 0 | 55 | 7 | 0.9680 | 100.00 | 1.0000 | -3.2000 |
12 | 1 | 55 | 7 | 0.8495 | 90.33 | 0.8523 | -0.3259 |
13 | 3 | 55 | 7 | 0.8342 | 89.70 | 0.8426 | -0.9890 |
14 | 7 | 55 | 7 | 0.6828 | 80.51 | 0.7022 | -2.7690 |
15 | 14 | 55 | 7 | 0.5506 | 73.14 | 0.5895 | -6.5970 |
16 | 21 | 55 | 7 | 0.5210 | 72.47 | 0.5793 | -10.070 |
17 | 60 | 55 | 7 | 0.2904 | 64.47 | 0.4571 | -36.461 |
18 | 90 | 55 | 7 | 0.4428 | 64.08 | 0.4511 | -1.8474 |
19 | 180 | 55 | 7 | 0.3361 | 55.28 | 0.3167 | 6.1404 |
20 | 360 | 55 | 7 | 0.2794 | 50.30 | 0.2405 | 16.1678 |
21 | 0 | 80 | 7 | 1.1667 | 100.00 | 1.0000 | 16.6667 |
22 | 1 | 80 | 7 | 0.7461 | 83.20 | 0.7433 | 0.3737 |
23 | 3 | 80 | 7 | 0.7081 | 80.35 | 0.6997 | 1.1909 |
24 | 7 | 80 | 7 | 0.6623 | 76.63 | 0.6429 | 3.0246 |
25 | 14 | 80 | 7 | 0.4315 | 65.34 | 0.4704 | -8.2681 |
26 | 21 | 80 | 7 | 0.4051 | 64.89 | 0.4635 | -12.586 |
27 | 30 | 80 | 7 | 0.3395 | 62.23 | 0.4228 | -19.708 |
28 | 60 | 80 | 7 | 0.3006 | 55.32 | 0.3172 | -5.2538 |
29 | 180 | 80 | 7 | 0.0759 | 39.78 | 0.0798 | -4.8751 |
30 | 360 | 80 | 7 | -0.0583 | 34.56 | 0 | — |
Test | Time/d | Temperature/℃ | Acid concentration/( | Predictive/% | Actual/% | Relative error/% |
---|---|---|---|---|---|---|
1 | 30 | 25 | 2 | 92.32 | 90.26 | 2.28 |
2 | 30 | 55 | 2 | 75.39 | 80.33 | -6.15 |
3 | 90 | 80 | 2 | 60.32 | 55.53 | 8.63 |
4 | 60 | 25 | 7 | 73.23 | 77.43 | -5.42 |
5 | 30 | 55 | 7 | 70.31 | 70.97 | -0.93 |
6 | 90 | 80 | 7 | 38.36 | 45.54 | -15.77 |
表3 检验数据神经网络预测结果与实测值
Table 3 Prediction results and measured values of neural networks for test data
Test | Time/d | Temperature/℃ | Acid concentration/( | Predictive/% | Actual/% | Relative error/% |
---|---|---|---|---|---|---|
1 | 30 | 25 | 2 | 92.32 | 90.26 | 2.28 |
2 | 30 | 55 | 2 | 75.39 | 80.33 | -6.15 |
3 | 90 | 80 | 2 | 60.32 | 55.53 | 8.63 |
4 | 60 | 25 | 7 | 73.23 | 77.43 | -5.42 |
5 | 30 | 55 | 7 | 70.31 | 70.97 | -0.93 |
6 | 90 | 80 | 7 | 38.36 | 45.54 | -15.77 |
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