Chemical Industry and Engineering Progress ›› 2022, Vol. 41 ›› Issue (11): 5761-5770.DOI: 10.16085/j.issn.1000-6613.2022-0216
• Energy processes and technology • Previous Articles Next Articles
LI Shuai1(), LIU Mingyan1,2(), MA Yongli1
Received:
2022-02-11
Revised:
2022-03-21
Online:
2022-11-28
Published:
2022-11-25
Contact:
LIU Mingyan
通讯作者:
刘明言
作者简介:
李帅(1995—),男,硕士研究生。E-mail: li568479439@163.com。
基金资助:
CLC Number:
LI Shuai, LIU Mingyan, MA Yongli. Prediction of scaling location of fluid in geothermal well based on BP artificial neural network[J]. Chemical Industry and Engineering Progress, 2022, 41(11): 5761-5770.
李帅, 刘明言, 马永丽. 基于BP人工神经网络预测地热井中流体的结垢位置[J]. 化工进展, 2022, 41(11): 5761-5770.
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URL: https://hgjz.cip.com.cn/EN/10.16085/j.issn.1000-6613.2022-0216
井的开度 | 井深/m | 套管直径/mm | 壁厚/mm |
---|---|---|---|
一开 | 550.00 | 406.0 | 9.65 |
二开 | 1950.00 | 273.0 | 8.94 |
三开 | 3275.00 | 177.8 | 9.19 |
四开 | 3860.00 | 152.0 | — |
井的开度 | 井深/m | 套管直径/mm | 壁厚/mm |
---|---|---|---|
一开 | 550.00 | 406.0 | 9.65 |
二开 | 1950.00 | 273.0 | 8.94 |
三开 | 3275.00 | 177.8 | 9.19 |
四开 | 3860.00 | 152.0 | — |
算法 | 函数 |
---|---|
梯度下降法 | traingd |
有动量的梯度下降法 | traingdm |
自适应lr梯度下降法 | traingda |
自适应lr动量梯度下降法 | traingdx |
弹性梯度下降法 | trainrp |
Fletcher-Reeves共轭梯度法 | traincgf |
Ploak-Ribiere共轭梯度法 | traincgp |
Powell-Beale共轭梯度法 | traincgb |
量化共轭梯度法 | trainscg |
拟牛顿算法 | trainbfg |
一步正割算法 | trainoss |
Levenberg-Marquardt | trainlm |
算法 | 函数 |
---|---|
梯度下降法 | traingd |
有动量的梯度下降法 | traingdm |
自适应lr梯度下降法 | traingda |
自适应lr动量梯度下降法 | traingdx |
弹性梯度下降法 | trainrp |
Fletcher-Reeves共轭梯度法 | traincgf |
Ploak-Ribiere共轭梯度法 | traincgp |
Powell-Beale共轭梯度法 | traincgb |
量化共轭梯度法 | trainscg |
拟牛顿算法 | trainbfg |
一步正割算法 | trainoss |
Levenberg-Marquardt | trainlm |
井底 温度/℃ | 井底压力/MPa | 井口 温度/℃ | 井口压力/MPa | 一开井深度/m | 二开井深度/m | 三开井深度/m | 四开井深度/m |
---|---|---|---|---|---|---|---|
116 | 28 | 95 | 0.186 | 550 | 1950 | 3275 | 3860 |
128 | 34 | 110 | 0.180 | 450 | 2205 | 3165 | 3758 |
井底 温度/℃ | 井底压力/MPa | 井口 温度/℃ | 井口压力/MPa | 一开井深度/m | 二开井深度/m | 三开井深度/m | 四开井深度/m |
---|---|---|---|---|---|---|---|
116 | 28 | 95 | 0.186 | 550 | 1950 | 3275 | 3860 |
128 | 34 | 110 | 0.180 | 450 | 2205 | 3165 | 3758 |
序号 | 预测结果/m | 实际结果/m | 绝对误差 | 相对误差/% |
---|---|---|---|---|
1 | 1.580 | 1.500 | 0.080 | 5.06 |
2 | 33.069 | 30.000 | 3.069 | 9.28 |
序号 | 预测结果/m | 实际结果/m | 绝对误差 | 相对误差/% |
---|---|---|---|---|
1 | 1.580 | 1.500 | 0.080 | 5.06 |
2 | 33.069 | 30.000 | 3.069 | 9.28 |
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