CN105404926A - 基于bp神经网络与mbfo算法的铝电解生产工艺优化方法 - Google Patents
基于bp神经网络与mbfo算法的铝电解生产工艺优化方法 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 36
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- 239000004411 aluminium Substances 0.000 claims description 59
- 241000894006 Bacteria Species 0.000 claims description 52
- 210000002569 neuron Anatomy 0.000 claims description 29
- 238000005868 electrolysis reaction Methods 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 16
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- AZDRQVAHHNSJOQ-UHFFFAOYSA-N alumane Chemical compound [AlH3] AZDRQVAHHNSJOQ-UHFFFAOYSA-N 0.000 claims description 5
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Abstract
Description
样本编号 | 1 | 2 | 3 | 4 | … |
x1 | 1681 | 1681 | 1681 | 1746 | … |
x2 | 628 | 715 | 625 | 727 | … |
x3 | 2.50 | 2.52 | 2.51 | 2.45 | … |
x4 | 1230 | 1230 | 1240 | 1240 | … |
x5 | 18 | 16.5 | 17.5 | 21 | … |
x6 | 14 | 15 | 15 | 17 | … |
x7 | 943 | 939 | 947 | 943 | … |
x8 | 3710 | 3720 | 3710 | 3723 | … |
y | 94.66 | 94.66 | 95.43 | 91.52 | … |
z | 12364.8 | 12396.3 | 12273.1 | 12797.1 | … |
w | 4.21 | 4.87 | 4.03 | 4.15 | … |
目标函数 | 电流效率 | 吨铝能耗 | 全氟化物排放量 |
迭代次数 | 800 | 800 | 800 |
隐含层传递函数 | Tansig | Logsig | Tansig |
输出层传递函数 | Purelin | Purelin | Purelin |
隐含层节点数 | 13 | 12 | 13 |
Claims (4)
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Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107511823A (zh) * | 2017-08-29 | 2017-12-26 | 重庆科技学院 | 机器人作业轨迹优化分析的方法 |
CN108363303A (zh) * | 2018-03-09 | 2018-08-03 | 重庆科技学院 | 基于ar偏好信息的差分进化铝电解多目标优化方法 |
CN108984813A (zh) * | 2018-03-09 | 2018-12-11 | 重庆科技学院 | 基于递归神经网络与角度偏好的铝电解建模与优化方法 |
CN109086469A (zh) * | 2018-03-09 | 2018-12-25 | 重庆科技学院 | 基于递归神经网络与偏好信息的铝电解建模与优化方法 |
CN109338414A (zh) * | 2018-10-30 | 2019-02-15 | 中国神华能源股份有限公司 | 酸法氧化铝电解过程中氟化铝添加量寻优方法及电子设备 |
CN109843460A (zh) * | 2016-09-30 | 2019-06-04 | 株式会社Uacj | 铝制品的特性预测装置、铝制品的特性预测方法、控制程序、以及记录介质 |
CN109949873A (zh) * | 2019-03-29 | 2019-06-28 | 中南大学 | 铝电解全流程氟物质流计算方法 |
CN111058061A (zh) * | 2019-10-28 | 2020-04-24 | 上海大学 | 一种提高工业铝电解生产电流效率的方法 |
CN112182946A (zh) * | 2020-09-21 | 2021-01-05 | 四川大学 | 基于geomen-pfnn的铝电解能耗模型构建方法 |
CN112239873A (zh) * | 2019-07-19 | 2021-01-19 | 郑州轻冶科技股份有限公司 | 一种铝电解工艺参数优化方法以及铝电解槽组 |
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Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109843460A (zh) * | 2016-09-30 | 2019-06-04 | 株式会社Uacj | 铝制品的特性预测装置、铝制品的特性预测方法、控制程序、以及记录介质 |
CN107511823A (zh) * | 2017-08-29 | 2017-12-26 | 重庆科技学院 | 机器人作业轨迹优化分析的方法 |
CN107511823B (zh) * | 2017-08-29 | 2019-09-27 | 重庆科技学院 | 机器人作业轨迹优化分析的方法 |
CN108363303B (zh) * | 2018-03-09 | 2020-09-04 | 重庆科技学院 | 基于ar偏好信息的差分进化铝电解多目标优化方法 |
CN109086469A (zh) * | 2018-03-09 | 2018-12-25 | 重庆科技学院 | 基于递归神经网络与偏好信息的铝电解建模与优化方法 |
CN108984813A (zh) * | 2018-03-09 | 2018-12-11 | 重庆科技学院 | 基于递归神经网络与角度偏好的铝电解建模与优化方法 |
CN108363303A (zh) * | 2018-03-09 | 2018-08-03 | 重庆科技学院 | 基于ar偏好信息的差分进化铝电解多目标优化方法 |
CN109086469B (zh) * | 2018-03-09 | 2022-11-11 | 重庆科技学院 | 基于递归神经网络与偏好信息的铝电解建模与优化方法 |
CN109338414A (zh) * | 2018-10-30 | 2019-02-15 | 中国神华能源股份有限公司 | 酸法氧化铝电解过程中氟化铝添加量寻优方法及电子设备 |
CN109949873A (zh) * | 2019-03-29 | 2019-06-28 | 中南大学 | 铝电解全流程氟物质流计算方法 |
CN109949873B (zh) * | 2019-03-29 | 2023-03-31 | 中南大学 | 铝电解全流程氟物质流计算方法 |
CN112239873A (zh) * | 2019-07-19 | 2021-01-19 | 郑州轻冶科技股份有限公司 | 一种铝电解工艺参数优化方法以及铝电解槽组 |
CN111058061A (zh) * | 2019-10-28 | 2020-04-24 | 上海大学 | 一种提高工业铝电解生产电流效率的方法 |
CN112182946A (zh) * | 2020-09-21 | 2021-01-05 | 四川大学 | 基于geomen-pfnn的铝电解能耗模型构建方法 |
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Application publication date: 20160316 Assignee: Guangzhou nuobi Electronic Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980052372 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20231220 Application publication date: 20160316 Assignee: Lingteng (Guangzhou) Electronic Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980052367 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20231220 Application publication date: 20160316 Assignee: Guangzhou Taipu Intelligent Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980052361 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20231220 Application publication date: 20160316 Assignee: GUANGZHOU GUOCHUANG TECHNOLOGY Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980052357 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20231220 Application publication date: 20160316 Assignee: GUANGZHOU YIJUN TECHNOLOGY Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980052341 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20231220 Application publication date: 20160316 Assignee: GUANGZHOU XINGYIN TECHNOLOGY Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980052337 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20231220 |
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Application publication date: 20160316 Assignee: Foshan WanChen Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980004249 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20240415 Application publication date: 20160316 Assignee: FOSHAN ZHENGRONG TECHNOLOGY Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980004248 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20240415 Application publication date: 20160316 Assignee: FOSHAN DOUQI TECHNOLOGY Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980004247 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20240415 |
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Application publication date: 20160316 Assignee: Foshan helixing Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980004524 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20240419 Application publication date: 20160316 Assignee: Foshan qianshun Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980004523 Denomination of invention: Optimization method for aluminum electrolysis production process based on BP neural network and MBFO algorithm Granted publication date: 20171205 License type: Common License Record date: 20240419 |