CN108363303B - 基于ar偏好信息的差分进化铝电解多目标优化方法 - Google Patents
基于ar偏好信息的差分进化铝电解多目标优化方法 Download PDFInfo
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- CN108363303B CN108363303B CN201810192924.4A CN201810192924A CN108363303B CN 108363303 B CN108363303 B CN 108363303B CN 201810192924 A CN201810192924 A CN 201810192924A CN 108363303 B CN108363303 B CN 108363303B
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- 229910052782 aluminium Inorganic materials 0.000 title claims abstract description 96
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 title claims abstract description 85
- 238000005868 electrolysis reaction Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 238000004519 manufacturing process Methods 0.000 claims abstract description 41
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 15
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- 230000009467 reduction Effects 0.000 claims abstract description 9
- 150000001875 compounds Chemical class 0.000 claims abstract description 6
- 238000004134 energy conservation Methods 0.000 claims abstract description 6
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- -1 perfluoro compound Chemical class 0.000 claims description 18
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- AZDRQVAHHNSJOQ-UHFFFAOYSA-N alumane Chemical compound [AlH3] AZDRQVAHHNSJOQ-UHFFFAOYSA-N 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 10
- 230000035772 mutation Effects 0.000 claims description 9
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- 238000004364 calculation method Methods 0.000 claims description 4
- 239000003792 electrolyte Substances 0.000 claims description 3
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- 239000005431 greenhouse gas Substances 0.000 abstract description 3
- TXEYQDLBPFQVAA-UHFFFAOYSA-N tetrafluoromethane Chemical compound FC(F)(F)F TXEYQDLBPFQVAA-UHFFFAOYSA-N 0.000 description 5
- 239000004411 aluminium Substances 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005094 computer simulation Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
Description
样本编号 | 1 | 2 | 3 | 4 | …… |
x<sub>1</sub> | 1683 | 1682 | 1686 | 1746 | …… |
x<sub>2</sub> | 624 | 716 | 625 | 743 | …… |
x<sub>3</sub> | 2.52 | 2.52 | 2.51 | 2.46 | …… |
x<sub>4</sub> | 1234 | 1230 | 1234 | 1235 | …… |
x<sub>5</sub> | 18.5 | 16.5 | 17.5 | 20 | …… |
x<sub>6</sub> | 14 | 14 | 15 | 16 | …… |
x<sub>7</sub> | 942 | 938 | 946 | 942 | …… |
y<sub>1</sub> | 94.65 | 94.66 | 94.43 | 93.22 | …… |
y<sub>2</sub> | 3721 | 3720 | 3725 | 3717 | …… |
y<sub>3</sub> | 4.25 | 4.84 | 4.01 | 4.15 | …… |
y<sub>4</sub> | 12354.3 | 12316.4 | 12283.1 | 12747.2 | …… |
目标函数 | 电流效率 | 槽电压 | 全氟化物排放量 | 吨铝能耗 |
迭代次数 | 1000 | 1000 | 1000 | 1000 |
隐含层传递函数 | Tansig | Logsig | Logsig | Tansig |
输出层传递函数 | Purelin | Purline | Purelin | Purelin |
隐含层节点数 | 13 | 12 | 12 | 13 |
y<sub>1</sub> | y<sub>2</sub> | y<sub>3</sub> | y<sub>4</sub> | x<sub>1</sub> | x<sub>2</sub> | x<sub>3</sub> | x<sub>4</sub> | x<sub>5</sub> | x<sub>6</sub> | x<sub>7</sub> |
99.24 | 3635 | 3.65 | 10835.15 | 1649 | 628 | 2.55 | 1210 | 16.5 | 14.5 | 942 |
98.13 | 3682 | 3.58 | 11527.21 | 1653 | 627 | 2.38 | 1200 | 17 | 15 | 924 |
95.37 | 3605 | 3.68 | 10478.52 | 1670 | 617 | 2.47 | 1090 | 17.5 | 15.5 | 935 |
Claims (6)
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CN110084428B (zh) * | 2019-04-26 | 2021-07-02 | 中国水利水电科学研究院 | 基于决策者偏好方案计算的水资源配置方法及系统 |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103903072A (zh) * | 2014-04-17 | 2014-07-02 | 中国矿业大学 | 一种基于决策者偏好的高维多目标集合进化优化方法 |
CN104156584A (zh) * | 2014-08-04 | 2014-11-19 | 中国船舶重工集团公司第七0九研究所 | 多目标优化差分进化算法的传感器目标分配方法及系统 |
CN104778368A (zh) * | 2015-04-20 | 2015-07-15 | 中国人民解放军国防科学技术大学 | 一种针对高维多目标优化问题的Pareto集个体排序方法 |
CN105302973A (zh) * | 2015-11-06 | 2016-02-03 | 重庆科技学院 | 基于moea/d算法的铝电解生产优化方法 |
CN105404926A (zh) * | 2015-11-06 | 2016-03-16 | 重庆科技学院 | 基于bp神经网络与mbfo算法的铝电解生产工艺优化方法 |
CN105631528A (zh) * | 2015-09-22 | 2016-06-01 | 长沙理工大学 | 一种基于nsga-ii和近似动态规划的多目标动态最优潮流求解方法 |
CN105809297A (zh) * | 2016-05-18 | 2016-07-27 | 西南石油大学 | 一种基于多目标差分进化算法的火电厂环境经济调度方法 |
CN106295880A (zh) * | 2016-08-10 | 2017-01-04 | 广东工业大学 | 一种电力系统多目标无功优化的方法及系统 |
CN106529166A (zh) * | 2016-11-04 | 2017-03-22 | 河海大学 | 一种基于maepso算法的区域水资源优化配置方法 |
CN108470237A (zh) * | 2018-02-12 | 2018-08-31 | 浙江工业大学 | 一种基于协同进化的多偏好高维目标优化方法 |
Family Cites Families (1)
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KR100852221B1 (ko) * | 2006-12-08 | 2008-08-13 | 한국전자통신연구원 | 유비쿼터스 컴퓨팅 환경에서의 다목적 최적화 기법을이용한 착용형 컴퓨터 및 그 방법 |
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Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103903072A (zh) * | 2014-04-17 | 2014-07-02 | 中国矿业大学 | 一种基于决策者偏好的高维多目标集合进化优化方法 |
CN104156584A (zh) * | 2014-08-04 | 2014-11-19 | 中国船舶重工集团公司第七0九研究所 | 多目标优化差分进化算法的传感器目标分配方法及系统 |
CN104778368A (zh) * | 2015-04-20 | 2015-07-15 | 中国人民解放军国防科学技术大学 | 一种针对高维多目标优化问题的Pareto集个体排序方法 |
CN105631528A (zh) * | 2015-09-22 | 2016-06-01 | 长沙理工大学 | 一种基于nsga-ii和近似动态规划的多目标动态最优潮流求解方法 |
CN105302973A (zh) * | 2015-11-06 | 2016-02-03 | 重庆科技学院 | 基于moea/d算法的铝电解生产优化方法 |
CN105404926A (zh) * | 2015-11-06 | 2016-03-16 | 重庆科技学院 | 基于bp神经网络与mbfo算法的铝电解生产工艺优化方法 |
CN105809297A (zh) * | 2016-05-18 | 2016-07-27 | 西南石油大学 | 一种基于多目标差分进化算法的火电厂环境经济调度方法 |
CN106295880A (zh) * | 2016-08-10 | 2017-01-04 | 广东工业大学 | 一种电力系统多目标无功优化的方法及系统 |
CN106529166A (zh) * | 2016-11-04 | 2017-03-22 | 河海大学 | 一种基于maepso算法的区域水资源优化配置方法 |
CN108470237A (zh) * | 2018-02-12 | 2018-08-31 | 浙江工业大学 | 一种基于协同进化的多偏好高维目标优化方法 |
Non-Patent Citations (4)
Title |
---|
ar-MOEA: A Novel Preference-Based Dominance Relation for Evolutionary Multiobjective Optimization;Jun Yi;《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》;20191030;第788-802页 * |
Pareto dominance based Multiobjective Cohort Intelligence algorithm;Mukundraj V. Patil;《Information Sciences》;20200529;第69-118页 * |
基于决策者偏好区域的多目标粒子群算法研究;麦雄发 等;《计算机应用研究》;20100415;第1301-1304页 * |
武器装备体系组合规划的高维多目标优化决策;周宇 等;《系统工程理论与实践》;20141125;第2944-2954页 * |
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Application publication date: 20180803 Assignee: Chongqing Qinlang Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2023980050332 Denomination of invention: Differential Evolution Multi objective Optimization Method for Aluminum Electrolysis Based on AR Preference Information Granted publication date: 20200904 License type: Common License Record date: 20231206 |
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Application publication date: 20180803 Assignee: Foshan shangxiaoyun Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980003005 Denomination of invention: Differential Evolution Multi objective Optimization Method for Aluminum Electrolysis Based on AR Preference Information Granted publication date: 20200904 License type: Common License Record date: 20240322 Application publication date: 20180803 Assignee: FOSHAN YAOYE TECHNOLOGY Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980003003 Denomination of invention: Differential Evolution Multi objective Optimization Method for Aluminum Electrolysis Based on AR Preference Information Granted publication date: 20200904 License type: Common License Record date: 20240322 Application publication date: 20180803 Assignee: Foshan helixing Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980003002 Denomination of invention: Differential Evolution Multi objective Optimization Method for Aluminum Electrolysis Based on AR Preference Information Granted publication date: 20200904 License type: Common License Record date: 20240322 |
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Application publication date: 20180803 Assignee: Foshan qianshun Technology Co.,Ltd. Assignor: Chongqing University of Science & Technology Contract record no.: X2024980004523 Denomination of invention: Differential Evolution Multi objective Optimization Method for Aluminum Electrolysis Based on AR Preference Information Granted publication date: 20200904 License type: Common License Record date: 20240419 |