Optimization of Chain-Reservoirs’ Operation with a New Approach in Artificial Intelligence

被引:0
|
作者
Mohammad Ehteram
Mohammed Falah Allawi
Hojat Karami
Sayed-Farhad Mousavi
Mohammad Emami
Ahmed EL-Shafie
Saeed Farzin
机构
[1] Semnan University,Department of Water Engineering and Hydraulic Structures
[2] Universiti Kebangsaan Malaysia,Civil and Structural Engineering Department, Faculty of Engineering and Built Environment
[3] University of Malaya,Department of Civil Engineering, Faculty of Engineering
来源
关键词
Artificial intelligence; water resources management; Optimization; Cascade reservoirs; Shark algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
Operation of reservoirs and power plants for better management of water resources and production of hydro-electric energy has been the objective of many studies. In this research, shark algorithm is used for management of water resources and hydro-electric plants. After the introduction of this procedure, the algorithm is applied to some complex cases such as Karun-4 reservoir, 4-reservoir system, 10-reservoir system and another one including 26 power plants. In the Karun-4 case, the aim was to reduce water shortages and the results obtained from shark algorithm were in 100% compliance with the absolute optimum answer obtained from Lingo software and non-linear method. This was the best solution to the problem to date in the published researches. In the 4-reservoir system, the objective was to increase the profit from the reservoirs. The shark algorithm yielded a value of 1194.64, which is the best answer to date to the question. In regard to the energy production by the 26 power plants, the shark algorithm yielded 40% more energy, compared to genetic algorithm.
引用
收藏
页码:2085 / 2104
页数:19
相关论文
共 50 条
  • [1] Optimization of Chain-Reservoirs' Operation with a New Approach in Artificial Intelligence
    Ehteram, Mohammad
    Allawi, Mohammed Falah
    Karami, Hojat
    Mousavi, Sayed-Farhad
    Emami, Mohammad
    EL-Shafie, Ahmed
    Farzin, Saeed
    WATER RESOURCES MANAGEMENT, 2017, 31 (07) : 2085 - 2104
  • [2] Optimization of well placement geothermal reservoirs using artificial intelligence
    Akin, Serhat
    Kok, Mustafa V.
    Uraz, Irtek
    COMPUTERS & GEOSCIENCES, 2010, 36 (06) : 776 - 785
  • [3] Artificial intelligence-based response surface progressive optimality algorithm for operation optimization of multiple hydropower reservoirs
    Niu, Wen-Jing
    Luo, Tao
    Yao, Xin-Ru
    Huang, Qing-Qing
    Gao, Hao-Yu
    Feng, Zhong-Kai
    ENERGY, 2024, 291
  • [4] Locomotive optimization using artificial intelligence approach
    Ziarati, K
    Chizari, H
    Nezhad, AM
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY, 2005, 29 (B1): : 93 - 105
  • [5] Application and Prospect of Microgrid Operation Optimization Enabled by Artificial Intelligence
    Sun Y.
    Meng Y.
    Ge L.
    Zhang Y.
    Wang S.
    Wang J.
    Gaodianya Jishu/High Voltage Engineering, 2023, 49 (06): : 2239 - 2252
  • [6] NEC AND NEW APPROACH IN OPTIMIZATION PROCESS OF DECISION-MAKING WITH ARTIFICIAL INTELLIGENCE
    Dufkova, Andrea
    ICMT '07: INTERNATIONAL CONFERENCE ON MILITARY TECHNOLOGIES, 2007, : 426 - 429
  • [7] Artificial intelligence for an energy and resource efficient manufacturing chain design and operation
    Rentsch, Ruediger
    Heinzel, Carsten
    Brinksmeier, E.
    9TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING - CIRP ICME '14, 2015, 33 : 139 - 144
  • [8] Performance Optimization of Industrial Supply Chain Using Artificial Intelligence
    Alomar, Madani Abdu
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [9] Less is more approach in optimization: a road to artificial intelligence
    Nenad Mladenović
    Jun Pei
    Panos M. Pardalos
    Dragan Urošević
    Optimization Letters, 2022, 16 : 409 - 420
  • [10] Less is more approach in optimization: a road to artificial intelligence
    Mladenovic, Nenad
    Pei, Jun
    Pardalos, Panos M.
    Urosevic, Dragan
    OPTIMIZATION LETTERS, 2022, 16 (01) : 409 - 420