Explosion risk-based water spray mitigation analysis of ultra-deep-water semi-submersible platforms

被引:9
|
作者
Shi, Jihao [1 ]
Li, Junjie [1 ]
Khan, Faisal [2 ,3 ]
Chang, Yuanjiang [1 ]
Zhu, Yuan [1 ]
Chen, Guoming [1 ]
机构
[1] China Univ Petr, Ctr Offshore Engn & Safety Technol, Qingdao 266580, Peoples R China
[2] Mem Univ, Fac Engn & Appl Sci, Ctr Risk Integr & Safety Engn C RISE, St John, NF A1B 3X5, Canada
[3] Texas A&M Univ, Mary Kay OConnor Proc Safety Ctr MKOPSC, Artie McFerrin Dept Chem Engn, College Stn, TX 77843 USA
基金
加拿大自然科学与工程研究理事会;
关键词
Semi-submersible platform; Explosion risk analysis; Water spray; Gas explosion mitigation; Global optimization; ARTIFICIAL NEURAL-NETWORK; OPTIMIZATION ALGORITHM; FREQUENCY-DISTRIBUTION; GAS-EXPLOSIONS; DISPERSION; CLOUD; MODEL;
D O I
10.1016/j.oceaneng.2021.109716
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Water spray is an economical and environment-friendly alternative to mitigate gas explosion. This study conducts explosion risk analysis (ERA)-based water spray mitigation analysis of ultra-deep-water semi-submersible platform. 5000 gas explosion simulations with and without water spray are conducted by using FLACS codes. The outcome demonstrates that water spray with droplet size 700 mu m and water application rate (WAR) 26 L/m(2) min is most effective to mitigate explosion scenarios. The droplet size is the significant factor to stimulate the mitigation effect of water spray under middle and small exceedance frequencies, e.g., 1E-05 and 1E-06. WAR becomes dominate to awaken the water spray's negative effect under large exceedance frequency, e.g., 1E-04. A precise and efficient approach of BRANN integrated with artificial bee colony optimization algorithm is used to determine the optimal water spray configuration given the desirable exceedance frequency and the expected design accidental load that the ultra-deep-water semi-submersible platform structures could resist.
引用
收藏
页数:8
相关论文
共 47 条
  • [41] Distribution of norovirus genotypes and subtypes in river water by ultra-deep sequencing-based analysis
    Boonchan, M.
    Motomura, K.
    Inoue, K.
    Ode, H.
    Chu, P. Y.
    Lin, M.
    Iwatani, Y.
    Ruchusatsawat, K.
    Guntapong, R.
    Tacharoenmuang, R.
    Chantaroj, S.
    Tatsumi, M.
    Takeda, N.
    Sangkitporn, S.
    LETTERS IN APPLIED MICROBIOLOGY, 2017, 65 (01) : 98 - 104
  • [42] Towards sustainable water reuse: A critical review and meta-analysis of emerging chemical contaminants with risk-based evaluation, health hazard prediction and prioritization for assessment of effluent water quality
    Khan, Uzair Akbar
    Loffler, Paul
    Spilsbury, Francis
    Wiberg, Karin
    Lundborg, Cecilia Stalsby
    Lai, Foon Yin
    JOURNAL OF HAZARDOUS MATERIALS, 2024, 480
  • [44] RISK ASSESSMENT OF GRANITE RESERVOIR TESTING IN ULTRA -DEEP WATER BASEMENT BURIED-HILL BASED ON AHP-FUZZY COMPREHENSIVE EVALUATION
    Meng, Wenbo
    Jiang, Donglei
    Yu, Yi
    Dou, Liangbin
    Zuo, Xiongdi
    Fang, Yong
    Chen, Jingyang
    Gao, Hui
    FRESENIUS ENVIRONMENTAL BULLETIN, 2022, 31 (08): : 8029 - 8036
  • [45] Evaluating offshore technologies for produced water management using GreenPro-I -: a risk-based life cycle analysis for green and clean process selection and design
    Sadiq, R
    Khan, FI
    Veitch, B
    COMPUTERS & CHEMICAL ENGINEERING, 2005, 29 (05) : 1023 - 1039
  • [46] Field data analysis and risk assessment of shallow gas hazards based on neural networks during industrial deep-water drilling
    Cao, Bohan
    Yin, Qishuai
    Guo, Yingying
    Yang, Jin
    Zhang, Laibin
    Wang, Zhenquan
    Tyagi, Mayank
    Sun, Ting
    Zhou, Xu
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 232