System uncertainty modelling using Monte Carlo simulation

被引:0
|
作者
Coughlan, L
Basil, M
Cox, P
机构
来源
MEASUREMENT & CONTROL | 2000年 / 33卷 / 03期
关键词
D O I
10.1177/002029400003300304
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The use of system models based on Monte Carlo simulation is demonstrated to allow a petrochemical operating company to focus its limited resources and budget in the areas of greatest sensitivity to gain the biggest benefit. For existing systems, the model help define whether the present operating conditions meet the applicable agreements; and whether or not optimization in areas can be made while still remaining within the terms of the relevant agreements. For new green field developments, the uncertainty can quickly be determined, establishing the limits that can be achieved and hence, the required equipment. The biggest benefit is for new projects with existing facilities, the allocation possibilities can be tested and the best method found.
引用
收藏
页码:78 / 81
页数:4
相关论文
共 50 条
  • [41] Measurement Uncertainty Evaluation in Vickers Hardness Scale Using Law of Propagation of Uncertainty and Monte Carlo Simulation
    Elizabeth, I
    Kumar, R.
    Garg, N.
    Asif, M.
    Manikandan, R. M.
    Girish
    Titus, S. S. K.
    [J]. MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA, 2019, 34 (03): : 317 - 323
  • [42] Measurement Uncertainty Evaluation in Vickers Hardness Scale Using Law of Propagation of Uncertainty and Monte Carlo Simulation
    Indu Elizabeth
    Rajesh Kumar
    Naveen Garg
    Mohammed Asif
    R. M. Manikandan
    S. S. K. Girish
    [J]. MAPAN, 2019, 34 : 317 - 323
  • [43] Modelling of acoustic agglomeration processes using the direct simulation Monte Carlo method
    Sheng, CD
    Shen, XL
    [J]. JOURNAL OF AEROSOL SCIENCE, 2006, 37 (01) : 16 - 36
  • [44] Modelling and simulation of complex measurement settings using the Monte-Carlo method
    Wolf, Macro
    Mueller, Martin
    Roesslein, Matthias
    [J]. TM-TECHNISCHES MESSEN, 2007, 74 (10) : 485 - 493
  • [45] An adaptive longterm electricity price forecasting modelling using Monte Carlo simulation
    Poullikkas, Andreas
    [J]. JOURNAL OF POWER TECHNOLOGIES, 2018, 98 (03): : 267 - 273
  • [46] Credit Risk Modelling using Hardware Accelerated Monte-Carlo Simulation
    Thomas, David B.
    Luk, Wayne
    [J]. PROCEEDINGS OF THE SIXTEENTH IEEE SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES, 2008, : 229 - 238
  • [47] Modelling gas adsorption in slit-pores using Monte Carlo simulation
    Sweatman, MB
    Quirke, N
    [J]. MOLECULAR SIMULATION, 2001, 27 (5-6) : 295 - 321
  • [48] Characterizing the ExacTrac Image Guidance System Using Monte Carlo Modelling
    Spurway, Alan
    Darvish-Molla, Sahar
    Sattarivand, Mike
    [J]. MEDICAL PHYSICS, 2019, 46 (11) : 5377 - 5377
  • [49] Monte Carlo simulation towards ripple phase modelling
    Kubica, K
    [J]. COMPUTERS & CHEMISTRY, 2001, 25 (03): : 245 - 250
  • [50] Monte Carlo modelling of Schottky diode for rectenna simulation
    Bernuchon, E.
    Aniel, F.
    Zerounian, N.
    Grimault-Jacquin, A. S.
    [J]. SOLID-STATE ELECTRONICS, 2017, 135 : 71 - 77