GB2500562A - System Of Monitoring And Controlling Fluid Processing Networks - Google Patents
System Of Monitoring And Controlling Fluid Processing Networks Download PDFInfo
- Publication number
- GB2500562A GB2500562A GB1200580.7A GB201200580A GB2500562A GB 2500562 A GB2500562 A GB 2500562A GB 201200580 A GB201200580 A GB 201200580A GB 2500562 A GB2500562 A GB 2500562A
- Authority
- GB
- United Kingdom
- Prior art keywords
- network
- parameter values
- values
- boundaries
- risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 239000012530 fluid Substances 0.000 title claims abstract description 145
- 238000012545 processing Methods 0.000 title claims abstract description 80
- 238000012544 monitoring process Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 186
- 230000009471 action Effects 0.000 claims abstract description 22
- 238000004519 manufacturing process Methods 0.000 claims abstract description 10
- 238000003860 storage Methods 0.000 claims description 25
- 230000008859 change Effects 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 17
- 208000010392 Bone Fractures Diseases 0.000 claims description 13
- 206010017076 Fracture Diseases 0.000 claims description 13
- 239000007788 liquid Substances 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000004880 explosion Methods 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 7
- 208000037656 Respiratory Sounds Diseases 0.000 claims description 4
- 206010037833 rales Diseases 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 3
- 239000013626 chemical specie Substances 0.000 claims description 2
- 230000000246 remedial effect Effects 0.000 abstract description 3
- 239000000126 substance Substances 0.000 abstract description 2
- 239000004571 lime Substances 0.000 description 16
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 14
- 235000011941 Tilia x europaea Nutrition 0.000 description 14
- 240000006909 Tilia x europaea Species 0.000 description 10
- 239000003921 oil Substances 0.000 description 8
- 238000005259 measurement Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 239000000463 material Substances 0.000 description 5
- 240000007313 Tilia cordata Species 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 4
- 230000015654 memory Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 239000007787 solid Substances 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 239000010454 slate Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 101100238304 Mus musculus Morc1 gene Proteins 0.000 description 2
- 230000029936 alkylation Effects 0.000 description 2
- 238000005804 alkylation reaction Methods 0.000 description 2
- 238000004523 catalytic cracking Methods 0.000 description 2
- 239000003795 chemical substances by application Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 150000004677 hydrates Chemical class 0.000 description 2
- 238000005984 hydrogenation reaction Methods 0.000 description 2
- 238000011282 treatment Methods 0.000 description 2
- 241001421757 Arcas Species 0.000 description 1
- 229910000975 Carbon steel Inorganic materials 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 241000283986 Lepus Species 0.000 description 1
- 101100444339 Neosartorya fumigata (strain ATCC MYA-4609 / Af293 / CBS 101355 / FGSC A1100) easL gene Proteins 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 201000003639 autosomal recessive cerebellar ataxia Diseases 0.000 description 1
- 239000010962 carbon steel Substances 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 239000007795 chemical reaction product Substances 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000004508 fractional distillation Methods 0.000 description 1
- JILPJDVXYVTZDQ-UHFFFAOYSA-N lithium methoxide Chemical compound [Li+].[O-]C JILPJDVXYVTZDQ-UHFFFAOYSA-N 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000005293 physical law Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D1/00—Pipe-line systems
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
- General Factory Administration (AREA)
Abstract
A method of monitoring a fluid processing network, such as in an oil production facility, an oil refinery or a chemical plant, particularly a flare network comprises a plurality of fluid processing regions, the method comprises steps of: receiving measured current parameter values at known points of the network; determining from the measured current parameter values regions of the network that are active, all other regions being deemed inactive; subtracting inactive regions of the network from a model of the fluid processing network to provide a current active network model; determining current parameter values of the current active network at least at points remote from the known points, the parameter values at said remote points being determined using the measured current parameter values and the current active network model; based on the current parameter values, such as pressure, flow rate or temperature, determining if one or more pre-specified boundaries are breached: and performing a predetermined action if one or more said boundaries are breached, such as issuing an alert which may be a message indicating a risk associated with breaching this boundary; severity of the risk; and recommendations for remedial action.
Description
System for Fluid Processing Networks
Field of the Invention
The present invention relates to systems for monitoring and I or control of fluid processing networks.
Background of the Invention
Nctworks for proccssing of fluids, such as in an oil production facility, an oil rcfincry or a chemical plant, arc made up of a large number of component parts including, without limitation, a large number of valves, pipc scgments and fluid chambers, with fluid flowing into and through thc components of thc network. Such processing networks may be provided with safety features, for example fluid release networks such as flare networks, aiming to maintain prcssure within thc network or within a part of thc fluid processing network at or below a safety limit.
A fluid release network may be a sub-network of a fluid processing network and is also made up of a large number of component pails including, without fimitation, a large number of pipe segmenis, valves and fluid chambers, with fluid flowing into and through the components of the network for the primary purpose of removing such fluid from the processing neiwork lo a safe place, including without limitalion venling or flaring to atmosphere.
The fluid processing network maybe provided with sensors including, without limitation, fluid pressure sensors, fluid temperature sensors and metal wall temperature sensors.
However these sensors can only measure property values at the specific point where the sensors are located, leaving areas of unknown pressure and! or temperature between the measurement points.
Summary of the Invention
A mdhod of monitoring a Iluid processing neiwork comprising a pluralily of Iluid processing regions, ihe method comprising the steps of:
I
receiving measured curreni parameter values al known points of the neiwork: determining from thc measured current parameter values regions of the network that are active, all other regions being deemed inactive: subtracting inactive regions of the network from a model of the fluid processing neiwork to provide a eurreni active neiwork model: determining eurren parameter values of the current active network at 1eas a points remote from the known points, the parameter values al said remote points being determined using the measured current parameter values and the current aclive network model; based on the current parameter values, determining if one or more pre-specificd boundaries arc breached; and performing a predetermined action if one or more said boundaries are breached.
2. A method according to aspect I wherein the current active network model is periodically updated using periodically received updates of the measured current parameter values.
3. A method according to aspect 2 wherein inactive regions that have become active, or active regions that have become inactive, are determined from the updates of the measured current parameter values.
4. A method according lo any preceding aspeci wherein the measured curreni parameler values, the delermined current parameter values and the pre-specified parameler boundaries are selected from fluid pressure, fluid temperature, pipe and I or vessel wall lemperalure, fluid flow rale, and level of liquid in a vessel.
5. A method according to any preceding aspect wherein the parameter boundaries include predetermined constant boundaries.
6. A melhod according lo any preceding aspeci wherein the parameter boundaries include variable boundaries or mathematical constraints derived from the values of one or more paramelers.
7. A method according to any preceding aspect wherein at least one predetermined risk is associated with a given parameter heing outside a parameter boundary.
8. A method according to aspect 7 wherein predetermined risks include a pipe fracture risk, a pipe blockage risk and an explosion risk.
9. A method according to any preceding aspect wherein the predetermined action is selected from one or more of: issuing a notification to a network operator and issuing an instruction to an automated network control system.
10. A method according to aspect 9 wherein the notification includes identification of a risk associated with said current parameter values breaching one or more boundaries.
1. A method according to any preceding aspect wherein current parameter values are determined for known points of the current active network for which measured parameter values are received.
I 2. A method according to aspect I 1 wherein measured current parameter values for known points are replaced with the corresponding determined current parameter values for the same points in a manner that is consistent with the model of the fluid processing network.
I 3. A method according to any preceding aspect wherein the current active network model includes values of settings of the network based on received values of settings of the network.
14. A method according to aspect 14 wherein the values of settings of the network include values oF valve settings of the network.
15. A method according to any preceding aspect wherein one or more of the pre-specilied boundaries apply to respective one or more pre-specilied poinis of the neiw ork.
16. A method according to any preceding aspect wherein the network is a flare network.
17. Computer program code which when run on a computer causes the computer to perform the method according to any preceding aspecL 18. A carrier medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of aspects 1-16.
19. A computer program product comprising computer readable code according to aspect iS.
20. An article of manufacture comprising: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium thai when executed by a programmahk system causes the system to perform the method according to any of aspects I -16.
21. A method of monitoring a fluid processing network formed from a plurality of fluid processing components, each fluid processing component being associated with one or more predetermined component models, the method comprising: receiving measured current parameter values at known points of the network: generating a modd of the fluid processing network from a selection of the predetermined component models; determining current parameter values of the network at least at points remote from the known points, the parameter values at said remote points being determined using the measured current parameter values and the network model: delermining if a curreni parameler value breaches one or more predetermined boundaries; and performing a predetermined action if one or more of said boundaries are breached, wherein one or more of the fluid processing components of the neiwork are each associated with two or more predetermined componeni models; and wherein for each component that is associated with two or more predetermined component models, one of the said two or more predetermined component models is seleeled for use in generating the model of the Iluid processing ndwork depending on conditions of the fluid processing network.
22. A method according to aspect 21 whcrein the conditions of the fluid processing network determining the component model for a particular component used in generating the model of the fluid processing network arc selected from: one or more current parameter values of the network; one or more anticipated future parameter values of the network; and the presence of one or more chemical species in the fluid processing network.
23. A method according lo any of aspects 19-22 wherein one or more components of the network are associated with at least one component model that is used in generating a model for all conditions of the fluid processing network.
24. A method according to any of aspects 19-23 wherein the model is modified in response to a change in a condition of the fluid processing network that causes a change in the selection of component model used in generating the model.
25. A method according to any of aspects 19-24 wherein the measured current parameler values, lhe delermined current parameter values and the predetermined parameter boundaries are selected from fluid pressure, fluid temperature, pipe and I or vessel wall lemperalure, fluid flow rale, and level ol liquid in a vessel.
26. A melhod according lo any of aspects 1 9-25 wherein the parameter boundaries include predetermined conslani boundaries.
27. A method according to any of aspects 1 9-26 wherein the parameter boundaries include variable boundaries or mathematical constraints derived from the values of one or more parameters.
28. A method according lo any of aspects 19-27 wherein one or more of the predelermined boundaries apply lo respeelive one or more pre-specified poinis of the neiworic 29. A method according o any of aspects 19-28 wherein a least one predelermined risk is associated with a given parameter breaching a parameter boundary.
30. A method according to aspect 29 wherein predetermined risks include a pipe fracture risk, a pipe blockage risk and ui explosion risk.
31. A method according to any of aspects 19-30 wherein the predetermined action is selected from one or more of: issuing a notification to a network operator and issuing an instruelion lo an aulomaled neiwork conirol syslem.
32. A method according to aspect 31 wherein the notification indudes identification of a risk associated with said current parameter values breaching one or more boundaries.
33. A method according to any of aspects 1 9-32 wherein current parameter values are determined for known points of the network for which measured parameter values are received.
34. A meihod according lo aspect 33 wherein measured current parameler values for know-n poinis are replaced with the corresponding delermined current parameter values br the same poinis in a manner Ihal is consistent wiLh Ihe model of Ihe fluid processing neiwork.
35. A method according to any of aspects 1 9-34 wherein the neiwork model includes values of settings of the network based on received values of sellings of the netw ork.
36. A method according to aspect 35 wherein the values of settings of the network include values of valve settings of the network.
37. A method according 10 any of aspects 19-36wherein the plurally of fluid processing components include pipe segmenls, valves and fluid chambers.
38. A method according to any of aspects 19-37 wherein the network is a flare network.
39. Computer program code which when run on a computer causes the computer to perform the method according to any of aspects 19-38.
40. A carrier medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of aspects 19-39.
41. A computer program product comprising computer readable code according to aspect 40.
42. An article of manufacture comprising: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium that when executed by a programmable system causes the system to perform the method according to any of aspects I 9-4!.
43. A method of monitoring a fluid processing network, the method comprising the sleps of: receiving measured current parameter values at known points of the network: determining current parameter values at least at points remote from the known points, the parameter values at said remote points being determined by application of Ihe measured parameter values Lu a model of Ihe neiwork: selecting one or more risks to be analysed from a group of predetermined risks, each predetermined risk heing associated with one or more points of the network, for each selected risk, determining if a risk at the one or more associated points of the network exceeds a predetermined acceptable risk limit for thai risk at one or more of those points; and performing a predetermined action if a risk exceeds a predetermined acceptable risk limit, wherein a risk is selected for analysis if a current parameter at the one or more points associated with the risk meets a predetermined risk selection requirement.
44. A method according to aspect 43 wherein one or more of the group of predetermined risks is not selected for analysis.
45. A method according to aspect 43 or 44 wherein each predetermined risk defines at least one parameter boundary that should not he breached.
46. A method according to any of aspects 43-45 wherein the measured current parameter values, the boundaries and the determined current parameter values and the predetermined parameter houndaries arc selected from fluid pressure, fluid temperature, pipe and I or vessel wall temperature, fluid flow rate, and level of liquid in a vessel.
47. A method according to any of aspects 43-46 wherein the parameter houndaries include variable houndaries or maihematical constraints derived from Ihe values of one or more parameters.
S
48. A melhod according lo any of aspects 43-47 wherein the predetermined action is selecied Irom one or more of: issuing a nolilicalion lo a network operalor and issuing an instruction lo an aulomaled neiwork conirol syslem.
49. A method according to aspect 48 wherein the notification includes identification of the risk limit that has been exceeded.
50. A method according lo any of aspect 43-49 wherein predelermined risks include a pipe fracture risk, a pipe blockage risk and an explosion risk.
51. A method according to any of aspects 43-50 wherein current parameter values are delermined for known points of the current active ndwork for which measured parameter values are received.
52. A method according to aspect 51 wherein measured current parameter values for known points arc replaced with the corresponding determined current parameter values for the same points in a manner that is consistent with the model of the fluid processing network.
53. A method according lo any of aspects 43-52 wherein the current network model includes values of settings of the network hased on received values of settings of Ihe neiworic 54. A method according to aspect 53 wherein the values of settings of the network include values of valve settings of the network.
55. A method according to any of aspects 43-54 wherein the network is a flare network.
56. Computer program code which when run Ofl a compuLer causes the computer to perlorm the method according to any of aspects 43-55.
57. A carrier medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of aspects 43-55.
58. A compuler program product comprising computer readable code according Lo aspeci 57.
59. An article of manufacture comprising: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium that when executed by a programmable system causes the system to perform the meihod according to any of aspects 43-55.
60. A method of monitoring a fluid processing neiwork, the method comprising the sleps of: receiving measured currenL parameter values aL known points of (he neLwork: receiving values of scttings of the network; determining current parameter values at least at points remote from the known points, the parameter values at said remote points bcing determined by application of the measured parameter values to a model of thc network; predicting parameter values at a future point in time if a predetermined change is made to the settings of the network, or if no change is made to the settings of the network; and performing a predetermined action if the predetermined change, or if no change, is determined to have the effect of causing one or more of the predicted parameter values to breach a predetermined parameter boundary.
61. A method according lo aspect 59 wherein the values of settings of the neiwork comprise values of valve seltings of the network.
62. A method according to aspect 60 or 61 wherein the measured current parameter values, the determined current parameter values and the predetermined parameter boundaries are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and level of liquid in a vesseL 63-A melhod according lo any of aspects 60-62 wherein the parameter boundaries include predetermined conslani boundaries.
64. A method according to any of aspects 60-63 wherein the parameter boundaries include variable boundaries or mathematical constraints derived from the values of one or more parameters.
65. A method according lo any of aspects 60-64 wherein al least one predelermined risk is associaled wilh a given parameter being outside a parameler boundary.
66. A method according to aspect 65 wherein predetermined risks include a pipe fracwre risk, a pipe blockage risk and an explosion risk.
67. A method according to any of aspects 60-66 wherein the predetermined action is selected from one or more of: issuing a notification to a network operator and issuing an instruction to an automated network control system.
6K A method according to aspect 67 wherein the notification includes identification of a risk associated with said current parameter values breaching one or more boundaries.
69. A method according to any of aspects 60-6S wherein current parameter values arc determined for known points of the current network for which measured parameter values arc received.
70. A method according to aspect 69 wherein measured current parameter values for known points are replaced with the corresponding determined current parameter values for the same points in a manner that is consistent with the model of the fluid processing network.
71. A melhod according lo any of aspects 60-70 wherein the current active neiwork model includes va'ues of seltings of ihe network based on received values of seltings of Ihe neiwork.
72. A melhod according lo any of aspects 60-71 wherein the values ol seLlings 0! the neiwork include values oF valve setLings oF the network.
73. A method according to any of aspects 60-72 wherein one or more of the pre-specified boundarics apply to rcspcctive onc or more pre-spccified points of the network.
74. A method according lo any of aspects 60-73 wherein the neiwork is a flare neiwork.
75. COUt program code which when run on a computer causes the computer to perform the method according to any of aspects 60-74.
76. A carrier medium calTying computcr readable code which when run on a computer causcs thc computer to perform the method according to any of aspccts 60-75.
77. A computer program product comprising computcr rcadablc code according to aspect 77.
78. An article of manufacture comprising: a machine-readable storage medium; and executable program instructions embodied in the machine readahle storage medium that when executed by a programmable system causes the system to perform the method according to any of aspects 60-75.
79. A method of monitoring a fluid release suh-nctwork of a fluid processing network, the method comprising: receiving measured curreni parameter values al known points of the sub-neiwork: determining current parameter values of the suh-network at least at points remote from the known points, the parameter values at said remote points heing determined using the measured current parameter values and the current active network model: based Ofl the current parameter values, determining if one or more pre-specified boundaries are breached; and performing a predetermined action if one or more said boundaries are breached.
SO. A method according to aspect 79 wherein the fluid release sub-network is a flare neiwork.
Si. A method according o aspect 79 or SO wherein the measured current paramder values, the determined eurren parameter values and the pre-specifled parameter boundaries are selected from fluid pressure, fluid temperature, pipe and I or vessel wall lemperaWre, fluid flow rate, and level of liquid in a vessel.
82. A method according to any of aspects 79-8 1 whcrein the parameter boundaries include predetermined constant boundaries.
83. A method according to any of aspects 79-82 wherein the parameter boundaries include variable boundaries or mathematical constraints derived from the involving the values of one or more parameters 84. A method according to any of aspects 79-83 wherein at least one predetermined risk is associated with a given parameter being outside a parameter boundary.
85. A method according to aspect 84 wherein predetermined risks include a pipe fracture risk, a pipe blockage risk and an explosion risk.
86. A method according lo any of aspects 79-85wherein the predetermined action is selecled from one or more of: issuing a notification lo a network operalor and issuing an instruclion lo an aulomaled neiwork control syslem.
87. A method according to aspect 86 wherein the notification indudes identification of a risk associated with said current parameter values breaching one or more boundaries.
88. A melhod according to any of aspects 79-88 wherein curreni parameter values are determined br known points of the sub-network For which measured parameter values are received.
89. A method according to aspect 88 wherein measured current parameter values for known points are replaced with the corresponding determined current parameter values for the same points in a manner that is consistent with the model of the fluid processing network.
90. A method according to any of aspects 79-89 wherein the sub-network model includes values of settings of the network based on received values of settings of the sub-network.
91. A method according to aspect 90 wherein the values of settings of the sub-network include values of valve settings of the network.
92. A method according to any of aspects 79-91 wherein one or more of the predetermined houndanes apply to respective one or more predetermined points of the network 93. Computer program code which when run on a computer causes the computer to perform the method according to any of aspects 79-92.
94. A carrier medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of aspects 79-92.
95. A computer program product comprising computer readable code according to aspect 94.
96. An article ol manulacture compnsing: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium that when executed by a programmable system causes the system to perlorm the method according to any of aspects 79-95.
In parlicular embodiments oF the invenlion relate to methods and apparatus br online monitoring ob fluid networks and other systems or planL In this conlext "online" means Ihe moniloring system modelling the Iluid neiwork is connecled Lo Ihe operaLing Iluid network, system or plani so as Lo he able Lo Lake measuremenLs and data in real time and model the neiwork, syslem or phint in real Lime or Fasler than real-lime.
Description of the Drawings
The invenlion will now be described in more delail wilh reference to the Figures, in which: Figure 1A illustrates an exemplary fluid processing neiwork including flare ndwork: Figure lB illustratcs an exemplary flare network; Figure 2 illustratcs a proccss for modelling a network, for examplc a flarc network; Figure 3 illuslrales apparatus for implementing an embodimeni of the invenhion: Figure 4A illustrates a first process for determining if a boundary has been breached according to an embodiment of the invention; Figure 4B illustratcs a second proccss for determining if a boundary has bccn breached according to an embodimeni of the invenlion; Figure 4C illustrates a third process for determining if a boundary has been breached according to an embodimeni of the invenlion; Figure 4D illusirahes a fourth process for delermining if a boundary has been breached according to an embodimeni of the invenlion; Figure 5 illustrates a process of determining if a future scenario will cause a boundary to he breached; and Figure 6 illuslrahes a syslem according lo an embodiment of the invention.
Detailed Description of the Invention
With relèrenee to Figure IA, a Iluid processing neiwork IOU may contain a plurality of regions I 01. Each region may have al kasi one fluid inlet for inteL of fluid mb a chamber I 0,ior example an inlet valve 103, and ab leasi one Iluid ouUeb br outlel oF Iluid oul of Ihe chamher, br example an outlel valve 107. Each region maybe provided with a path br oublet oF fluid from the neiwork alter Ihe Iluid has been processed, br example a valve 109. Each region 101 may be connected to a safety outlet, for example a valve 111, for outlet of fluid from the network through a fluid rclcase sub-nctwork bcfore, during or after processing of the fluid in order to relieve pressure within a region 101 if pressure within the region breaches a prcdetcrmincd safety level. With rcfcrcnce to Figure 1B, the outlet path for each region may lead to a region 120 of a flare network 110 comprising pipework and one or more headers 113 leading to one or more flare stacks 115 for ignition of the fluid.
Sensors (not shown), for example fluid pressure sensors and / or fluid lemperature sensors, may be disirihuled throughout the fluid processing neiwork, including the fluid release sub-network.
Different regions 101 may be provided for different functions, and different fluids may be present in different regions 101 -For example, an oil refinery network may contain a fractional distillation region providing a plurality of different fractions of crude oil, each fraction being supplied to a different region or regions of the network for different treatments depending on the end product to be produced from each fraction. Exemplary treatments for each fraction include, without limitation, hydrogenation, alkylation and catalytic cracking. An oil platform may have areas for high-pressure, medium-pressure and low-pressure separation of oil and gas.
The fluid in each region may be in a liquid state or a gaseous state, and I or a combination of liquid and gas, and the state may vary over time.
in order to cnsure that pressurc does not rcach a dangerous levcl in arcas where fluid property values are not dircctly measured, the fluid processing network may be modclled in order to dctcrminc such property valucs.
With relèrence to Figure 2, in a lirsl step 2W a mode] of Ihe flare network 110 maybe prepared by determining each component "building block" oF the hare neiwork and the connectivily oh di hierent componenis. Exemplary components include, withoul hmitation, pipe segmenls, valves, chambers and hares. The represenlation may include properlies of each componeni including, wilhoul limitalion, the malerial Ihat each component is madc of; one or morc dimensions of pipe segmcnts and chambers such as pipe segment diameter, length and wall thickness; and propertics such as wall roughness.
The model may he a set of mathemalical relalionships including a plurality of parameters of Ihe nelwork that vary over time (e.g. pressure, lemperalure) and describe the behaviour of Ihe nelwork over lime. The model may he generaled from sets of: algebraic equalions; ordinary differential equations; ordinary differenlial and algebraic equalions: inlegral and partial differenlial and ordinary differenlial equalions and algebraic equalions.
Exemplary models may he as implemented in commercially available gPROMS® software or MATLAB® software, and Ihe like.
The neiwork is deemed lo he operaling in a sale and ellicieni manner provided Ihe values of the model's parameters remain within pre-specified boundaries which may define a safe range for one or more parameters or combination of parameters for operation of the network, and I or boundary values for efficient operation of the network, for example boundary values that will avoid breakdown of all or part of the network and I or excessive utilisation of raw materials and energy. Parameter boundaries may be determined during design of the fluid processing network based on safety standards, recommended practice and considerations of material-of-construction properties.
Exemplary parameters for which boundaries may be specified include, without limitation, one or more of: (i) hluid pressure boundaries; (ii) fluid flow rate boundaries; (iii) fluid temperature boundaries; and (iv) melal wall lemperature boundaries br any componeni paris including, wiihout limilalion, pipe segmenis, valves and Iluid chambers.
The boundaries of a network may indude pre-determined constant boundaries, for example a constant associated with a property of a component of the network, and each component of the network may have one or more pre-determined constant boundaries associated with it. These pre-delermined consiani boundaries may he incorporaled ink the model of the flare neiwork 110 generated al siep 201.
The boundaries of a neiwork may include variable boundaries. Variable boundaries may vary over lime depending on the function of Ihe neiwork and I or materials presenl within the nelwork at a given lime. These variable boundaries may nol he incorporaled ink the model generaled al siep 201, hut maybe generated in real time depending on the application and condition of the neiwork al a given lime.
Boundaries maybe specified for example in terms of: (a) numerical lower and/or upper limits on the value of a certain parameter: for example, the temperature Tp of the fluid inside a pipe segment may have to he kept above a cerlain temperature limit TL at all times in order to avoid the risk of brittle fracture of the pipe: in this case, Tp is a model parameter, the value of which varies over time while, for a given material of construction of the pipe, TL is a known constant (e.g. approximately -46°C for certain types of carbon steel suitable for low-temperature applications); (h) relationships between the values of two or more parameters; tbr example, in order to avoid the risk of blockage caused by the formation of solid hydrates or solid ice in a pipe segment carrying fluid that contains water, the temperature, Tp, of the fluid inside it must always stay above the hydrate and ice formation temperatures, I and Ii respectively. Accordingly, boundaries in this case may be defined in terms of mathematical inequality constraints of the form Tr> H and F p> I'j. All three of these temperatures, Ip, lii and Ti, are parameters, the values of which vary over time depending on Ihe changing nature oF Ihe Iluid composilion and pressure as determined by Ihe solution of ihe model.
Each boundary maybe associated with one or more risks For exampk, in the case of safcty boundarics an explosion or rupturc risk may bc associatcd with thc fluid prcssurc within the network breaching a boundary fluid pressure value, and a fracture risk may he associated wilh a pipe temperature within the neiwork falling below a minimum pipe Lemperature.
A risk may he delermined to exist as soon as a boundary has been breached, or if a boundary remains breached for a predetermined period of time.
The representation of the entire network may he divided at step 203 into a plurality of regions, for example regions 101 as described with reference o Figure 1, or regions 120 in the case where the flare network I 10 only is modelled. hach region may have a different functionality.
A given boundary may be a universal boundary applicable to a network as a whole, for example a whole flare network 110 or may he different for two or more different regions, for example regions 120 of flare network 110. Exemplary universal boundary values include a universal maximum pressure and a universal maximum Mach number.
Each region, for exampk a region associated with a specific fluid processing operation, may independently he divided into sub-regions with different boundaries for different sub-regions. For exampk, a pipe may he made from a number of segments that differ in one or more of material or thickness,resulting in a different pressure and I or temperature boundary fir different sub-regions. A boundary may apply to a single component of the network or a sub-region containing a combination of components of the network.
With reference to Figure 3, a current state estimator 306 is configured to receive the network model from a database 304 storing the model generated as described in Figure 2, including a representation of the network and boundary values. The current state estimator 306 is further configured to receive data 302 from measurements made by sensors in the neiwork. The data 302 may be applied 10 the network model 304 lo determine real-time values of the parameters oF ihe neiwork, such as current temperatures and pressures within the neiwork, at points remole liom the measurement poinls, as described in more delail with reference to Figures 4A -4D. The curreni slate eslimator 306 may also analyse data 302 1mm measuremenl poinis and correci any inconsislencies in these dala, br example due Lo inaccurate readings. In a first sLep, the current slaLe cstimator 306 may determinc if data received from a measuremcnt point are inaccuratc based on the network modcl and on measurements from other measurement points and determine a more accurate value for that point to he used in place of the measured value.
if thc mcasurcd parameter values arc not mutually consistent (according to thc physical laws embedded in the model) then they may be replaced by the corresponding determined current paramctcr values such that parametcr values throughout thc network arc mutually consistent. Accordingly, the current state estimator 306 may determine accurate values for parameters throughout the network, including at measurement points.
It will he understood that method steps described in emhodimenls herein may be implemented as routines in compuler program code running on a computer processor or as equivaknt specialized circuits tbr data processing. Indeed such a system may he implemented in a computer architecture capable of running multiple processes in parallel, as will he described hereinafter with reference to Fig. 6.
Figure 4A illustrates a process for a data processing system configured to determine the state of a flare network 110 during operation, in use, embodiments of the invention provide real-time monitoring of fluid networks.
The system is configured to receive at step 401 current network data 302. The data 302 may include one or more of: data from measurements made by sensors in the flare network 110, for example pressures and temperatures from fluid pressure sensors and! or fluid temperature sensors: and fluid flow rate data, which may be directly measured fluid flow rate data or data on the exieni lo which inlet valves 111 of the ndwork 110 are open, from which flow-rates may be ddermined using established engineering techniques. The current network data 302 includes not only data of the measured parameter hut also the point in the network at which the parameter was measured.
The dala 302 only allows determinalion of wheiher a boundary value has been breached at the poinis of the neiwork from which Ihe dala has been taken.
At step 403, the system is configured to combine the received measured data with the network model stored in database 304 in order to determine a current network state, including parameters such as fluid pressure values and / or fluid temperature values both at the points al which direct measuremenis have been made and al points remote from those direci measuremeni poinis. This is achieved via Ihe solulion of a state estimalion problem applied lo Ihe underlying sd of mathematical equalions in Ihe model and based on an appropriate algorithm including, withoul limilalion, exiended Kalman Filters, Particle Fillering and others. The stale estimation problem and methods for us solution are described in more detail in, for example, S. Simon, Optimal State Estimation: Kalman, Hinf and Nonlinear Approaches, Wiley Interscience, 2006.
The curreni parameter values of the network may he checked at slep 405 againsl boundaries, stored in database 304 or another database, to determine if a boundary has been breached. The slored boundaries may he slored in a separale risk dalahase (nol shown). Ihe risk database may include the identity of a model parameter or combination of modd parameters associated with a risk, and the lower and upper fimits of the value of this parameter which determine whether a message alerting the operator needs to be issued, and the severity of this message (e.g. "for information", "warning", "severe warning" etc.). if a boundary has been breached, then an alert may be issued to a system user at step 407. The alert may contain one or more of: an alert as to the boundary that has been breached; a message indicating a risk associated with breaching this boundary; severity of the risk; and recommendations for remedial action.
if the network is automated, or certain safety systems of the network are automated, then the system may he configured to send an instruction to the automated network control system to take the relevatu remedial action. For example, a valve may he automatically opened in order to relieve a dangerous build up of pressure.
The current network data 302 may be periodically updated, and the parameter values of the current network state determined at step 403 may be periodically updated based on Ihe updated current network daLi in order thai the currenl nelwork stale delermined by the system is represenlalive of lhe rea-1ime stale of the nelwork.
Referring again to Figure I B, one or more regions I 20 of flare network I 10 may he inactivc during operation of the network. For example, fluid may bc flowing into or through one or more regions 1 20 and no fluid may he flowing in one or more other regions 120.
The dala processing syslem may be configured Lo analyse only the aclive regions of the network, and exclude maclive regions from the model. By "active region" as used herein is meani a region into or lhrough which fluid is flowing al or above a predelermined flow rate minimum, or has been flowing within a predetermined period of time, or where other paramelers (for example, melal wall temperaLures) are slill changing over lime as a resull of relatively recenl flow-of malerial lhrough the region. By "maclive region" as used herein is meant a region into or through which fluid is nol flowing at or above Ihe predetermined flow rate minimum, and has not been flowing at or above the predelermined flow rale minimum for al easL a predetermined period, or where other parameters are not changing with time. The flow rate minimum may be, but is not necessarily, a flow rate of zero.
With reference to Figure 4B, the system maybe configured to carry out steps similar to those of Figure 4A except that current network data 302 may be analysed at step 409 to determine currently active regions of the total network model stored in database 304, for example by tracing and modelling all possible flow paths emanating from valves that are currently open or valves that have recently been open and terminating at a flare stack.
Regions which are not deemed to be active may be excluded from the model of the total network to give a model of the currently active network, and the current network state determined at step 403 may be done only for the currently active network.
The activc or inactive state of any givcn rcgion may change over time, and thc data processing systcm may bc configured to periodically re-analysc thc current network data 302 to determine if any region has changcd from active to inactive, or from inactive to activc. In this way, the currently active nctwork determined at step 409 and the culTcnt network slale of Ihe currenl active neiwork generaled al sLep 403 maybe periodically updated in real lime SO as lo remain represenlalive at all Limes of Ihe current state oF the network.
Note that certain embodiments according to the invention may be able to automatically track and redefine active regions of the network and indude them in the computation, for example by using a code rouline Ihat monilors predetermined valves of the neiwork between their open and closed siales. Such a code rouline may execule from time lo lime or in response io a trigger, in order lo updale and/or redefine aclive regions to be included in the analysis.
By generaling a model thai includes only the active regions for analysis, grealer compulalional efficiency may be achieved and, the model may he more rohusl than a model including regions in which the values of paramelers representing fluid flowrales may be at or close 10 zero.
The total number of risks monitored and potentially identified hy the system may he very large. For example, a pipe may become brittle if it remains at below a first minimum boundary pipe temperature for at least a time Ti (for example due to the Joule-Thomson effect), and so a first risk associated with pipe temperature may he a fracture risk. A predetermined acceptable pipe fracture risk limit may he exceeded if a measured or determined pipe temperature falls below a predetermined minimum pipe temperature boundary, or if a pipe temperature remains below a predetermined minimum pipe temperature boundary for more than a predetermined maximum time.
Furihermore, if a gas flowing ihrough the pipe conlains waler vapour, ihen Ihere may he a second risk of ice and/or other solids such as hydrates forming within the pipe at Lemperatures below Ihe minimum houndary va'ue, which may ullimalely resull in hlockage of ihe pipe. A predetermined acceptable ice blockage risk limil may he cxcecdcd if water vapour is flowing through a pipe and if a measurcd or dctcrmincd pipe temperature falls below a predetermined minimum pipe temperature boundary, or if a pipe temperaturc rcmains below a prcdctermincd minimum pipe temperature boundary br more than a predetermined maximum time during flow of waler vapour Lhrough the pipe.
With reference to Figure 4C, datahase 304 containing the network mode] contains al] prcdetermincd risks and acccptablc risk limits, in another embodiment, a scparatc risk database may contain al] predetermined risks and acceptable risk]imits. Each entry in this database may comprise one or more hems of informalion including, hul nol limiled lo, the idenlily of the modes parameler associaled with the risk, the lower and upper limits of Ihe value of this parameter which determine wheiher a message akrling the operator needs to he issued, and the severity of this message (e.g. "for informalion", "warning", "severe warning" dc.). The data processing system may be configured to seleci all or a subsel of the predelermined acceplable risk limils for analysis lo determine if one or more acceplable risk limits has been exceeded al steps 411 and 413.
Each risk to be ana'ysed may be associated with a sekction requiremeni which may be hased on parameter values from the current network state determined at step 403. For example, with reference again Lo Lhe example risk of ice lorming wiLhin a pipe segmeni, the system may be configured not to monitor the ice formation risk within a pipe or pipes of a particular region if the measured or determined temperature of the pipe or pipes in that region is above a predetermined level, and / or if no water vapour is present in the pipe or pipes of that region.
The degree of detail that needs to be incorporated within the model may depend on the risks that are to be monitored. For example, a model of a pipe segment that is capable of predicting the potential formation of ice may be more complex than one that can predict only the temperature and pressure within the pipe. The model database 304 may contain multipk mod&s of the same component (such as pipe segments or vessels), each such model having different degrees of detail. For example, in the ease of a component represenling a pipe segment, the model database may eonlain lhree dislinet models: the simplesi modes may comprise on'y a description of the relation belween the fluid fiowrate in the pipe and the pressure drop within the pipe; a model of intermediate complexity may additionafly include a description of the variation of temperature in the wall of Ihe pipe athng the length oF the pipe, togeiher with descriptions of ihe heal IransFer between ihe wall and Ihe Iluid in Ihe pipe and between the wall and (lie surrounding almosphere; linally, Ihe most deLailed model may addilionally include a description oF Ihe polenlial lormalion oF solids wiLhin Ihe pipe al very low temperalures.
The syslem may then aulomalically select a model of the appropriale degree oF delail lbr cach and evcry component of thc network that is consistent with thc risks that are sclectcd to be analysed in constructing a model of the overall network the selection may he made via the evaluation of pre-specified logical conditions involving the current values of the parameters in the nctwork. in the cxample of the pipc segment mentioned above, the system may switch from the simplest modes to the intermediate complexity model if the temperature Tp of the fluid in the pipe drops to within a pre-specified margin of the temperature T11 of brittle fracture of the material of construction of the pipe; T is a parameter that is computed by the current state estimator 403, and its value will generally change over time, while TB is a known constant. Furthermore, the system may he configured to switch to the most detailed model if the differences between the temperature TF of the fluid in the pipe and the temperatures of potential ice or hydrate lormation, Ii and T11 respectivdy, are smafler Ihan pre-specified margins; both T1 and T11 are time-varying parameters Ihal are compuled by Ihe current stale estimalor 403.
Moreover, ihe choice of an appropriale model br each componenl may he periodically changed as and when required lhroughout Ihe operation oF the syslem, such changes Iriggering a real-lime re-conliguration of ihe overall neiwork model.
the systcm may be configurcd to monitor ccrtain risks rcgardlcss of thc current network state determined at step 403. For exampk, the system maybe configured to always monitor risks that may have a severe safety impact, for cxample an cxplosion risk, as opposed to risk limits associated with risks that may impact only the efficiency of the network.
By selecting the risks lo be analysed and re-configuring the modes in the manner described above, the processing lime and I or processing pow-er required by the system may be reduced with little or no reduction in the effecliveness of the system.
Figure 4D illusirales a combination of Figures 4B and 4C, wherein inactive regions are determined al siep 409 and are omilted Irom Ihe network mode] as descrihed in Figure 4B, a suhseL oF the risks to he monitored are sekcted as described in Figure 4C, and Ihe network model is auiomaticálly re-conligured Lu incorporale componeni models oF an appropnaLe degree oF detail. If regions of Ihe network are delermined Lo he inactive, thcreby resulting in a more compact network modcl, then this may allow for morc risks to be selected to be monitorcd using more detailcd component modcls than if no determination of inactive regions is made.
The dala processing syslem described with reference Lo Figures 4A -4D is configured to read when the currenl slate of ihe neLwork has breached one or more pre-specified boundaries. The data processing system may he further configured to delermine if ihe network is likely lo breach one or more boundaries at some fulure lime, should certain hypothetical evenls take place.
Figure 5 i]]ustrates the manner in which the system may execute one future scenario slarling from the eurrenL slate 501 ol the neLwork as deLermined aL slep 403 descrihed above with reference to Figures 4A -4D. ihe definition of a future scenario 502 comprises items of information including hut not]imited to the conditions under which the scenario needs to be considered, the time horizon of interest to the scenario (e.g. a certain number of minutes starting from the current time), and the variation of zero or more inputs to the network (e.g. flowrates entering through one or more of the valves 111 in Figure 1B) over this time horizon. For example, a future scenario may determine the effects of opening some valves 111 which are currently closed, and/or of closing of some of the valves which are currently open, and/or of variations in the flowrates or other properties (e.g. composition and/or temperature) of the material received through valves which are currently open. lie number of such additional inputs may be zero, in which case Ihe fulure scenario will simply determine how ihe neLwork stale will evolve over Lime from ils current condition if no changes are made lo network settings. At sLep 503, the syslem determines whether the future scenario is lo be evaluated al the current time.
The definiLion of a future scenario may specify that il must be executed al pre-specified, usually regular, time inlervals (e.g. every 10 minuies) and/or whenever one or more crileria (expressed in lerms of logical condilions involving Ihe curreni values ol one or more of Ihe system paramelers) hold Irue. For example, a fulure scenario relating lo a hypothetical eveni thai may potentially cause Ihe network to exceed its capacity may need to he considered only ii the system is currenily operating near its capacily, atheil slill under it.
Ai slep 503, the system may also proceed with the evalualion of a future scenario following an explicil request issued by exiernal agents such as, for example, authorised human operalors or olher computer programmes. Such a future scenano may eilher he entirely pre-defined or he parlially or fully configured by the exiernal agent prior to us execution. This funclionalily allows the system to ad as a decision supporl lool, for example providing advance imowledge of the effects of a proposed sd of actions on the network's future behaviour.
Assuming Ihal slep 503 delermines thai Ihe future scenario is to he evalualed, then slep 504 determines the set of active regions that need to he considered given the current state ol Ihe neLwork and the additional network inpuls stipulaLed hy Ihe future scenario Using the model database 304, this step may also construct an appropriate subset of the network model describing these active regions as described in step 409 in Figures 4B and 4D. In doing so, and by applying appropriate criteria to the predicted state of the network, the system may also determine the appropriate degree of modelling detail to be applied to the modelling of each component in the network as described in step 411 in Figure 4D.
Using the model constructed at step 504, a future scenario evaluator is configured at step 505 to perform a dynamic simulation to advance time over a pre-specified time interval, taking account of the time varying inputs to the network as specified in the definition of the future scenario, and predicting the state of the network at the end of this time interval.
Then at step 506 the future scenario evaluator assesses this predicted state against the database of risks 507 and issues appropriale a1er messages should one or more boundaries he breached. Each such message may comprise several items of information, including, hut not limited to, an identification of the future scenario being evaluated, the future time (e.g. relative In the current Lime) aL which Lhe hreach is predicLed to take place, and the identities and values of the parameter(s) involved in Ihis breach.
Finally, at step 508 the future scenario evaluator checks whether the end of the time horizon of interest for thc future scenario bcing evaluated has been reached. If this is thc case, the execution of the scenario terminates, otherwise the a'gorithm is repeated from slep 504.
The syslem may he configured to consider for evalualion one or more fulure scenarios using one or more respeclive fulure scenario evaluaLcrs operaling concurrently or sequentially, such consideration taking place at regular Lime inlervals during Ihe network's operation. Analysis of each future scenario may he conducled concurrently or sequentially on Ihe same processor, or analysis may be divided between a plurality of processors. Oplionally, each fulure scenario has a dedicaled processor running a dedicaled future scenario evaluator.
Figure 6 illustrates an embodiment of the system as described herein, illustrating both monitoring of the current state of a flare network and evaluating the effect of a number n of future scenarios on the flare network.
The system comprises computational components 306, 601, 602, 603, 604 implemented as general computer code which is independent of the particular flare network to which is system is being applied. A separate instantiation of the future scenario evaluator component is executed for each future scenario under consideration. Each computational component may be implemented as a separate computer program. Different components maybe executed on the same or on dilTereni computer processors, communicaLing with each other using well-established inter-process communication protocols such as the Message Passing Interface (MPI) or Parallel Virtual Machine (PVM) protocols.
The flare network and the desired behaviour of the system when applied to it are defined entirely in terms of the configuration files 304, 310 and 610. A separate future scenario definition file 610 is provided for each future scenario under consideration. The complete separalion heiween genera] compulalional components and network-specilic conliguralion li]es facililales the deploymeni and mainlenance of Ihe syslem.
During the operation of the system, real-time data are received from the sensors in the network. Some of thcse data are used by thc Inlet Flowrates Calculator 602 to determine the inlet flowrates to the network if these are not already measured directly. The data, including Ihe computed inlet flow-rates, are then passed to the Flare Nelwork State Eslimalor 306 which is a computer code implementation of the algorithm described in Figure 4D, Any alerls issued are transmitted lo Ihe Central Monilor 603 which communicales them in an appropriate form to Ihe Operator Console 604.
The Flare Nelwork Stale Estimalcr 306 also communicales Ihe current stale of the syslem to the Future Scenario Evaluators 601, each of which is responsible for the execution of a different pre-defined future scenario 610. Each Future Scenario Evaluator 601 is a computer code implementation of the algorithm described in Figure 5. Any alerts arising from the execution of the corresponding future scenario are transmitted to the Central Monitor 603 which then communicates them in an appropriate form to the Operator Console 604.
The system described herein may allow for accurate determination of: parameters such as temperature and pressure of fluid within the network; prediction of parameters at points in the future if changes are made or are not made; and determination of risks to efficiency and I or safety of the network.
As shown by the above description, at least some implementations of the invention as described herein may involve programming, lbr example, of a processor unit of one or more servers. Implementations that may involve programming include, without hmitation: generating a model of a fluid processing network; determining parameters of the network by applying measured data to a mode] of the network; determining ii a determined parameter exceeds a boundary; predicting the effect on a parameter if a change, or if no change is made to thc scttings of the system; determining if a predicted parameter exceeds a boundary; and taking a predetermined action if a determined or predictcd parameter exceeds a boundary for that parameter, such as issuing an alert Program aspects of the technology may he thought of as "products" or "articles of manufacture" typically in Ihe lorm of executable code and/or associated data that is carried on or embodied in a type of machine readable medium. "Storage" type media include any or all of the memory of the supporting electronics system, computing devices, processors or the like, or associated modules thereof, such as various semiconductor memories, tapc drives, disk drives and the like, which may providc storage at any timc for the software programming. All or portions of the software may at times he communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another computer or processor. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may he considered as media hearing the software.
Hence, a machine readable medium may take many forms, including hut not limited to, a tangible non-transitory storage medium, a carrier wave medium or physical transmission medium. Tangible non-volatile storage media include, tbr example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like. Tangible volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables: copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media can take the term of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RI) and infrared (1K) data communications. Many of these forms of computer readable media may he involved in carrying one or more sequences of one or more instructions to a processor for execution.
The invention has been described herein primarily with reference to a fluid release networks, for example a flare network of a fluid processing network. However, it will he understood that the invention may he applied to any fluid processing network in order to, without limitation, generate a model of the fluid processing network; determine paramelers oF the Iluid processing neiwork by app'ying measured dala lo a mode] of Ihe network; determining if a determined parameter exceeds a boundary; predicling the elièct on a parameter ii a change, or if no change is made lo the seltings of the syslem; determining ii a predicled parameter exceeds a boundary; and laking a predetermined action if a delermined or predicted parameler exceeds a boundary Ior that parameler, such as issuing an alert. Such alerts can be uscd for example as mcssagcs for human opcrators, as messagcs for gencral machine control interfaces, as safety alcrts and / or alarms.
For example, the invenlion may be applied lo an oil refinery which may have a fluid processing neiwork including, without limilation, one or more hydrogenalion, alkylation and / or calalylic cracking regions dedicaled for a specific oil fraclion, and further hydrogenation, ailcylalion and / or catalytic cracking regions for further respeclive specific oil fractions.
Although the present invenlion has been described in terms of specific exemplary embodiments, it will he appreciated that various modifications, a]terations and/or comhinaLions ol leatures disclosed herein wiH he apparent to Ihose skilled in the art without departing from the scope of the invention as set forth in the following claims.
Claims (1)
- Claims A method of monitoring a fluid proccssing network comprising a plurality of fluid processing regions, the method comprising the steps of: receiving measured current parameter values at known points of the network; delermining from the measured curreni parameter values regions of the neiwork that are acUve, all other regions being deemed maclive: subtracting inactive rcgions of thc network from a model of the fluid processing nctwork to providc a current active network modcl; determining current parameter values of the current active network at least at points remote from the known points, the parameter values at said remote points being determined using the measured current parameter values and the current activc network model; based on the current parameter values, determining if one or more pre-specificd boundaries arc breached: and performing a predetermined action if one or more said boundaries are breached.2. A method according lo claim 1 wherein the curreni aclive network model is periodically updated using periodically received updates of the measured current parameler values.3. A method according lo claim 2 wherein inactive regions Ihat have become aclive, or active regions Ihal have become maclive, are determined from Ihe updates of the measured current parameler values.4. A method according to any preceding claim wherein the measured current parameter values, the determined current parameter values and the pre-speeified parameter boundaries are selected from fluid pressure, fluid temperature, pipe and I or vessel wall temperature, fluid flow rate, and level of liquid in a vessel.5. A melhod according lo any preceding claim wherein (be parameler houndaries include predetermined conslani boundaries.6. A method according to any preceding claim wherein the parameter houndaries include variable boundaries or mathematical constraints derived from the values of one or more parameters.7. A method according lo any preceding claim wherein at least one predetermined risk is associated with a given parameter being outside a parameter boundary.8. A method according to claim 7 wherein predetermined risks include a pipe fracture risk, a pipe blockage risk and an explosion risk.9. A method according to any preceding claim wherein the predetermined action is selected from one or more of: issuing a notification to a network operator and issuing an instruction to an automated network control system.10. A method according to claim 9 wherein the notification includes identification of a risk associated with said current parameter values breaching one or more boundaries.1. A method according to any preceding claim wherein current parameter values are determined for known points of the current active network for which measured parameter values are received.I 2. A method according to claim I I wherein measured current parameter values for known points are replaced with the corresponding determined current parameter values for the same points in a manner that is consistent with the model of the fluid processing network.13. A method according to any preceding claim wherein (be current active network model includes va'ues of settings of the network based on received values of settings of Ihe network.14. A melhod according to claim 14 wherein the values oF settings oF the network include values oF valve settings of the network.15. A method according to any preceding claim wherein one or more of the pre-specified boundaries apply to respective onc or more prc-spccified points of the network.16. A method according to any preceding claim wherein the network is a flare network.17. Computer program code which when run on a computer causes the computer to perform the method according to any preceding claim.iS. A carrier medium calTying computer readable code which when run on a computer causes the computer to perform the method according to any of claims 1-16.19. A computer program product comprising computer readable code according to claim IS.20. An article of manufacture comprising: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium that when executed by a programmable system causes the system to perform the method according to any of claims 1-16.21. A method of monitoring a fluid processing network formed from a plurality of fluid processing components, each fluid processing component being associated with one or more predetermined component models, the method comprising: receiving measured current parameter values at known poinis of the neiwork: generating a model of the fluid processing network from a selection of the predetermined component models; delermining curreni parameler values of ihe network at leasi al poinis remote from (be known poinis, (be parameler values al said remole points being determined using (be measured curreni parameter values and Ihe network model: dctermining if a current parameter value breaches one or more predctcrmincd boundaries; and performing a predetermined aclion if one or more of said boundaries are breached, wherein one or more of the fluid processing components of the network are each associated with two or more predetermined eomponenl models; and wherein for each component that is associated with two or more predetermined component models, onc of the said two or more predetermined component models is selected for use in gcncrating thc model of the fluid processing network depending on at least one condition of the fluid processing network.22. A method according to claim 21 wherein the at least one condition of the fluid processing network determining Ihe component model for a particular component used in generating the model of the fluid processing network indude one or more of: one or more curreni parameltr values of the neiwork; one or more anlicipated future parameter values of the network: and the presence of one or more chemical species in the fluid processing network.23. A method according to any of claims 19-22 wherein one or more components of the network are associated with at least one component model that is used in generating a model for all conditions of the fluid processing network.24. A method according to any of claims 19-23 wherein the model is modified in response lo a change in a condition of Ihe Iluid processing network thai causes a change in the selection of component model used in generating the model.25. A melhod according lo any of claims 19-24 wherein the measured current parameter values, Ihe determined current parameter values and the predetermined parameter boundaries are selected Irom Iluid pressure, Iluid temperature, pipe and I or vessel wall lemperalure, fluid flow rale, and level ol liquid in a vessel.26. A method according to any of claims I 9-25 wherein the parameter boundaries include predetermined constant boundaries.27. A method according lo any of claims 19-26 wherein the parameter boundaries include variable boundaries or mathemalical consiraints derived from the values of one or more parameters.28. A method according to any of claims 19-27 wherein one or more of the predelermined boundaries apply o respective one or more pre-spe ified poinis of the network.29. A method according to any of claims 19-28 wherein at lcast one prcdetermined risk is associated with a givcn paramctcr brcaching a parameter boundary.30. A method according to claim 29 wherein predetermined risks include a pipc fracture risk, a pipe blockage risk and an explosion risk.31. A method according to any of claims 19-30 wherein the predetermined action is selected from one or more of: issuing a notification to a network operator and issuing an instruction to an automated network control system.32. A method according to claim 31 wherein the notification includes identification of a risk associated with said current parameter values breaching one or more boundaries.33. A method according lo any of claims 19-32 wherein curreni parameter values are deLermined for known poinis of Ihe neiwork for which measured parameter values are received.34. A method according to claim 33 wherein measured current parameter values for known points are replaced with the corresponding determined current parameter values br ihe same poinis in a manner lhai is consisient wiih ihe model of Ihe fluid processing neiwork.35. A method according to any of claims I 9-34 wherein the network model includes values of settings of the network bascd on received values of settings of the network.36. A method according io claim 35 wherein the values of seitings of ihe network include values of valve seiiings of the neiworic 37. A method according to any of claims I 9-3ówhcrcin the plurality of fluid processing components include pipe segments, valves and fluid chambers.38. A method according to any of claims 19-37 wherein the network is a flare network.39. Computer program code which when run on a computer causes the computer to perform the method according to any of claims 19-38.40. A carrier medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of claims 19-39.41. A computer program product comprising computer readable code according to claim 40.42. An article of manufaciure comprising: a machine-readable storage medium; and executable program insiructions embodied in the machine readable storage medium thai when executed by a programmable system causes the system to perform the meihod according to any ol claims 19-41.43. A method of monitoring a fluid processing network, the method comprising the steps of: receiving measured current parameter values at known points of the network: determining current parameter values at least at points remote from the known points, the parameter values at said remote points being determined by application of the measured parameter values Lu a model of the neiwork: selecting one or more risks to be analysed from a group of predetermined risks, each predetermined risk heing associated with one or more points of the network, for each selected risk, determining if a risk at the one or more associated points of the network exceeds a predetermined acceptable risk limit for that risk at one or more of those points; and performing a predetermined action if a risk exceeds a predetermined acceptable risk limit, wherein a risk is selected for analysis if a current parameter at the one or more points associated with the risk meets a predetermined risk selection requirement.44. A method according to claim 43 wherein one or more of the group of predetermined risks is not selected for analysis.45. A method according to claim 43 or 44 wherein each predetermined risk defines at least one parameter boundary that should not he breached.46. A method according to any of claims 43-45 wherein the measured cuntnt parameter values, the boundaries and the determined current parameter values and the predetermined parameter houndaries arc selected from fluid pressure, fluid temperature, pipe and I or vessel wall temperature, fluid flow rate, and level of liquid in a vessel.47. A method according to any of claims 43-46 wherein the parameter boundaries include variable houndaries or mathematical constraints derived from the values of one or more parameters.48. A method according 10 any of claims 43-47 wherein the predetermined action is selecied from one or more of: issuing a nolilicalion lo a network operalor and issuing an instruelion lo an aulomaled neiwork conirol syslem.49. A method according to claim 48 whcrein the notification includes idcntification of the risk limit that has been exceeded.50. A method according lo any of claim 43-49 wherein predetermined risks include a pipe fracture risk, a pipe blockage risk and an explosion risk.51. A method according to any of claims 43-50 wherein current parameter values are delermined for known points of (he currenl active neiwork for which measured parameter values are received.52. A method according to claim 51 whcrein measurcd current parameter values for known points arc replaced with the corresponding determined currcnt parameter values for the same points in a manner that is consistent with the modcl of thc fluid processing network.53. A method according lo any of claims 43-52 wherein the current network model includes values of settings of the network hased on received values of settings of Ihe nelworic 54. A method according to claim 53 wherein the values of settings of the network include values of valve settings of the network.55. A method according to any of claims 43-54 wherein the network is a flare network.56. Computer program code which when run Ofl a compuLer causes the computer to perlorm the method according to any oF claims 43-55.57. A carder medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of claims 43-55.58. A compuler program product comprising computer readable code according Lo claim 57.59. An article of manufacture comprising: a machine-readable storage medium; and cxccutable program instructions embodicd in thc machine readabic storagc medium that when executed by a programmable system causes the system to perform the meihod according to any of c'aims 43-55.60. A method of monitoring a fluid processing neiwork, the method comprising the sleps of: receiving measured currenL parameter values aL known points of the neLwork: receiving values of scttings of the nctwork; dctermining current parameter valucs at lcast at points rcmotc from the known points, the paramcter valucs at said remote points bcing determined by application of the measured parameter values to a model of the network; predicting parameter values at a future point in time if a predetermined change is made to the settings of the network, or if no change is made to the settings of the network; and performing a predetermined action if the predetermined change, or if no change, is determined to have the effect of causing one or more of the predicted parameter values to breach a predetermined parameter boundary.61. A method according lo c'aim 59 wherein the values of seltings of Ihe network comprise values of valve seltings of the network.62. A method according to claim 60 or 6! wherein the measured current parameter values, the determined current parameter values and the predetermined parameter boundaries are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and level of liquid in a vesseL 63-A melhod according lo any of claims 60-62 wherein the parameter boundaries include predetermined conslani boundaries.64. A method according to any of claims 60-63 wherein the parameter boundaries include variable boundaries or mathematical constraints derived from the values of one or more parameters.65. A method according lo any of claims 60-64 wherein at leasi one predetermined risk is associaled wilh a given parameter being outside a parameler boundary.66. A method according to claim 65 wherein predetermined risks include a pipe fracwre risk, a pipe blockage risk and an explosion risk.67. A method according to any of claims 60-66 wherein the predetermined action is selected from one or more of: issuing a notification to a network operator and issuing an instruction to an automated network control system.6K A method according to claim 67 wherein the notification includes identification of a risk associated with said current parameter values breaching one or more boundaries.69. A method according to any of claims 60-6S wherein current parameter values are determined for known points of the current network for which measured parameter values are received.70. A method according to claim 69 wherein measured current parameter values for known points arc replaced with the corresponding determined eunent parameter values for the same points in a manner that is consistent with the model of the fluid processing network.71. A melhod according lo any ol claims 60-70 wherein the current active neiwork model includes va'ues of seltings of ihe network based on received values of seltings of Ihe neiwork.72. A melhod according lo any of claims 60-7! wherein the va'ues of seLings of ihe neiwork include values of valve setlings of the network.73. A method according to any of claims 60-72 wherein one or more of the pre-specified boundaries apply to respective onc or more prc-spccified points of the network.74. A method according lo any of claims 60-73 wherein the network is a flare neiwork.75. COUt program code which when run on a computer causes the computer to perform the method according to any of claims 60-74.76. A carrier medium calTying computer readable code which when run on a computer causes the computer to perform the method according to any of claims 60-75.77. A computer program product comprising computer readable code according to claim 77.78. An article of manufacture comprising: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium that when executed by a programmable system causes the system to perform the method according to any of claims 60-75.79. A method of monitoring a fluid release sub-network of a fluid processing network, the method comprising: receiving measured curreni parameter values al known points of the sub-neiwork: determining current parameter va!ucs of the sub-network at least at points remote from the known points, the parameter values at said remote points being determined using the measured current parameter values and the current active network model: based Ofl the current parameter values, determining if one or more pre-specified boundaries are breached; and performing a predetermined action if one or more said boundaries are breached.SO. A method according to claim 79 wherein the fluid release sub-network is a flare neiwork.Si. A method according lo claim 79 or 80 wherein the measured currenl parameter values, the determined eurrenl parameter values and the pre-specifled parameter boundaries are selected from fluid pressure, fluid temperature, pipe and I or vessel wall lemperalure, fluid flow rate, and level of liquid in a vessel.82. A method according to any of claims 79-Si wherein the parameter boundaries include predetermined constant boundaries.83. A method according to any of claims 79-82 wherein the parameter boundaries include variable boundaries or mathematical constraints derived from the involving the values of one or more parameters 84. A method according to any of claims 79-83 wherein at least one predetermined risk is associated with a given parameter being outside a parameter boundary.85. A method according to claim 84 wherein predetermined nsks include a pipe fracture risk, a pipe blockage risk and an explosion risk.86. A method according 10 any of claims 79-85wherein Ihe predetermined aclion is selecled from one or more of: issuing a nolificalion lo a network operalor and issuing an instruclion lo an aulomaled nelwork conirol syslem.87. A method according to claim 86 wherein the notification includes identification of a risk associated with said current parameter values breaching one or more boundaries.88. A melhod according to any of claims 79-88 wherein current parameter values are determined br known points of the sub-network For which measured parameter values are received.89. A method according to claim 88 wherein measured current parameter values for known points are replaced with the corresponding determined current parameter values for the same points in a manner that is consistent with the model of the fluid processing network.90. A method according to any of claims 79-89 wherein the sub-network model includes values of settings of the network based on received values of settings of the sub-network.91. A method according to claim 90 wherein the values of settings of the sub-network include values of valve settings of thc network.92. A method according to any of claims 79-91 wherein onc or more of the predetermined houndanes apply to respective one or more predetermined points of the network 93. Computer program code which when run on a computer causes the computer to perform the method according to any of claims 79-92.94. A carrier medium carrying computer readable code which when run on a computer causes the computer to perform the method according to any of claims 79-92.95. A computer program product comprising computer readable code according to claim 94.96. An article ol manulacture compnsing: a machine-readable storage medium; and executable program instructions embodied in the machine readable storage medium that when executed by a programmable system causes the system to perlorm the method according to any of claims 79-95.
Priority Applications (11)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1200580.7A GB2500562A (en) | 2012-01-13 | 2012-01-13 | System Of Monitoring And Controlling Fluid Processing Networks |
| US14/371,962 US9720422B2 (en) | 2012-01-13 | 2013-01-10 | System for fluid processing networks |
| JP2014551674A JP6227558B2 (en) | 2012-01-13 | 2013-01-10 | Method, system, carrier media and product for monitoring a fluid treatment network |
| MYPI2014701906A MY174095A (en) | 2012-01-13 | 2013-01-10 | System for fluid processing networks |
| EP13702498.0A EP2802948B1 (en) | 2012-01-13 | 2013-01-10 | System for fluid processing networks |
| PCT/GB2013/050038 WO2013104905A2 (en) | 2012-01-13 | 2013-01-10 | System for fluid processing networks |
| AU2013208770A AU2013208770B2 (en) | 2012-01-13 | 2013-01-10 | System for fluid processing networks |
| IN5788DEN2014 IN2014DN05788A (en) | 2012-01-13 | 2013-01-10 | |
| KR1020147022546A KR20140112554A (en) | 2012-01-13 | 2013-01-10 | System for Fluid Processing Networks |
| BR112014017263A BR112014017263A8 (en) | 2012-01-13 | 2013-01-10 | monitoring method and system for fluid processing networks |
| IL233576A IL233576A (en) | 2012-01-13 | 2014-07-09 | System for fluid processing networks |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1200580.7A GB2500562A (en) | 2012-01-13 | 2012-01-13 | System Of Monitoring And Controlling Fluid Processing Networks |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| GB201200580D0 GB201200580D0 (en) | 2012-02-29 |
| GB2500562A true GB2500562A (en) | 2013-10-02 |
Family
ID=45813990
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB1200580.7A Withdrawn GB2500562A (en) | 2012-01-13 | 2012-01-13 | System Of Monitoring And Controlling Fluid Processing Networks |
Country Status (1)
| Country | Link |
|---|---|
| GB (1) | GB2500562A (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799759A (en) * | 2019-01-30 | 2019-05-24 | 宁夏万维智联科技有限公司 | A kind of harmful influence Intellectualized monitoring embedded system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080082215A1 (en) * | 2006-09-28 | 2008-04-03 | Exxonmobil Research And Engineering Company | Method and apparatus for enhancing operation of a fluid transport pipeline |
| US20100299122A1 (en) * | 2005-10-03 | 2010-11-25 | Tyco Fire Products Lp | System and method for evaluation of fluid flow in a piping system |
-
2012
- 2012-01-13 GB GB1200580.7A patent/GB2500562A/en not_active Withdrawn
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100299122A1 (en) * | 2005-10-03 | 2010-11-25 | Tyco Fire Products Lp | System and method for evaluation of fluid flow in a piping system |
| US20080082215A1 (en) * | 2006-09-28 | 2008-04-03 | Exxonmobil Research And Engineering Company | Method and apparatus for enhancing operation of a fluid transport pipeline |
Also Published As
| Publication number | Publication date |
|---|---|
| GB201200580D0 (en) | 2012-02-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Adepu et al. | Using process invariants to detect cyber attacks on a water treatment system | |
| CN107976968B (en) | Method and system for detecting an operating mode of a valve in a process plant | |
| JP7324564B2 (en) | Method and system for detecting the condition of process plant entities | |
| US20240048596A1 (en) | Parametric analysis of integrated operational and information technology systems | |
| Krotofil et al. | The process matters: Ensuring data veracity in cyber-physical systems | |
| Orojloo et al. | A game-theoretic approach to model and quantify the security of cyber-physical systems | |
| US7711500B1 (en) | Pressure relief valve monitoring | |
| Wang et al. | Fault detection behavior and performance analysis of principal component analysis based process monitoring methods | |
| WO2015104691A2 (en) | Systems, methods, and devices for detecting anomalies in an industrial control system | |
| JP2019525076A (en) | Smart high integrity protection system | |
| Priyanka et al. | Remote monitoring and control of LQR-PI controller parameters for an oil pipeline transport system | |
| AU2013208770B2 (en) | System for fluid processing networks | |
| Geng et al. | A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes | |
| Ruiz-Cárcel et al. | Canonical variate analysis for performance degradation under faulty conditions | |
| Dakheel et al. | Intrusion detection system in gas-pipeline industry using machine learning | |
| US20230228605A1 (en) | Method and system for wireless flow sensing of manually operated valves | |
| EP3614221B1 (en) | Risk evaluation device, risk evaluation system, risk evaluation method, risk evaluation program, and data structure | |
| Rubio-Hernan et al. | Event-triggered watermarking control to handle cyber-physical integrity attacks | |
| GB2500562A (en) | System Of Monitoring And Controlling Fluid Processing Networks | |
| Salmasnia et al. | Integration of production planning, maintenance scheduling and noncentral chi-square chart parameters with random failures and multiple assignable causes | |
| Zhang et al. | A novel key performance indicator oriented process monitoring method based on multiple information extraction and support vector data description | |
| Bauer et al. | Measuring cause and effect between process variables | |
| Dou et al. | Optimization method of suspected electricity theft topic model based on chi-square test and logistic regression | |
| Weaver et al. | Simulating Energy and Security Interactions in Semiconductor Manufacturing: Insights from the Intel Minifab Model | |
| Mejía et al. | Early detection and diagnosis of operating events in real-time for producing wells with artificial lift systems with submersible pumping |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| WAP | Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1) |