Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01J—CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
  • B01J19/00—Chemical, physical or physico-chemical processes in general; Their relevant apparatus
  • B01J19/0006—Controlling or regulating processes
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01J—CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
  • B01J19/00—Chemical, physical or physico-chemical processes in general; Their relevant apparatus
  • B01J19/0006—Controlling or regulating processes
  • B01J19/0033—Optimalisation processes, i.e. processes with adaptive control systems
• • C—CHEMISTRY; METALLURGY
  • C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
  • C10G—CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION; RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES
  • C10G7/00—Distillation of hydrocarbon oils
  • C10G7/12—Controlling or regulating
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01J—CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
  • B01J2219/00—Chemical, physical or physico-chemical processes in general; Their relevant apparatus
  • B01J2219/00049—Controlling or regulating processes
  • B01J2219/00168—Controlling or regulating processes controlling the viscosity
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01J—CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
  • B01J2219/00—Chemical, physical or physico-chemical processes in general; Their relevant apparatus
  • B01J2219/00049—Controlling or regulating processes
  • B01J2219/00186—Controlling or regulating processes controlling the composition of the reactive mixture
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
  • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
  • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
  • G01N24/085—Analysis of materials for the purpose of controlling industrial production systems
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/46—NMR spectroscopy
  • G01R33/4625—Processing of acquired signals, e.g. elimination of phase errors, baseline fitting, chemometric analysis

Definitions

• This invention relates to the field of petroleum refining and in particular to methods and systems for controlling petroleum distillates produced in a distillation tower.
• Crude oil also known as petroleum
• Crude oil is a complex mixture of hydrocarbons.
• the component hydrocarbons of this mixture are separated from one another to produce commercially valuable petroleum products.
• the physical and chemical processing steps for separating crude oil into component hydrocarbons are collectively referred to as "refining.”
• an effective technique for separating them from one another is fractional distillation.
• heated petroleum is fed to a distillation tower having a temperature gradient that progressively decreases from a high at the base of the tower to a low at the top of the tower.
• Petroleum vapor which consists of the component hydrocarbons in their vapor phase, rises through the distillation tower. As it rises, the petroleum vapor encounters progressively lower temperatures.
• the distillate collected from any tray within a distillation tower is not a pure form of a particular hydrocarbon.
• the distillate although dominated by a particular hydrocarbon, includes impurities. These impurities affect properties of the distillate.
• a difficulty associated with the fractional distillation of petroleum is the maintenance of control over selected properties of the distillate.
• These selected properties include such physical and chemical properties as the distillate's aromaticity, boiling point, flash point, cloud point, viscosity, pour point, API gravity, freeze point, octane, PIONA, and RVP. These properties are commonly measures of a distillate's commercial or market quality as a product and hence are at times refined to herein as product quality factors or simply quality factor. It is possible to control the foregoing quality factors by controlling some or all of the tower operating variables. Examples of tower operating variables include distillates flow rates, stripping steams flow rates, reflux flow rate, feed flow rate, pumparound heat duties, tower pressure, and thermal condition of the tower feed. The exact operating variables available depend the specific configuration of the distillation unit.
• Another method to obtain values of selected quality factors of a distillate recognizes that, to a great extent, these properties are influenced by the temperature distribution in the distillation tower.
• sensors distributed in the tower obtain temperature measurements at various heights in the tower. These measurements are delivered to a processor that correlates them with values of quality factors. This correlation can be performed using a look-up table of empirically derived results, or by using a mathematical model that generates values as a function of temperature.
• the method of the invention overcomes deficiencies in the prior art by performing on-line nuclear magnetic resonance measurement (NMR) of the values of selected properties of at least one distillate from a distillation tower and, optionally, of the tower feed.
• NMR nuclear magnetic resonance measurement
• the result of this on-line measurement, together with the desired values of the selected quality factors of the distillate, are applied as inputs to an automated controller.
• the controller calculates values for tower operating variables to obtain distillates having the desired quality factors.
• a system incorporating the invention can respond rapidly and automatically to variations in the tower input feed and in the output from the distillation tower.
• NMR measurements do not rely on optical or infrared radiation, they are not affected by high opacity of the measured material. Additionally, NMR measurements are relatively stable as a function of temperature. Consequently, reliable NMR measurements can be made across the broad range of temperatures of the various distillates produced in a distillation tower.
• features of the invention may extend to practices where only the feedstock is measured, or where only one or more output fractions are measured, and one or more process variables are adjusted in response to such measurement.
• the process variables are adjusted for feed-forward control in response to measurements of feedstock material, and are adjusted for feed-back control in response to measurements of product material.
• One practice of the invention includes the steps of imposing a steady magnetic field on a hydrocarbon material involved in the distillation process, i.e., on a sample of a distillate or of the feed. With the steady magnetic field in place, an NMR sensor imposes a transient magnetic field on the distillate or feed, and measures its response to this transient magnetic field.
• An NMR sensor generally does not directly provide values of the selected quality-related properties of the distillate or of the feed. Instead, the NMR sensor provides the chemical composition of the distillate or feed.
• one practice of the invention includes the step of estimating quality factors of the measured material on the basis of its measured composition. These estimated values are then used to control tower operating variables selectively, thereby generating a distillate having desired quality factors. Examples of these quality factors include aromaticity, boiling point, flash point, cloud point, viscosity, pour point, API gravity, freeze point, octane, PIONA, RVP, or other chemical or physical properties.
• the desired product quality factors are generally dictated by competitive market forces, environmental regulations and economic factors including feed and product prices and operating costs. They may also be constrained by specific characteristics of the refinery, including equipment configuration and availability and operating constraints.
• the method of the invention can thus provide rapid measurements of the values of selected properties of a variety of products produced by a distillation tower. Because the method of the invention relies on NMR rather than on optical measuring techniques, the accuracy of these measurements can be essentially independent of the opacity or the temperature of the distillates.
• a system for practice of the invention optionally includes an optimizer for specifying desired long-term values of selected tower operating variables that produce products having the desired quality factors, while optimizing economic operation of the tower considering, among other things, feed and operating cost and product values.
• the optimizer can also take into account specific characteristics of the refinery, including equipment configuration and availability and operating constraints including product production limits.
• the system further includes one or more sensors for measuring selected properties of at least one distillate and, optionally, of the feed. These sensed values, together with desired long-term values of selected operating variables specified by the optimizer, are provided to a controller. On the basis of the sensor information, desired quality factors, and desired long-term values, the controller determines current values of the operating variables needed to form distillates having the desired quality factors, while optimizing economic performance of the tower.
• FIG. 1 is a block schematic representation of a multivariate distillation control system embodying features of the invention
• FIG. 2 is a schematic block diagram that shows details of the distillation control system of FIG. 1.
• FIG. 1 shows a multivariate control system 10 incorporating principles of the invention.
• the multivariate control system 10 includes a distillation system 11 in communication with a multivariate controller 12.
• the output of the distillation system 11 is a plurality of hydrocarbon distillates, each characterized by values of selected quality factors. These values, which are represented in FIG. 1 by an output vector y that is fed back to the multivariate controller 12, are the controlled variables for the multivariate control system 10.
• Measured values of feed quality properties, represented by vector z are fed forward into the multivariate controller.
• the multivariate control system 10 further includes an optimizer 13 for generating desired long-term values of selected tower operating variables that produce products of the desired qualities, while optimizing economic operation of the tower. These desired values are represented in FIG. 1 by a setpomt vector r generated by the optimizer 13 and supplied to the multivariate controller 12. Elements of vector r take into account specific characteristics of the refinery, including equipment configuration and availability and operating constraints including product production limits.
• the optimizer 13 is an optional enhancement of the system 10.
• Alternative practices for entering setpoint data into the controller 14 include a keyboard device for manual entry and other techniques known in the automated control field.
• the multivariate controller 12 In response to the setpoint vector r and one or more differences between desired product quality factors and the output vector y and/or the feed forward vector z, the multivariate controller 12 generates a vector of manipulated variables x that, when applied to the distillation system 11, adjusts the distillation process to change the values of the elements in the output vector y to approach the corresponding desired values.
• the illustrated distillation system 11 includes an input pipeline 14 leading to a distillation tower 15. Before reaching the tower 15, the pipeline 14 passes through a furnace 16 whose operating temperature is under the control of the multivariate controller 12. A pump 20 coupled to the input pipeline 14 propels crude oil through the furnace 16 and into the distillation tower 15.
• the distillation tower 15 is of the type commonly found in petroleum refineries. Such a distillation tower 15 typically includes a plurality of tower outputs disposed at different heights along the tower 15.
• the distillation tower 15 shown in FIG. 2 is merely illustrative and a wide variety of distillation towers can be used. Each output of the distillation tower 15 corresponds to a particular fraction distilled from the petroleum input.
• Outputs disposed at lower portions of the tower 15 correspond to heavier fractions, such as heating oil or kerosene. Outputs disposed at higher portions of the tower 15 correspond to lighter fractions, such as gasoline or naphtha.
• the distillation tower can include one or more pumparound streams 50, to remove heat from sections of the tower to adjust internal reflux and to affect the volume distillate produced at a given quality.
• the number of tower outputs and pumparound stream affects the computational burden on the multivariate controller 12, but not the subject matter of the invention. Hence, for the sake of clarity and ease of exposition.
• FIG. 2 shows only first and second tower outputs 22, 24 and one pumparound stream 50.
• a first valve 36 is connected with the first tower output 22 and controls the rate at which a first distillate is withdrawn from the distillation tower 15.
• a second valve 38 is connected with the second tower output 24 and controls the rate at which a second distillate is withdrawn from the distillation tower 15.
• First and second valve actuators 40, 42 respond to the multivariate controller 12 to control the positions of the first and second valves 36, 38 respectively.
• a first nuclear magnetic resonance (NMR) sensor 24 is coupled to the first tower output 22 and samples the first distillate, or fraction, through a first shunt tube 26.
• a second NMR sensor 28 is coupled to the second tower output 24 and samples the second distillate, or fraction, through a second shunt tube 30.
• the illustrated control system 10 includes a third NMR sensor 51 coupled to the tower feed through a shunt tube 52.
• the distillation system 11 illustrated in FIG. 2 shows three distinct NMR sensors 24, 28, 51, it will be appreciated that a single NMR sensor can be used, on a time-shared basis, for both the first and second tower outputs 22, 24, and the feed 14.
• the sensed information output from the NMR sensors 24, 28, 51 are provided to calibrators 32, 34, 53.
• the calibrators are preferably chemometric modeling units and transform the outputs of the NMR sensors 24, 28, 51 into a format suitable for the multivariate controller 12 to which they are connected.
• One preferred sensor employs the technology of the I/A Series® Process NMR equipment available from The Foxboro Company of Foxboro, Massachusetts, however, a wide variety of NMR sensors may be used.
• a chemometric modeling unit for each calibrator 32, 24, 53 and suitable for practice of the invention is preferably implemented by a digital processor executing instructions for estimating values of selected physical properties on the basis of the measured hydrogen chemistry of a sample. These instructions implement procedures well-known to those of ordinary skill in the art. Such procedures include establishing look-up tables, interpolating between values in a look-up table, and implementing mathematical models for estimating values of the selected properties.
• Each chemometric modeling unit can be local to one NMR sensor as shown in FIG. 2. In one alternative practice of the invention (not shown), the NMR sensors in the distillation system 11 share a common chemometric modelling unit, on a time-sharing basis.
• Both the optimizer 13 and the multivariate controller 12 preferably are implemented as software instructions executed on a programmable digital processor. In practice, these instructions are executed on a general purpose digital computer. However, particularly to meet demanding performance requirements, the optimizer 13 and the multivariate controller 12 can be implemented with application specific integrated circuits.
• a suitable optimizer and multivariate controller for practice of the invention are sold by Simulation Sciences of Brea, California under the names ROMeoTM and ConnoisseurTM, respectively.
• the specific implementation of the multivariate controller 12 and the optimizer 13 are within the level of skill in the art and do not affect the scope of this invention.
• the pump 20 propels crude oil through the furnace 16 and into the distillation tower 15, where it separates into a plurality of distillates. These distillates exit from the distillation tower 15 through a plurality of tower outputs, two of which are shown in FIG. 2.
• the first distillate flows to the first valve 36, and, to the extent that the first valve is open, out of the system.
• a sample of the first distillate from the first tower output 22 is directed to the first NMR sensor 24 through the first shunt tube 26.
• the flow rate of the first distillate is thus under the control of the first valve 36.
• the second distillate flows to the second valve 38 and, to the extent the second valve is open, out of the system.
• a sample of the second distillate from the second tower output 24 is directed to the second NMR sensor 28 through the second shunt tube 30.
• the illustrated system 10 embodies the optional feature whereby a sample of the input feed stock is directed to the feed NMR sensor, 51, through shunt tube 52, and is returned to the feed stream.
• the NMR sensor 24 imposes a steady magnetic field on the sample from the first distillate to align the magnetic dipole moments associated with the molecules in the sample. With the steady magnetic field in place, the NMR sensor 24 imposes a transient magnetic field having a direction different from, and preferably orthogonal to, that of a steady magnetic field. This transient magnetic field temporarily aligns the magnetic dipoles of the sample in a direction other than that in which the static magnetic field aligns them. When the transient magnetic field is turned off, the dipoles in the sample spring back into the alignment imposed upon them by the steady magnetic field. As they do so, the dipoles generate an RF signal.
• the rate at which a particular dipole springs back to alignment with the steady magnetic field, and hence the frequency of the resulting RF signal, is characteristic of the sample's molecular structure.
• the RF spectrum thus generated, and which the NMR sensor 24 detects, provides a way of determining the chemical composition of the sample.
• the NMR sensor 24 thus provides information on the chemical composition of the distillate from the first tower output 22. It is known in the art to predict the corresponding values of selected properties from this measured composition of the sample. This operation of converting the measured sample composition into values of selected properties is carried out by a first calibrator 32, preferably a chemometric modeling unit 32 in communication with both the multivariate controller 12 and the NMR sensor 24.
• the input information to the first chemometric modelling unit 32 is the chemical composition of the sample as measured by the NMR sensor 24.
• the output of the first chemometric modelling unit 32 is a corresponding set of measured values of the selected properties.
• the second and third NMR sensors 28 and 51 and the associated calibrator 34, 53 operate in an identical manner to supply, to the multivariate controller 12, information concerning the properties of the second distillate and of the feed.
• the multivariate controller 12 determines the values of manipulated operating variables of the illustrated distillation system 11. These determined values minimize the differences between the desired values of the product quality factors and actual values of the corresponding selected properties, as provided by the first and second NMR sensors, 24, 28 operating in conjunction with the first and second chemometric modelling units or other calibrators 32 and 34. Desired long-term values of the operating variables, as determined by the optimize 13, are also taken into account by the controller 12.
• the feed NMR 51 operating in conjunction with its chemometric modeling unit, 53, provides the controller 12 information on feed quality, so that adjustments to tower operation can be made before the effect of feed variation becomes evident in the product.
• This feed forward capability is important in managing feed stock changes from one crude type to another.
• the controller 12 determines values of the manipulated variables using methods known in the art. These methods typically include reference to a look-up table and the implementation of empirically derived dynamic mathematical models.
• the multivariate controller 12 transmits control signals to the first and second actuators 40, 42. These actuators 40, 42 then selectively adjust the first and second valves 36, 38 to adjust the flow rates of the distillates from the first and second outputs 22, 24 of the distillation tower 15.
• the multivariate controller 12 can provide a control signal to the furnace 16, for controlling the temperature at which crude oil enters the distillation tower 15.
• the controller 12 can, in like manner, adjust any of numerous other operating variables, as known in the art.

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• Chemical & Material Sciences (AREA)
• Organic Chemistry (AREA)
• Chemical Kinetics & Catalysis (AREA)
• Engineering & Computer Science (AREA)
• Oil, Petroleum & Natural Gas (AREA)
• Automation & Control Theory (AREA)
• General Chemical & Material Sciences (AREA)
• Production Of Liquid Hydrocarbon Mixture For Refining Petroleum (AREA)
• Vaporization, Distillation, Condensation, Sublimation, And Cold Traps (AREA)
• Physical Or Chemical Processes And Apparatus (AREA)
• Feedback Control In General (AREA)

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  • 2001-01-12 WO PCT/US2001/001049 patent/WO2001051588A1/en not_active Application Discontinuation
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