WO2001008054A2 - Procede et systeme de conception de procedes - Google Patents

Procede et systeme de conception de procedes Download PDF

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Publication number
WO2001008054A2
WO2001008054A2 PCT/US2000/020449 US0020449W WO0108054A2 WO 2001008054 A2 WO2001008054 A2 WO 2001008054A2 US 0020449 W US0020449 W US 0020449W WO 0108054 A2 WO0108054 A2 WO 0108054A2
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Prior art keywords
linear
linear equations
units
linear program
computerized system
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PCT/US2000/020449
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English (en)
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WO2001008054A3 (fr
Inventor
Murali V. Ariyapadi
Vijay M. Barreto
Subhas C. Pal
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Raytheon Company
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Priority to AU66102/00A priority Critical patent/AU6610200A/en
Publication of WO2001008054A2 publication Critical patent/WO2001008054A2/fr
Publication of WO2001008054A3 publication Critical patent/WO2001008054A3/fr

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Classifications

    • 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
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • 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], 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], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31343—Design of factory, manufacturing system control
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31467—Display of operating conditions of machines, workcells, selected programs
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31472—Graphical display of process
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31474—Icon display for quick access of detailed information
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/36—Nc in input of data, input key till input tape
    • G05B2219/36143—Use of icon to represent a function, part of program
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • This invention relates generally to industrial processes and more particularly to a method and system for process design.
  • Such products include consumer goods, for example, gasoline and motor oil, as well as industrial goods, for example, industrial chemicals .
  • Linear program-based models refer to models of a system that consists of a linear set of equations that are solved for an optimal solution. In addition to utilizing linear program models for optimizing the operation of existing plants, linear program models are also used for planning purposes, including potential modifications to an existing plant or building a new facility. Although linear program-based models are used to some extent, conventional design techniques using available linear program-based models are more time-consuming than desired and do not necessarily provide sufficient information to allow a designer to readily assess the relative desirability of a plurality of design options.
  • the present invention provides an apparatus and method for determining the design for a process that addresses shortcomings of prior systems and methods .
  • a computerized system for facilitating design of a manufacturing process system having a plurality of process units includes a linear program model, a processor, and a memory accessible by the processor.
  • the linear program model forms a plurality of linear equations describing constraints on the manufacturing process system.
  • the linear equations include as variables price information associated with at least one input product and at least one output product of at least one of the process units.
  • the computerized system also includes a linear program engine stored on the memory and operable to be executed on the processor.
  • the linear program engine is further operable to generate an optimal solution for the system of linear equations .
  • a computerized method for designing a manufacturing process having a plurality of units includes providing to a linear program engine data representing a plurality of linear equations describing constraints on the manufacturing process, the linear equations including, as a variable, energy flow information associated with at least one of the plurality of units.
  • the method also includes optimizing based on a predetermined criteria the linear equations using the linear program engine. In response to the optimization, a description of the manufacturing process is generated.
  • a computerized system for facilitating design of a manufacturing process system having a plurality of process units includes a processor, a memory accessible by the processor, and a linear program model.
  • the linear program model forms a plurality of linear equations describing constraints on the manufacturing process system.
  • the linear equations include discrete capital cost information for at least one of the plurality of process units.
  • the discrete capital cost information includes data representing quantified capital costs associated with redundant process units.
  • the system also includes a linear program engine stored in the memory and operable to be executed on the processor and further operable to generate an optimal solution for a system of linear equations.
  • Embodiments of the invention provide numerous technical advantages. For example, in one embodiment of the invention, a system is provided that allows rapid evaluations of a number of alternative process configurations. Because of this rapid evaluation, additional alternative scenarios can be considered. Further evaluations are performed with significant savings in man-hours and project costs. According to the same embodiment, competing technologies for various process steps may be evaluated and compared.
  • teachings of the invention may be used in the design of a variety of systems, applications to which the invention is particularly pertinent include determining efficient routes to interlink intermediate streams produced within refinery process units and to processes that convert a gas stream to liquid products.
  • the invention may also be used in determining efficient routes for utilization of utilities and energy streams such as in gas to liquid conversion processes.
  • FIGURE 1A is a block diagram of an example industrial process
  • FIGURE IB is a conceptual block diagram of the process of FIGURE 1A;
  • FIGURE 2A is a block diagram showing steps of a process for converting a feed into intermediate and final products;
  • FIGURE 2B is a block diagram showing a particular selection of steps to be performed in converting a feed to intermediate and final products
  • FIGURE 2C is an alternative process to that of FIGURE 2B for converting feed to intermediate and final products
  • FIGURE 3 is a block diagram illustrating a computer system for automatically generating and evaluating an industrial process and for providing a preferred solution for the process according to the teachings of the invention
  • FIGURE 4 is a block diagram of a portion of the system of FIGURE 3 illustrating additional details of particular modeling techniques according to the teachings of the inventory;
  • FIGURE 5 is a block diagram of a spreadsheet for use as an interface in the computer system of FIGURE 3;
  • FIGURE 6 is a flow chart illustrating a method for utilizing the system of FIGURE 3 to obtain a design for a plant process
  • FIGURE 7 is a graph illustrating that the heat in a reactor effluent stream may be split into many energy streams;
  • FIGURE 8 is a flow chart illustrating an example reactor model
  • FIGURES 9A through 9C are block diagrams illustrating energy sinks
  • FIGURES 10A through IOC are block diagrams illustrating an example energy utilization scenario
  • FIGURE 11 is a block diagram illustrating a utility balance incorporated into the system of FIGURE 3;
  • FIGURE 12 is a block diagram illustrating example utility producers and utility users for an example manufacturing process
  • FIGURE 13 shows cost curves generated according to both conventional methods and according to the process of FIGURE 14;
  • FIGURE 14 is a flow chart illustrating the generation of the cost database illustrated in FIGURE 3; and FIGURES 15A through 15C are alternative processing steps for the system shown in FIGURE 1A that were generated and compared by the system of FIGURE 3.
  • FIGURES 1 through 15C of the drawings like numerals being used for like and corresponding parts of the various drawings .
  • FIGURE 1A is a block diagram illustrating an example industrial process 10 designed according to the teachings of the present invention.
  • Process 10 receives inputs 12 and produces outputs 16.
  • input 12 is raw crude and output 16 is syncrude.
  • output 16 is syncrude.
  • a plurality of processing steps 14 are performed.
  • FIGURE 1A illustrates one particular set of processing steps 14, other processing steps could be used to generate syncrude 16 from raw crude 12.
  • the number of processing steps 14 may be numerous, resulting in great complexity in comparing the desirability of one set of processing steps 14 to an alternative set of processing steps 14.
  • a computer system that automatically evaluates a number of relevant variables and generates a desirable set of processing steps 14 based on a given criteria.
  • the desired criteria is internal rate of return.
  • Internal rate of return refers to the economic rate of return on the investment and operating costs of a given process 10 as realized from the generation of outputs 16.
  • FIGURE IB a simplified block diagram of FIGURE 1A is illustrated in FIGURE IB.
  • process 10 receives crude feeds 12 as inputs and generates syncrude 16 as an output.
  • a plurality of units associated with processing steps 14 are illustrated.
  • a front end distillation unit 18 receives crude feed 12.
  • Front end distillation unit 18 provides intermediate product 40 to primary upgrading units 22 and intermediate product 30 to syncrude and distillates pool 50. Pool 50 collects syncrude and distillates.
  • Front-end distillation unit 18 also generates a diluent recycle as denoted by reference numeral 28.
  • Primary upgrading units 22 perform various functions related to processing intermediate product 40, advancing the final product toward syncrude.
  • Primary upgrading units 22 cooperate with auxiliary units 20 as denoted by reference numerals 32 and 34 to generate intermediate products 36 and 38.
  • auxiliary units 20 cooperate with secondary upgrading units 24 as denoted by arrows 42 and 44 to generate intermediate products and distillates 46.
  • Primary upgrading units produce intermediate products and distillates 38 that are provided to syncrude and distillates pool 50.
  • utilities and offsites unit 26 representing utilities associated with the described processing units and additional units that do not directly receive feed or intermediate products.
  • Process 10 results in syncrude 16 from syncrude/distillates pool 50.
  • FIGURE IB A number of alternative processing steps may be represented by process 10 illustrated in FIGURE IB.
  • the present invention allows selection of an optimal process 10. The selection of a particular process is described in conjunction with FIGURE 15A below and its desirability over other alternative processes is described in conjunction with FIGURES 15B and 15C.
  • FIGURE 2A is an illustration of a complex processing scheme 56 that requires many processing steps to convert a feed 52 to intermediate and/or final products 54.
  • Each block 58, 60, 62, 64, 66, 68, 70, 72, 74, 76 represent individual processing steps.
  • the method of the present invention determines a preferred integrated route interconnecting individual processing steps including interactions between the processes and different technologies that maximizes a desired criteria. In the described examples, that criteria is the internal rate of return and/or the best configuration for a certain set of economic parameters.
  • FIGURE 2B and 2C show examples of two possible ways,
  • FIGURE 2B a processing step 80, a processing step 82, and a processing step 86 are performed, resulting in intermediate and final products 54.
  • FIGURE 2C utilizes steps 80, 84, and 86 resulting in intermediate and final products 54.
  • alternative flows of intermediate products such as intermediate products 88, 90, 92, 94, 101, 110, 112, 116, 114, and 118 are utilized.
  • Actual complex process plants generally incorporate greater complexity in numerous possible processing steps and competing combinations of processing routes, making the route selection process very complex and time consuming. The present invention addresses this problem.
  • FIGURE 3 illustrates a system 120 for modeling and optimizing processes such as those described in FIGURES 1A through 2C.
  • system 120 generates a preferred solution quicker than conventional methods and additionally considers factors that result in more accurate determinations of the economically optimal solution. 12
  • System 120 includes a linear program engine 122 and an interface 12 .
  • Stored for use with the linear program engine 122 is a linear program model 125 and a project results output 130.
  • Interface 124 allows both automatic and manual communication with linear program model 125.
  • Linear program model 125 forms a set of linear equations as inputs to linear program engine 122 that define a process to be optimized.
  • Linear program engine 122 is operable to solve those equations for a preferred solution, or "program.” Suitable examples of linear program application software include Process Industry Modeling System (PIMS) marketed by Aspen Technology, Inc., Refinery Planning in Modeling System (RPMS) by Bonner & Moore, and Generalized Refinery Transportation Marketing Planning System (GRTMPS) by Haverly Systems, Inc.; however, other suitable systems may be used. All of these application software include a linear program engine 122 such as CPLEX and a language to construct the LP model. Linear program engine 122 is implemented through use of such an application software executed on a processor having an associated memory for storing the software application as well as other information.
  • PIMS Process Industry Modeling System
  • RPMS Refinery Planning in Modeling System
  • GRTMPS Generalized Refinery Transportation Marketing Planning System
  • All of these application software include a linear program engine 122 such as CPLEX and a language to construct the LP model. Linear program engine 122 is implemented through use of such an application software
  • Linear program engine 122 receives inputs from linear program model 125 and provides outputs through user interface 124. Inputs may include feed and product description and prices 128, constraints 134, process submodels 132, capital investment cost curves 135, and unique modeling techniques 136. In one embodiment, these inputs are provided in the form of a plurality of spreadsheets. Linear programming engine 122 may also generate project results report 130 providing a determination of a preferred solution based upon the provided inputs. Information is placed into linear program model 125 in a format suitable for execution by linear program engine 122 by interface 124 and in particular, by use of spreadsheets 410 and smart links 126, described below.
  • FIGURE 4 is a block diagram of unique modeling techniques 136.
  • Unique modeling techniques include intermediate product pricing 510, energy integration 520, utility balances and integration 530, linear and nonlinear mathematical models to calculate yields and properties 540, estimation of capital costs and operating costs inclusive of inflation 550, and calculation of major economic parameters for project evaluation 560. Each of these modeling techniques is described in greater detail below.
  • interface 124 receives input information for generating linear program model 125 from a number of sources. These sources include spreadsheets 410, inputs 138, smart spreadsheets 142, a cost database 144, an operating data/licensor information database 146, an external process simulation results 148, a utility database 150, and process models 152. Receipt of input information by interface 124 is described in greater detail below in conjunction with FIGURE 5.
  • Interface 124 also receives project results report 130 generated by linear program engine 122 and provides the report to the user as output 140, as described in greater detail in conjunction with FIGURE 5.
  • Interface 124 also includes a plurality of smart links 126.
  • Smart links 126 provide an efficient way for linear program engine 122 to receive information as inputs on Smartlinks 126 are macros that update information within linear program model 125 with information provided by a user in spreadsheets 410.
  • interface 124 incorporates Excel spreadsheets 410, illustrated best in FIGURE 5, for allowing a user to easily modify linear program model 125.
  • Spreadsheets 410 includes locations for storing three types of information: input information 412, output information 414, and help information 416.
  • the following input windows are provided: a problem description 418, a crude selection 420, process units 422, tank storage 424, product specifications 426, product requirements 428, feed and product pricing 430, and economic parameters 432. Details of each of these categories of information and the input of such information into spreadsheets 410 is described in greater detail below.
  • Problem description window 418 is a summary description of the problem to be solved.
  • the summary description is generated or modified by a user of system
  • Crude selection window 420 is used to select the feed that is converted by the process to be designed into a final end product. All crudes currently available are presented to the user. The user may modify the minimum and maximum throughput constraints for the crude or mix of crudes fed to the refinery and may specify an Overall Onstream Factor for an entire refinery. Overall Onstream Factor refers to number of operating days in a year (less than or equal to 365) . Based on the constraints, the feed is calculated by spreadsheets 410. Crude selection window 420 also allows specification by the user of crude price and diluent selection. Diluents are primarily utilized with extra heavy crudes to facilitate transportation and handling. After the desired information is provided within crude selection window 420, an "UPDATE" function allows smart links 126 to modify linear program model 125 with the provided information for optimization by linear program engine 122.
  • UPDATE UPDATE
  • Process units window 422 allows entry of information for process units in three categories: (1) distillation/primary upgrading units, (2) secondary upgrading/finishing units, and (3) auxiliary units. These categories of process units correspond to primary upgrading units 22, secondary upgrading units 24, and auxiliary units 20, respectively, illustrated in FIGURE IB. Each category of process units is associated with an input table that allows the user to specify minimum and maximum throughput constraints for each unit. The user also typically specifies if there is any existing capacity available for these units. Individual unit onstream and design factors may also be entered. An "UPDATE" function allows smart links 126 to modify linear program model 125 with the provided information for optimization by linear program engine 122.
  • Tank storage window 424 allows a user to specify either the number of days of storage or the total storage capacity for each of three types of storage: feed, product, and intermediate storage. Additionally, the type of tanks and any existing storage capacity available can be specified.
  • An "UPDATE” function allows smart links 126 to modify linear program model 125 with the provided information for optimization by linear program engine 122.
  • Product specification window 426 allows the user to specify product specifications. For example, product specifications for a typical refinery are divided into a number of major categories: LPG, gasoline, kerosene, diesel, fuel oil, and syncrude.
  • An "UPDATE" function allows smart links 126 to modify in linear program model
  • linear program engine 125 with the provided information for transmission to linear program engine 122.
  • Product requirements window 428 allows the user to specify the minimum and maximum production required.
  • UPDATE allows smart links 126 to modify linear program model 125 with the specified information for transmission to linear program engine 122.
  • Feed and product pricing window 430 allows the user to make changes to the feed, product, and utilities prices.
  • the price of crude is specified in a "CRUDE
  • Economic parameters window 432 allows the user to define overall economic parameters that govern the given process configuration of a given process complex. These parameters include the number of years required for capital recovery and the number of operating days in the year. The user may specify the number of years, and the number of operating days in the year is determined by spreadsheets 410 based on the Overall Onstream Factor defined in crude selection window 420. The user may also select a geographical cost basis for the project and construction year, impose a maximum allowable Total Investment Cost. The economic parameters also provides for apportionment of labor cost, dependent on site and craft labor availability, in construction of such facilities. Additional factors may also be included in this window, including inflation factors, type of depreciation and tax rate.
  • An "UPDATE" function allows smart links 126 to modify linear program model 125 with the specified information for transmission to linear program engine 122.
  • Block flow diagram window 436 displays a block flow diagram of the designed processing complex. The distribution of all streams going through the distillation units, primary upgrading units, secondary upgrading units, and finishing units is illustrated. Auxiliary units are also displayed. A second block flow diagram showing gas plants and some additional finishing units may be accessed from this window. Inter unit transfer flows and unit capacities are displayed.
  • Capacity utilization/total investment cost window 438 provides information related to capacity utilization and Total Investment Cost of each unit and system and is organized into five or more components. Typically, for a refinery complex they are: Total Investment Cost Summary, Distillation/Primary Upgrading Units, Secondary Upgrading/Finishing Units, Auxiliary Units, and Offsites and Misc. Total Investment Cost Summary provides the user an overall view of the Total Investment Cost for each block of process unit types. The other components provide more detailed information for each individual unit . Finished products window 440 is divided into product categories from the complex. For example: Product Summary, Gasoline grades, Kerosene/Jet Fuel, Diesel grades/Fuel Oil grades, and Syncrude. Product Summary is a summary of the production constraints and total production. The other categories include detailed information on the composition of each blend.
  • Utility/Summary 442 includes four summary tables: Overall Utility Balance, Distillation/Primary Upgrading Units, Secondary Upgrading/Finishing Units, and Auxiliary Units.
  • the Overall Utility Balance table summarizes total utilities consumed by the designed refinery.
  • the other tables show the utilities consumed by each unit of the designed refinery.
  • Economic analysis window 444 contains estimated Total Investment Cost for the designed processing complex, the operating costs and revenue, and the project's internal rate of return. This information is automatically retrieved from results/output report 130 generated by linear program engine 122. Help location 416 stores help information related to system 120.
  • interface 124 In generating linear program model 125, interface 124 also communicates with a number of databases, including cost database 144, operating detail licensor information database 146, process simulation database 148, utility database 150, and process models database 152 in addition to communicating with smart spreadsheets 142. Interface 124 also communicates with a cost database 144. Cost database 144 includes information related to the cost of units and systems and is described in greater detail below in conjunction with FIGURES 13 and 14. Interface 124 also communicates with an operating data and licensor information database 146. This communication allows estimation of operating cost of each unit/system. The database can be updated with information from technology licensors . Interface 124 also communicates with utility database
  • Utility database 150 allows estimation of utility requirement for each unit and/or system.
  • FIGURE 6 is a flow chart illustrating a method for utilizing system 120 of FIGURE 3 to obtain a design for a plant process. The method begins at step 162. Initially, the nature of the project and project objectives are defined. At a step 164 a determination is made of whether the design is a grassroots design, which refers to an initial design as opposed to a redesign of an existing system, or whether the design involves revamp of an existing facility. If a grassroots design is required, a determination is made at step 166 of whether customization is required.
  • a basic default model for linear program model 125 includes a number of processing steps as well as yield data, product properties, utilities requirement, and capital and operating costs for each processing step.
  • Customization refers to defining new process units or utilizing more accurate yield or operating data that may be available. If customization is not required at step 168, interface 124 is utilized to define linear program model 125 as described above. At step 170, linear program engine 122 determines a preferred solution for the project process based on the provided inputs. This preferred result is based on maximizing a desired criteria, which in this example is the internal rate of return of the project. At steps 172 and 174 the results are obtained and provided through interface 124 as output 140. The method concludes at step 182.
  • the process is illustrated by reference numeral 176.
  • information is gathered about the facility such as process units, yield information, operating conditions, bottlenecks, design capacities, and other relevant details of the plant.
  • a model of the facility is customized and provided to linear program model 125. Customization refers to defining new process units or utilizing more accurate yield or operating data that may be available. Customization may also be performed for a grassroots model at step 180. Processing continues at steps 168, 170, 172, 174, and 182 for the revamp scenario in a similar manner to that of a grassroots design.
  • FIGURE 3 a number of innovative modeling techniques are provided that enhance the speed and accuracy of system 120. These techniques are generally illustrated in FIGURE 3 as unique modeling techniques 136. Referring to FIGURE 4, these techniques include intermediate product pricing 510, energy integration 520, utility balances and integration 530, linear and nonlinear mathematical models to calculate yields and properties 540, estimates of capital costs and operating costs inclusive of inflation 550, and calculation of major economic parameter project evaluation 560. These techniques are described in greater detail below.
  • One aspect of the invention utilizes intermediate product pricing 510 that contributes to more accurate modeling.
  • intermediate product processing is described below.
  • a refinery or other similar processing facility generally produces finished products, such as syncrude 16, for market sale or intermediate products that serve as feeds to other process plants, or both.
  • feed and product prices are provided to a linear programming model.
  • intermediate product quality often varies with each of the possible processing options.
  • some refineries and upgraders produce a liquid blend such as synthetic crude.
  • the quality of synthetic crude produced such as sulfur, nitrogen, boiling range, gravity, etc. will depend upon the processing route used, and hence its value cannot be determined a priori and specified in a linear program model.
  • linear program model 125 generates the price of intermediate products based on their quality.
  • the price of intermediate product streams is treated as a variable that is calculated by linear program engine 122. This allows varying the price of final products during optimization as the properties of the final products change .
  • a front-end unit consumes the primary raw material to produce three intermediate products, LI, L2 and L3.
  • the qualities of these intermediate products are different from one another.
  • These intermediate products can be transferred, for example, to a syncrude product blending unit or to another processing unit.
  • Linear program model 125 to be run on linear program engine 122, is designed such that each unit in a model has the option to accept a feed stream from a number of possible upstream or downstream (as recycle) units, process the feed for sale to other units.
  • intermediate product price as a variable within the process model variations in the intermediate and final product price based upon variations in the underlying process may be incorporated, resulting in more accurate modeling.
  • the use of such a modeling technique is graphically depicted by unique modeling techniques 136 in FIGURE 3.
  • the price of the syncrude product is internally calculated by considering the intermediate stream price and along with flow rates to, for example, the syncrude blend.
  • the intermediate price equations developed are functions of stream bulk properties such as sulfur, nitrogen, specific gravity, conradson carbon residue, unsaturation content, etc. These equations, which are shown below, are developed by considering the cost (capital, operating, profit margin etc. required for processing a given stream to finished product (s) .
  • Linear programming models are used to maximize an objective function, which is normally the overall profit margin of the refinery, petrochemical plant, etc.
  • the objective function can be expressed by the following formulae :
  • Price a x * (P ⁇ Pn,) +a 2 * (P 2 -P 2b ) + ... a n * (P n -P nb )
  • a 1 _ n coefficient with units of $/property P- L . ⁇ property of stream
  • a unit price balance is established. This is achieved by charging each process unit for the input feed, and selling the output products to another unit. For example, a delayed coking unit is fed vacuum resid priced at one dollar a barrel. Linear program engine 122 internally calculates the price of the vacuum resid, and charges
  • SPDx Price determination table "x" unit
  • the user does not have to provide the price of one or more products of the plant, but rather such prices are treated as variables.
  • the model will use the prices of the products that are provided and calculate the prices of those products for which prices are not provided.
  • FIGURE 4 Another aspect of the invention is optimization of energy utilization in the modeling process, depicted by reference numeral 520 in FIGURE 4.
  • Processes such as those available to convert gas to liquid products (GTL) are energy intensive. Their success and marketability depends on how effectively energy is integrated and managed.
  • the effluent stream from a reactor is modeled as a mass effluent stream and a separate energy stream.
  • This allows the "energy stream” to be directed into other process units that need energy, as a raw material, or sent to steam or power generation units.
  • a very significant amount of energy stream is produced in the front end reforming unit that needs to be integrated with energy consumers in the processing complex and excess used for steam power generation. Effective energy integration between various process units and generation of export steam and/or power with the excess energy is a key for the success of gas-to- liquid processes.
  • the GTL and IGCC processes typically have a front-end reactor (gasifer) to produce synthesis gas from: natural or associated gas, refinery resid streams, heavy oil feeds, and solid feeds such as Coal and petroleum coke. Air or oxygen is supplied as another feed.
  • a front-end reactor gasifer
  • the temperature of the feed(s) is an input in linear program model 125 and the feed characteristics such as carbon to hydrogen ratio, sulfur and nitrogen content, etc. can be inputs (external feed) or can be generated by the engine 122 (internal feed) .
  • the product, known as syngas, leaves the reactor at a very high temperature. This stream is an excellent source of very high level heat that can be recovered.
  • the composition and heat contained in this reactor effluent stream is a function of temperature of the feed(s), feed properties, and reactor operating conditions (such as temperature) .
  • the heat contained in the reactor effluent stream is:
  • QQ ⁇ [ ⁇ miCp, ⁇ Ti-T f ) Where T ⁇ is the reactor exit temperature and T f is the temperature to which the effluent stream should be cooled.
  • the heat in the reactor effluent stream is split into many energy streams, as illustrated in FIGURE 7.
  • QQ1 [ ⁇ m ⁇ C Pi ] (T ⁇ )
  • QQ2 [ ⁇ ittiCp (t 1 -t 2 )
  • QQN [ ⁇ m.Cp (t n -l -t n )
  • QQF [ ⁇ m ⁇ Cp ⁇ ] (t f -l -T f )
  • Latent heat of vaporization can also be added to the above sets of equations when phase change occurs during cooling.
  • These energy streams are calculated by linear program engine 122.
  • the number of energy cuts (QQ1, QQ2 , ••• / QQN) and their sizes (temperatures t l t 2 . ...,) are predetermined by the user.
  • These energy streams are calculated by linear program engine 122 and made available to other users in the form similar to that of a material stream.
  • FIGURE 8 illustrates an example reactor model 810.
  • Reactor model 810 includes a reactor block 812, a gas cleaning unit 814, a gas turbine block 816, and a heat recovery steam generation block 818.
  • the products from the reactor are illustrated in FIGURE 8 as : the reactor effluent-Mass stream; QQl-Energy stream; QQ2-Energy stream; and QQN-Energy stream.
  • FIGURES 9A through 9C illustrate energy sinks made available for users of those heat sources. These are: cold streams requiring heating (preheating) , stream generation, and final heat rejection to air or cooling water.
  • FIGURES 10A, 10B, and 10C illustrate an energy utilization scenario.
  • the energy streams can produce high pressure steam 950, low pressure steam 952, and reject heat
  • the 954 to a cooling medium such as air or cooling water.
  • the 954 to a cooling medium such as air or cooling water.
  • the objective here is to determine an economically optimal way to distribute the available heat to these three energy sinks .
  • Q HP ⁇ HP - BET .
  • H HP - sf ⁇ Specific enthalpy (ex: BTU/LB) required to super preheat the boiler feed water
  • the enthalpy values are either entered into the linear program model 125 by the user or can be calculated using formula or data interpolation.
  • M HP _ BFW , M HP and M HPS can all be equal or different.
  • the first term represents the sensible heat addition to heat the boiler feed water to the saturation temperature.
  • the second term is the heat of vaporization required to convert saturated water to steam and the third term provides the heat required to super heat the saturated steam.
  • Linear program engine 122 calculates the optimal values for M HP _ BFW , M HP and M HPS that satisfy the energy balance equation listed above, (A) .
  • T ⁇ p is specified in the linear program model 125. If this condition is not satisfied, then stream QQN will not be available for generating high pressure steam 950.
  • the cost of the steam generation is a function of the heat level and hence the temperature driving force available to it.
  • a steam generator cost curve was generated and input into the linear program model 125 as described below in conjunction with the description of cost database 144.
  • linear program engine 125 calculates the cost using the following equation:
  • the steam generator cost ((QQl*Tlm + QQ2 * T2m + ..) /Base Duty) ⁇ n * TIC-Base.
  • n is typically between 0.6 - 1.0.
  • Linear program engine 122 performs a similar analysis for the steam generator and the final cooling steps and maximize the profit considering the operating costs and revenues generated by each of the blocks.
  • linear program model 125 energy flow is represented by variables in linear program model 125, allowing efficient distribution of energy to be considered as a factor in linear program engine 122 selecting an optimal process design.
  • Another aspect of the invention involves utility balances and integration 530, illustrated in FIGURE 4.
  • Conventional linear program models developed for process plants do not include global balances for all utilities. To facilitate description, conventional modeling techniques are described in detail below.
  • Step 1 In conventional linear program models developed for process plants, utilities requirements for every processing step are provided to calculate total utilities requirements. Also price for each of the utility (assuming that utilities purchased rather than produced within the processing plant) are provided in the model.
  • Step 2 While the linear program engine is running, total utilities requirement (ex: raw water, steam, cooling water, etc.) for the configuration is calculated. These requirements are then treated as if they are purchased rather than being generated within the plant (which is the actuality) . In other words utilities are treated as operating costs (ex: quantity of steam required X cost per quantity of steam)
  • Step 3 Utility balances are performed outside of the linear program model to calculate the system sizes. Capital investment costs are then estimated for the utility systems using these developed system sizes. Step 4 : These capital investment costs are added to those of the processing unit investment costs to develop the total capital costs. This is used to determine the project internal rate of return (IRR) and to compare with alternate scenarios .
  • IRR project internal rate of return
  • Utility balances are performed outside of the model manually and a cost factor applied to determine investment cost.
  • This approach in general, is very time consuming and does not allow the user to look at the overall picture to perform a global optimization. Further, this approach is inaccurate and results in an unreliable forecast of investment costs as a result of building excessive design factors in the utility systems to account for the uncertainty in the estimate.
  • a process 230 of FIGURE 11 includes process units 244, water systems 246, fuel gas system 250, and substation 252. According to a utility balance approach, utility inputs and outputs of each of these process units is balanced. Thus, inputs including water 234, natural gas 236, and power 238 are balanced with outputs of associated process units. Thus, the fact that some process units generate utilities is considered. Those outputs include process water 254, steam 256, cooling water 258, fuel gas 260, and power 262. Major utility requirements for all the processing units are input into linear program model 125. Linear program engine 122 then performs a balance to estimate the capacity requirements for each of the utility systems and calculates the net purchases required from outside sources.
  • a water balance is performed considering the process water usage and steam and cooling water blow downs to estimate the net water requirements.
  • a similar balance approach is used for fuel gas and power as well.
  • This approach allows linear program engine 122 to estimate capital costs for the utility systems suitable for comparison purposes and operating costs for the purchase of utilities such as raw water, natural gas and power from outside sources.
  • Capital cost curves are stored in the LP model 122.
  • the generation of utilities by process units is taken into account in determining net utility requirements .
  • Step 1 Major utility requirements for all the processing units are input into linear program model 125.
  • Step 2 Linear program engine 122 calculates the net steam requirements for a configuration, according to the following formula.
  • HPS Reqd. HPS Reqd. for process units - HPS produced in process units.
  • Net MPS Reqd. MPS Reqd. for process units - MPS produced in process units.
  • Net LPS Reqd. LPS Reqd. for process units - LPS produced in process units where, HPS, MPS and LPS indicate high, medium and low pressure steams respectively. The concept is easily extended to any number of steam levels .
  • Step 3 The net steam required should be supplied by the steam generation system.
  • the steam boilers of the steam generation system generate steam only at the highest level required.
  • the BFW system receives treated raw water from the raw system and converts it into BFW to satisfy the requirement. This determines the operating capacity of the BFW System.
  • Step 8 The calculated unit capacities are then used along with the individual system over design factors and cost curves to determine the investment costs for each of the utilities systems.
  • the utility balancing approach allows linear program engine 122 to consider the investment costs for all of the utility systems in the determination of optimal processing route.
  • This approach eliminates the tedious and time consuming approach used by the conventional linear program models .
  • the above concept can be easily extended to other utility systems also.
  • linear and non-linear mathematical models 540 illustrated in FIGURE 4 are utilized to calculate yields and properties of the yields.
  • Linear programming models model only linear systems. However, actual processes are often non-linear. Such systems are typically linearized for the purpose of modeling. Linearization limits the range of usefulness of a model, and the user should be aware of such model limitations. Smart interconnected spreadsheet modeling is utilized according to one embodiment of the invention to address this problem.
  • Deasphalted oil yields are typically obtained by performing lab tests.
  • a correlation is used to estimate deasphalted oil yield as a function of feed properties such as specific gravity.
  • the properties of deasphalted oil, such as specific gravity, sulfur, nitrogen, conradson carbon and metals are non-linear functions of the deasphalted oil yield. This is difficult to model using conventional techniques.
  • a smart spreadsheet 142 is used to predict the yield and product properties for this unit.
  • This spreadsheet calculates the vacuum residue feed properties for the selected crude feed, calculates the yield and product properties, and updates linear program model 125.
  • This modeling technique also allows the user to replace the estimated values by licensor values, when available. This may be extended to model some of the other process units also.
  • Another aspect of the invention involves estimation of operating costs and capital costs 550.
  • Major operating cost parameters such as utilities, catalyst and chemical cost, running royalty, operating labor, maintenance, insurance and taxes are included in linear program model 125.
  • Tax rates are based on the region and are also included in the costs .
  • the cost of the imported utility streams is one of the components of the operating costs.
  • the utility balance approach described above helps to determine the actual utilities to be imported for a processing unit. Purchase price for each of the utility streams such as that for raw water, etc. is provided to linear program model 125. Linear program model 125 uses the internal utility balance and the pricing information to calculate this component of the operating costs.
  • FIGURE 13 shows example cost curves generated according to both conventional methods and according to the present invention.
  • Conventional cost curves are determined by obtaining the cost of the unit for a base capacity and then fitting an exponential curve that passes through the base point ( Figure 13) .
  • curve 274 in FIGURE 13 conventional cost curves developed using this approach are non-linear but smooth.
  • Cost curves are functions of the unit size, operating severity, geographical location and process complexity.
  • the cost estimate using the conventional method is not realistic as it does not reflect the minimum feasible unit size and step change in cost due to the need to use multiple trains when single train maximum capacities are exceeded. Thus the existing cost curve modeling methods may not find the optimal solution.
  • an innovative methodology and modeling system is utilized for a more complex investment cost curve format to be incorporated, as shown in FIGURE 13.
  • This method allows the model to predict more accurate capital costs taking into consideration the minimum feasible unit size and maximum capacities.
  • the cost curves 276 developed are not continuous like conventional ones, instead they have breaks 272 wherever the maximum capacity of the unit is exceeded and a second train is required.
  • the minimum capacity constraints are modeled by assigning a minimum capital cost 270 for all capacities below the constraint. This discourages linear program engine 122 from choosing unrealistic processing schemes.
  • the conventional way to determine the Rough Order of Magnitude (ROM) Total Investment Cost (TIC) of a project is to develop a cost curve, that is a nonlinear function of the unit capacity. These curves are determined by obtaining the cost of the unit for a base capacity, and then fitting an exponential curve that passes through the base point. The curve is smooth but non-linear. The cost curve is a function of the unit size, operation severity, geographical location and process complexity. The drawback of using the conventional method is that the curve built is a function of the base cost and exponent input into the model only.
  • TIC [ (Operating Capacity/Base Capacity) ⁇ (Exponent)] * Base TIC.
  • the value of the exponent is typically in the range of 0.6 to 1.0.
  • linear program model 125 is developed to accurately model the capital investment costs of all the units with the additional design margins that would at the same time not modify the actual utilities required. This is done through the use of factors that would modify the TIC to create a pseudo-TIC. This method allows the model to adjust the capital cost charge due to different operation severities, exceeding maximum process capacity constraints, and multiple trains.
  • An additional feature is that the model has built-in minimum unit capacities, in order to discourage the linear program engine 122 from choosing unrealistic processing schemes.
  • the cost curves developed are not smooth like conventional ones, instead they have breaks in them wherever the maximum capacity of the unit is exceeded and a second train is required.
  • the minimum capacity constraints are modeled by assigning a minimum capital cost for all capacities below the constraint.
  • linear program model 125 Additional features found within linear program model 125 are inflation factors and specific site cost factors. These features expand and applicability of the model to different geographic locations and time frames, since the base curves were developed for a specific location and year. Detailed Description of Cost Curves:
  • CC Capital Charge
  • Many linear program models are designed to be used on a daily basis and to optimize the daily operations of a plant or refinery. It is because of this that the capital cost of any new units or a whole grassroots facility must be entered into the matrix as a capital charge per day. This capital charge is introduced into the objective function and thus the investment required for the new units is optimized.
  • the Total Investment Cost must be divided by the number of years required by the user to recover the capital investment, and by the number of days in one year that the facility is in operation.
  • OPF Onstream Factor
  • Unit Onstream Factor Each process unit may have its respective onstream factor. This factor is a measure of how reliable a process is and how much downtime is required for activities such as periodic clean-up of certain key equipment, shutdowns of units for maintenance. This factor may be different from the overall onstream factor.
  • Design Factor A process unit may be designed with a margin above the normal operating requirements to accommodate for uncertainty in any of the design parameters. This additional margin is defined as the design factor.
  • MT Multiple Trains
  • Certain process units may be critical for the operation of the overall facility. These units are generally designed to have an additional train, to serve as a spare.
  • An example of such a unit is the stream generation unit, common practice is to provide two spare steam boilers.
  • IF Inflation Factor
  • Miscellaneous Factor The model incorporates within the overall TIC a factor for additional costs which are dependent on the size and complexity of the process units. Examples of the systems that are priced in this manner are the distributed control system, fire water system, ISBL/OSBL piping, and flare system, etc.
  • SLF Site Location Factor
  • the process units TIC is estimated for construction in the U.S. Gulf Coast. The actual plant may be built elsewhere and labor rates, and freight expenses, etc., are different to those at the U.S. Gulf Coast. An adjustment is required for changing the construction site.
  • the methodology used to developed the capital cost curves 135 for use in linear program model 125 are described below: The additional factors, described above, are provided to an LP cost curve sub-model when the user makes any changes and automatically updated within the program through smart links 126.
  • the value of the exponent is typically in the range of 0.6 -
  • the capital charge curves are generated using:
  • CC coefficient curves as a function of capacities are then developed for use in the linear program model 125.
  • An example cost curve that is stored as a part of the linear program model 125 is provided below:
  • FIGURE 14 is a flow chart illustrating the generation of cost database 144. The steps involved in the development of a cost database 144 suitable for use in system 120 are outlined here.
  • each data set consists of total investment cost, base year and geographical location for a processing block (such as crude distillation, delayed coking, etc.).
  • NCI Nelson Cost Index
  • linear program models are typically used for maximizing the revenue.
  • a time frame such as a day, or a month, or a year is selected to compute the revenue (example: $ per day).
  • process oriented industries such as refineries and petrochemical complex models typically use one day as the time frame.
  • the revenues calculated by the model are used in further economic analysis to calculate the net profit in dollars per year or internal rate of return.
  • the objective function optimized by system 120 is internal rate of return as measured by revenue per day. That means all the internal calculations, with the exception of the overall economic analysis, are typically performed on a per day basis.
  • This $/Flow capital recovery cost when multiplied by the respective unit capacity in Flow/day results in the capital recovery cost expressed in $/day for that processing block.
  • the total capital recovery ($/day) is a sum of capital recovery charges for all the processing blocks, utilities and other capital investment components.
  • the capital recovery period is a direct input to the model. Alternately, this can be calculated for a desired internal rate of return (IRR) using the help facility 416, FIGURE 5.
  • IRR internal rate of return
  • the total investment costs for various types of tanks are collected. These costs are then converted into $ TIC /BBL cost for the base year of 1998 on the Gulf Coast basis. Instead of using a fixed $/BBL value for all the capacity ranges, a cost curve can also be used to estimate the tankage cost.
  • the $/BBL investment cost is converted into $/BBL capital recovery cost, also at step 214.
  • the Total Investment Cost allowances for the following systems are also provided at step 214 as a fraction of the total processing unit costs, which are ISBL/OSBL, Infrastructure (Buildings and Sitework) , Solid and Liquid Handling. Inclusion of all the major costs in the optimization process eliminates additional external steps and iterations required for developing an optimized processing configuration.
  • a table of overall Nelson Cost Index is compiled and used to convert the investment costs to any year of interest automatically at step 216.
  • Factors are developed for every capital cost component to convert from Gulf Coast to another basis. This may be easily extended to convert the capital costs into any other Geographical location automatically.
  • modeling techniques 510, 520, 530, 540, 550, and 560 allow for more rapid and more accurate optimization of a process design.
  • FIGURES 15A, 15B, and 15C illustrate alternative processing steps for the system shown in FIGURE 1A that were generated and compared with each other by the system of FIGURE 3.
  • the generation of these alternative processes and the selection of the best alternative is described below.
  • the input required such as, crude feed, feed rate, syncrude specification and economic parameters were entered into the model using spreadsheets 410 of interface 124.
  • User interface 124 in turn, generated a linear program model 125. Linear program model was then executed.
  • Results such as, block flow diagram with stream flows, utilities summary, unit capacities and ROM capital cost breakdown (Table 6) and overall economic analysis (Table 7) were updated automatically from the output report 130.
  • System 120 selected process 300, which includes carbon rejection followed by secondary hydroprocessing as the preferred method (Case 1; FIGURE 15A) for this scenario.
  • a block diagram of this process is illustrated in FIGURE 15A and includes distillation step 304, carbon rejection step 308, and secondary hydroprocessing step 312 to result in syncrude 314 from diluted crude 302.
  • the model was used to run other predetermined alternate cases to verify the solution obtained. Two such cases are described here.
  • Case 2 (process 320; FIGURE 15B) consisted of residual thermal cracking followed by carbon rejection 328 as the primary upgrading option with secondary hydroprocessing 312 to produce syncrude.
  • Case 3 (process 340) utilized residual hydroprocessing 350 and carbon rejection 354 in parallel as the primary upgrading routes in addition to secondary hydroprocessing step 312 to produce syncrude 362 (FIGURE 15C) .
  • the results are summarized in Table 8.
  • case 1 was better than cases 2 and 3.
  • the actual quantity and quality of synthetic crude produced is dependant on the route selected. Since the quality of synthetic crude varies with the upgrading route it is difficult to assign a value ($/BBL) for it before its quality is determined.
  • conventional linear program methods require a price to be assigned to the syncrude in order for the model to perform a global optimization.
  • conventional linear program models lack a capital cost structure and also do not have the cost components defined with enough accuracy to perform studies of this nature. Due to the above reasons, conventional method would have taken significantly more time and effort and hence would have required more money to solve this complex problem.
  • VBALC 2 ETHYLENE -0.0095 -0.0007
  • VBALC3 PROPYLENE -0.0168 -0.0012
  • VBALC4 BUTYLENES -0.0164 -0.0012
  • VBALIC4 ISO-BUTANE -0.0045 -0.0003

Abstract

L'invention concerne un système informatique destiné à faciliter la conception d'un système de procédés de fabrication constitué d'une pluralité de modules de traitement. Ledit système comprend un modèle de programme linéaire, un processeur, et une mémoire à laquelle le processeur peut accéder. Le modèle de programme linéaire constitue une pluralité d'équations linéaires décrivant les contraintes régissant le système de procédés de fabrication. Ces équations linéaires comprennent des variables, en l'occurrence les informations de prix associées à un produit d'entrée au moins et à un produit de sortie au moins de l'un des modules de traitement au moins. Le système informatique selon l'invention comprend également un moteur de programme linéaire stocké dans la mémoire et s'exécutant sur la processeur. Ce moteur de programme linéaire sert, en outre, à produire une solution optimale pour le système d'équations linéaires.
PCT/US2000/020449 1999-07-27 2000-07-27 Procede et systeme de conception de procedes WO2001008054A2 (fr)

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Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2404457A (en) * 2003-07-30 2005-02-02 Fisher Rosemount Systems Inc Process control system utilising economic models
WO2005010783A1 (fr) * 2003-07-28 2005-02-03 The University Of Waikato Ameliorations apportees a un systeme et a un logiciel destines a la modelisation de la consommation d'energie
WO2005020118A1 (fr) * 2003-08-18 2005-03-03 General Electric Company Procédé et système d'évaluation et d'optimisation de la sélection de pétrole brut
EP1559030A2 (fr) * 2002-10-23 2005-08-03 Fina Technology, Inc. Procede et systeme d'exploitation d'une installation de production d'hydrocarbures
EP1794262A2 (fr) * 2004-06-18 2007-06-13 ExxonMobil Upstream Research Company Conception d'installation de traitement de fluides hydrocarbones
US7269466B2 (en) 2002-07-24 2007-09-11 Mitsubishi Denki Kabushiki Kaisha Optimal operation controller of plant
DE10335326B4 (de) * 2002-08-16 2007-09-20 Heidelberger Druckmaschinen Ag Verfahren und Vorrichtung zur Simulation von Prozessabläufen in der graphischen Industrie
US7698022B2 (en) 2006-06-23 2010-04-13 Saudi Arabian Oil Company System, method, and program product for targeting and optimal driving force distribution in energy recovery systems
US7729809B2 (en) 2006-06-23 2010-06-01 Saudi Arabian Oil Company System, method, and program product for targeting and identification of optimal process variables in constrained energy recovery systems
EP2350748A1 (fr) * 2008-10-10 2011-08-03 ExxonMobil Research and Engineering Company Optimisation en temps réel d'alimentation, de distribution et de consommation d'hydrogène gazeux de raffinerie
US8032262B2 (en) 2009-10-08 2011-10-04 Saudi Arabian Oil Company System, method, and program product for synthesizing non-constrained and constrained heat exchanger networks
US8116920B2 (en) 2009-10-08 2012-02-14 Saudi Arabian Oil Company System, method, and program product for synthesizing non-thermodynamically constrained heat exchanger networks
US8150559B2 (en) 2006-06-23 2012-04-03 Saudi Arabian Oil Company Systems and program product for heat exchanger network energy efficiency assessment and lifetime retrofit
US8150560B2 (en) 2006-06-23 2012-04-03 Saudi Arabian Oil Company Methods for heat exchanger network energy efficiency assessment and lifetime retrofit
US8417486B2 (en) 2009-10-30 2013-04-09 Saudi Arabian Oil Company System, method, and program product for synthesizing heat exchanger networks and identifying optimal topology for future retrofit
US8606551B2 (en) 2008-06-06 2013-12-10 Saudi Arabian Oil Company System, program product, and related methods for global targeting of process utilities under varying conditions
US9094470B2 (en) 2002-04-15 2015-07-28 Fisher-Rosemount Systems, Inc. Web services-based communications for use with process control systems
US9606594B2 (en) 2012-03-19 2017-03-28 Saudi Arabian Oil Company Methods for simultaneous process and utility systems synthesis in partially and fully decentralized environments
US9760099B2 (en) 2008-06-06 2017-09-12 Saudi Arabian Oil Company Systems, program code, computer readable media for planning and retrofit of energy efficient eco-industrial parks through inter-time-inter-systems energy integration
US9927788B2 (en) 2011-05-19 2018-03-27 Fisher-Rosemount Systems, Inc. Software lockout coordination between a process control system and an asset management system
JP2021056736A (ja) * 2019-09-30 2021-04-08 横河電機株式会社 システム、方法、および、プログラム
JP2021056738A (ja) * 2019-09-30 2021-04-08 横河電機株式会社 システム、方法、および、プログラム

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3891836A (en) * 1972-04-21 1975-06-24 Mobil Oil Corp Apparatus for optimizing multiunit processing systems
EP0582069A2 (fr) * 1992-06-16 1994-02-09 Praxair Technology, Inc. Procédé a deux phases pour contrôler un processus en temps réel

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3891836A (en) * 1972-04-21 1975-06-24 Mobil Oil Corp Apparatus for optimizing multiunit processing systems
EP0582069A2 (fr) * 1992-06-16 1994-02-09 Praxair Technology, Inc. Procédé a deux phases pour contrôler un processus en temps réel

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CAINE D J ET AL: "Linear programming comes of age: a decision-support tool for every manager" MANAGEMENT DECISION, 1996, MCB UNIVERSITY PRESS, UK, vol. 34, no. 4, pages 46-53, XP001028042 ISSN: 0025-1747 *
MURPHY F H ET AL: "Representation schemes for linear programming models" MANAGEMENT SCIENCE, JULY 1992, USA, vol. 38, no. 7, pages 964-991, XP001028036 ISSN: 0025-1909 *
SHARDA R: "The state of the art of linear programming on personal computers" INTERFACES, JULY-AUG. 1988, USA, vol. 18, no. 4, pages 49-58, XP001028033 ISSN: 0092-2102 *

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US9760651B2 (en) 2002-04-15 2017-09-12 Fisher-Rosemount Systems, Inc. Web services-based communications for use with process control systems
US9094470B2 (en) 2002-04-15 2015-07-28 Fisher-Rosemount Systems, Inc. Web services-based communications for use with process control systems
US7269466B2 (en) 2002-07-24 2007-09-11 Mitsubishi Denki Kabushiki Kaisha Optimal operation controller of plant
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US7966331B2 (en) 2003-08-18 2011-06-21 General Electric Company Method and system for assessing and optimizing crude selection
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EP2350748A1 (fr) * 2008-10-10 2011-08-03 ExxonMobil Research and Engineering Company Optimisation en temps réel d'alimentation, de distribution et de consommation d'hydrogène gazeux de raffinerie
EP2350748A4 (fr) * 2008-10-10 2014-04-23 Exxonmobil Res & Eng Co Optimisation en temps réel d'alimentation, de distribution et de consommation d'hydrogène gazeux de raffinerie
US8116920B2 (en) 2009-10-08 2012-02-14 Saudi Arabian Oil Company System, method, and program product for synthesizing non-thermodynamically constrained heat exchanger networks
US8032262B2 (en) 2009-10-08 2011-10-04 Saudi Arabian Oil Company System, method, and program product for synthesizing non-constrained and constrained heat exchanger networks
US8417486B2 (en) 2009-10-30 2013-04-09 Saudi Arabian Oil Company System, method, and program product for synthesizing heat exchanger networks and identifying optimal topology for future retrofit
US9927788B2 (en) 2011-05-19 2018-03-27 Fisher-Rosemount Systems, Inc. Software lockout coordination between a process control system and an asset management system
US9606594B2 (en) 2012-03-19 2017-03-28 Saudi Arabian Oil Company Methods for simultaneous process and utility systems synthesis in partially and fully decentralized environments
US9612635B2 (en) 2012-03-19 2017-04-04 Saudi Arabian Oil Company Systems and computer programs for simultaneous process and utility systems synthesis in partially and fully decentralized environments
JP2021056736A (ja) * 2019-09-30 2021-04-08 横河電機株式会社 システム、方法、および、プログラム
JP2021056738A (ja) * 2019-09-30 2021-04-08 横河電機株式会社 システム、方法、および、プログラム

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