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WO2018196998A1 - Procédé et système destinés à l'attribution de ressources dans une usine de traitement - Google Patents

Procédé et système destinés à l'attribution de ressources dans une usine de traitement Download PDF

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Publication number
WO2018196998A1
WO2018196998A1 PCT/EP2017/060206 EP2017060206W WO2018196998A1 WO 2018196998 A1 WO2018196998 A1 WO 2018196998A1 EP 2017060206 W EP2017060206 W EP 2017060206W WO 2018196998 A1 WO2018196998 A1 WO 2018196998A1
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WO
WIPO (PCT)
Prior art keywords
resources
atomic
recipe
product
processor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2017/060206
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English (en)
Inventor
Michael Pirker
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Siemens Corp
Original Assignee
Siemens AG
Siemens Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens AG, Siemens Corp filed Critical Siemens AG
Priority to PCT/EP2017/060206 priority Critical patent/WO2018196998A1/fr
Publication of WO2018196998A1 publication Critical patent/WO2018196998A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/08—Auctions

Definitions

  • the following steps are executed: processing, by a processor, a recipe for the production of a continuous prod ⁇ uct, and allocating, by a processor, resources in a process plant based on the recipe.
  • the system for allocating resources in a process plant com- prises an electronic memory, storing a recipe for the produc ⁇ tion of a continuous product, and at least one processor, programmed for processing the recipe and for allocating resources in a process plant based on the recipe.
  • the following advantages and explanations are not necessarily the result of the object of the independent claims. Rather, they may be advantages and explanations that only apply to certain embodiments or variants.
  • the method and the system organize an allocation of resources according to production recipes of continuous products in a product-driven way. Based on the product's recipe, plant re- sources are allocated automatically in advance, while still considering the continuous character of the product.
  • the method and the system introduce more flexibility by fa ⁇ cilitating automated product-driven resource allocation in process plants. Incidents, like unscheduled maintenance, and unforeseen conditions, like need for higher or reduced plant output, can be handled in an automated way. In existing pro ⁇ cess plants, flexible behavior, like re-routing a product during its production, can be realized only manually - if at all.
  • An embodiment of the method comprises the additional step of producing the product according to the recipe with the allo ⁇ cated resources.
  • the processor allocates the resources by executing automated auctions at a virtual market place.
  • the processor combines parts of the recipe to derive atomic auctions consisting of atomic combinations of required resources.
  • the processor forms an atomic auction sequence by concatenating the atomic auctions, and allocates the resources by executing the atomic auction sequence using a negotiation algorithm.
  • This embodiment introduces an extra processing step for iden ⁇ tifying the atomic combinations of plant resources.
  • Each com ⁇ bination is a set of resources.
  • the processor derives the atomic auctions with a rule-based system, in particular a constraint solver, the rule-based system processing rules from a rule base and knowledge from a knowledge base stored in an electronic memory, the rules and/or knowledge describ ⁇ ing correlations and/or dependencies between one resource and at least one other resource in the production plant, and/or a topology and/or location of the resources in the production plant, and/or a runtime behavior of the resources in the pro ⁇ duction plant, and/or malfunctioning conditions of the resources in the production plant.
  • This embodiment uses a rule-based system for the automated derivation of atomic auctions.
  • the atomic combinations of re ⁇ quired resources are identified by the rule-based system, by combining recipe parts. Based on correlations/dependencies expressed in explicit rules, the rule-based system can derive the atomic combinations as sets of plant resources. Based on the recipe and the current state of the process plant, a rule base describing important features of the process plant is used to derive sets of resources that must be allocated to produce the product .
  • the negotiation algorithm processes offers of available resources and executes the atomic auctions based on an atomic consideration of several required resources for each atomic auction.
  • the negotiation algorithm uses combinatorial auctions to allocate atomic combinations of required re ⁇ sources. This means that, when an atomic set of required re ⁇ sources is identified, a combinatorial auction can be applied to allocate these resources, as required by the recipe of the product .
  • a software agent is running on the processor, the software agent representing the product of the recipe and initiating the atomic auctions in order to allocate the resources.
  • the system comprises a pro ⁇ duction control system, configured for a production of the product with the allocated resources.
  • the system is configured for performing the method.
  • the computer-readable storage media has stored thereon in ⁇ structions executable by one or more processors of a computer system, wherein execution of the instructions causes the com ⁇ puter system to perform the method.
  • the computer program is being executed by one or more proces ⁇ sors of a computer system and performs the method.
  • Fig. 2 a schematic overview of an embodiment of a system for allocating resources in a process plant PP
  • Fig. 3 a flowchart of an embodiment of the method for allo ⁇ cating resources in a process plant.
  • Figure 1 shows a section of a process plant. Based on a product's recipe, plant resources are allocated automatically in advance. To automate the resource allocation, negotiation mechanisms are used: The plants available resources, like pipes and tanks, are offered at a virtual market place. The product, represented by a software agent, starts a negotia- tion sequence based on the required resources derived from its recipe. For instance, a recipe for mixing a first liquid A with a second liquid B requires a certain amount of the first liquid A to be transported via a first pipe PI to a specific tank T and another amount of the second liquid B via a second pipe P2 to the same tank T. While the tank T is filled with the first liquid A, it may occupy the first pipe PI and the tank T at the same time.
  • FIG. 2 shows a schematic overview of an embodiment of a system for allocating resources in a process plant PP.
  • the first pipe PI, the second pipe P2 and the tank T from Figure 1 are depicted as resources in Figure 2.
  • a processor CPU is programmed to perform resource allocation based on virtual market places by means of auctions. To this end, the processor CPU uses the concept of "combinatorial auctions", meaning the atomic consideration of several re ⁇ quired resources.
  • a combinatorial auction can be applied to allo ⁇ cate these resources, as required by a recipe RCP of a prod ⁇ uct that is stored in an electronic memory M, for example a hard drive or cloud storage.
  • the processor CPU can be pro- grammed to implement software platforms to realize such re ⁇ source allocations, for example software agent market places.
  • program code representing at least one soft ⁇ ware agent SOA and at least on virtual market place VMP can be loaded into the processor CPU for execution.
  • the processor CPU derives an atomic auction sequence AAS, like "allocate (PI AND T) from 10:43 to 10:52", from an instruction in the reci- pe RCP, like "Fill 300 liters of the first liquid A to a tank”.
  • AAS atomic auction sequence
  • This derivation is performed by a rule-based system RBS that is executed by the processor CPU.
  • the rule-based system RBS processes rules stored in a rule base RB located in the electronic memory M.
  • Each rule describes correlations and dependencies of one resource of the process plant PP (like a pipe) with other resources (like a tank), e.g. the first pipe PI leads to the tank T.
  • a rule engine of the rule-based system RBS can derive atomic sets of plant resources (like "PI AND T") .
  • the rule-based system RBS can also process knowledge stored in a knowledge base KB located in the electronic memory M.
  • the rule-based system RBS to solve such tasks can be a constraint solver.
  • the electronic memory M, the processor CPU and/or the automic auction sequence AAS can be located in a cloud CL .
  • Figure 3 shows a flowchart of an embodiment of the method for allocating resources in a process plant and gives a summary and example of the approach.
  • a product dataset PD (stored for example in the electronic memory M depicted in Figure 2) con ⁇ tains a recipe RCP and a knowledge base KB, the latter con ⁇ taining knowledge about the current state of the plant (e.g. locations of different product components) .
  • the product da- taset PD is fed to a rule-based system RBS which processes the product dataset using a rule base RB .
  • the rule base RB describes important features of the process plant (like to ⁇ pology, runtime behavior, malfunctioning conditions etc.) and is used by the rule-based system to derive sets of atomic re- sources that must be allocated to process the recipe RCP.
  • the depicted rules derive that always a pipe and a tank located beside each other must be handled as an atomic set in each resource allocation step (i.e. these rules describe topological information).
  • Other rules might describe maintenance, malfunctioning, and other important aspects of the process plant also.
  • the output of the rule-based system RBS is an atomic auction sequence AAS that is processed at a virtual market place VMP.
  • this step is required prior to resource allocation because of the physical installations for fluids or gases that are processed, e.g. pipes are directly connect- ed to tanks.
  • resource allocation can be applied in process plants (continuous production systems) .
  • process plants continuous production systems
  • the rule-based system RBS is used for automated derivation of the atomic auction sequence AAS, which is then executed with combinatorial auctions for automated resource allocation at the virtual market place VMP.
  • the combinatorial auctions allocate atomic combinations of required resources (like a pipe and a tank) according to the recipe RC .
  • the atomic combinations (like “a pipe AND a tank”, “a reactor”, “a pipe AND a valve AND a pump") of required re- sources are identified by the rule-based system RBS, by com ⁇ bining parts of the recipe RCP ("a pipe”, “a tank”, “a reac ⁇ tor”, “a valve”, “ a pump”) .
  • the method can be executed by a processor such as a microcon ⁇ troller or a microprocessor, by an Application Specific Inte- grated Circuit (ASIC) , by any kind of computer, including mo ⁇ bile computing devices such as tablet computers, smartphones or laptops, or by one or more servers in a control room or cloud .
  • a processor, controller, or integrated circuit of the computer system and/or another processor may be configured to implement the acts described herein.
  • the above-described method may be implemented via a computer program product including one or more computer-readable stor ⁇ age media having stored thereon instructions executable by one or more processors of a computing system. Execution of the instructions causes the computing system to perform oper ⁇ ations corresponding with the acts of the method described above .
  • the instructions for implementing processes or methods de- scribed herein may be provided on non-transitory computer- readable storage media or memories, such as a cache, buffer, RAM, FLASH, removable media, hard drive, or other computer readable storage media.
  • Computer readable storage media in ⁇ clude various types of volatile and non-volatile storage me- dia.
  • the functions, acts, or tasks illustrated in the figures or described herein may be executed in response to one or more sets of instructions stored in or on computer readable storage media.
  • the functions, acts or tasks may be independ ⁇ ent of the particular type of instruction set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro code and the like, operating alone or in combination.
  • processing strategies may include multiprocessing, multitasking, parallel processing and the like.

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Abstract

Dans un premier temps, une recette (RCP) pour la production d'un produit continu est traitée. Ensuite, des ressources (P1, P2, T) dans une usine de traitement (PP) sont attribuées en fonction de la recette. Ainsi, les ressources sont attribuées d'une manière orientée produit. En fonction de la recette du produit, des ressources d'usine sont attribuées automatiquement à l'avance, tout en tenant compte du caractère continu du produit. Ceci permet davantage de flexibilité en facilitant l'attribution de ressources orientée produit automatisée dans des usines de traitement. Des incidents, tels que la maintenance non planifiée, et des conditions imprévues, comme un besoin plus élevé ou réduit en sortie d'usine, peuvent être gérés de façon automatisée. Dans des usines de traitement existantes, un comportement flexible, tel qu'un nouveau routage d'un produit pendant sa production, peut être exécuté uniquement manuellement, le cas échéant. Dans un mode de réalisation, les ressources sont attribuées par exécution de ventes aux enchères automatisées à un emplacement de marché virtuel (VMP). Dans un autre mode de réalisation, un processeur (CPU) combine des parties de la recette pour déduire des enchères atomiques constituées de combinaisons atomiques de ressources requises. Le processeur forme une séquence d'enchères atomiques (AAS) par concaténation des enchères atomiques, et attribue les ressources par exécution de la séquence d'enchères atomiques à l'aide d'un algorithme de négociation. Ce mode de réalisation introduit une étape de traitement supplémentaire pour identifier les combinaisons atomiques de ressources d'usine.
PCT/EP2017/060206 2017-04-28 2017-04-28 Procédé et système destinés à l'attribution de ressources dans une usine de traitement Ceased WO2018196998A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111950849A (zh) * 2020-07-09 2020-11-17 华为技术有限公司 数据处理方法和数据处理装置

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100217420A1 (en) * 2009-02-26 2010-08-26 Sinclair Paul Andrew Method of generating recipe for process
US8855968B1 (en) * 2012-12-10 2014-10-07 Timothy Lynn Gillis Analytical evaluation tool for continuous process plants

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100217420A1 (en) * 2009-02-26 2010-08-26 Sinclair Paul Andrew Method of generating recipe for process
US8855968B1 (en) * 2012-12-10 2014-10-07 Timothy Lynn Gillis Analytical evaluation tool for continuous process plants

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
BANASZEWSKI RONI F ET AL: "An application of a multi-agent auction-based protocol to the tactical planning of oil product transport in the Brazilian multimodal network", COMPUTERS & CHEMICAL ENGINEERING, vol. 59, 5 December 2013 (2013-12-05), pages 17 - 32, XP028754598, ISSN: 0098-1354, DOI: 10.1016/J.COMPCHEMENG.2013.06.007 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111950849A (zh) * 2020-07-09 2020-11-17 华为技术有限公司 数据处理方法和数据处理装置

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