Method for monitoring and controlling the energy cost for the production of a product lot

Abstract

A method monitors the energy cost for the production of a product lot using a manufacturing execution system (MES) that enables the operator of a production facility to optimize the production process in terms of energy costs. The method includes a) executing a production process being scheduled and controlled by the MES to produce the product lot; b) for each individual production step measuring the energy consumption over the course of the execution of the individual production step; c) creating a data model within the MES that correlates production specific data and the energy consumption data related to the product lot; d) defining commands to manage the production specific data and the energy consumption data wherein the commands are web APIs; and e) evaluating the production specific data and the energy consumption data and creating an energy consumption profile for the production process related to the product lot.

Claims

1. A method for monitoring and/or controlling energy costs for a production of a product lot using a manufacturing execution system in a production plant having a plurality of production lines, which comprises the steps of: executing a production process being scheduled and controlled by the manufacturing execution system to produce the product lot, the production process having a number of scheduled production steps; measuring, for each individual production step, an energy consumption over a course of an execution of the individual production step for the product lot; creating a data model within the manufacturing execution system that correlates production specific data and energy consumption data related to the product lot; defining commands to manage the production specific data and the energy consumption data wherein the commands are configured as web application programming interfaces (APIs); and evaluating the production specific data and the energy consumption data and creating an energy consumption profile for the production process related to the product lot produced within the manufacturing execution system.

2. The method according to claim 1, which further comprises comparing the energy consumption profile to an energy cost profile.

3. The method according to claim 2, wherein a production scheduler uses a result of a comparison of the enemy consumption profile in order to adjust a scheduled order of at least one of the scheduled production steps.

4. The method according to claim 3, which further comprises linking the energy consumption data to the production specific data thereby determining an energy cost for the production of the product lot.

5. The method according to claim 1, wherein an energy and resource management system is provided as a self-consistent application that is embedded in the manufacturing execution system.

6. The method according to claim 5, wherein the energy and resource management system processes as input data the production specific data at runtime and the energy consumption data retrieved from sensors logically connected to production resources involved in the scheduled production steps and outputs an ordinated data set of the production specific data and the energy consumption data associated to the product lot for each of the scheduled production steps.

7. The method according to claim 1, wherein the manufacturing execution system provides information about which production resources are involved in a specific production step.

8. The method according to claim 1, wherein the web APIs are RESTful web APIs.

9. A non-transitory computer readable medium having computer-executable instructions for performing a method for monitoring and/or controlling energy costs for a production of a product lot using a manufacturing execution system in a production plant having a plurality of production lines, which method comprises the steps of: executing a production process being scheduled and controlled by the manufacturing execution system to produce the product lot, the production process having a number of scheduled production steps; measuring, for each individual production step, an energy consumption over a course of an execution of the individual production step for the product lot; creating a data model within the manufacturing execution system that correlates production specific data and energy consumption data related to the product lot; defining commands to manage the production specific data and the energy consumption data wherein the commands are configured as web application programming interfaces (APIs); and evaluating the production specific data and the energy consumption data and creating an energy consumption profile for the production process related to the product lot produced within the manufacturing execution system.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

(1) FIG. 1 is an illustration showing architecture for an energy resource management system (ERMS) within an industrial cluster scenario;

(2) FIG. 2 is an illustration showing an entity/relationship diagram for an equipment model view on a data model of the energy resource management system;

(3) FIG. 3 is an illustration showing an entity/relationship diagram for a production model view on the data model of the energy resource management system;

(4) FIG. 4 is an illustration showing an entity/relationship diagram for a cluster model view on the data model of the energy resource management system;

(5) FIG. 5 is an illustration showing a command execution scheme for the interaction with the ERMS;

(6) FIG. 6 is an illustration showing internal interaction components of the ERMS; and

(7) FIG. 7 is an illustration showing an implementation for the energy resource management system within the environment of a manufacturing execution system.

DETAILED DESCRIPTION OF THE INVENTION

(8) The present invention lays in the technical field of manufacturing execution systems (MES/MOM). By way of non-limiting example, reference will be made to the architecture of the SIMATIC IT Unified Architecture Foundation (in short, SIT UAF) product of Siemens Corporation.

(9) SIMATIC IT Unified Architecture Foundation is a platform for creating an integrated MES/MOM ecosystem, targeting the current and future transformational technologies that are shaping the future of manufacturing: mobile internet, automation of knowledge work, the Internet of Things, and the Cloud. At the core of SIT UA Foundation is a unified manufacturing operations data bus that provides a common backbone for the integration of legacy applications while providing the first fundamental step towards a unified integrated system.

(10) In a nutshell, SIT UAF is:

(11) a) the platform approach to creating a Manufacturing Operations Management strategy;

(12) b) the core element for executing, tracking and monitoring production progress and performance; and

(13) c) the data bridge between PLM and Shop Floor Automation, to create the Digital Enterprise.

(14) SIT UAF is constructed around Domain Driven Design (DDD). Application data, services and events are defined by a model setting out the application structure and exposed as meta-data at any layer. SIT UAF includes common MOM components and services for connecting external systems, aggregating and exposing data, defining plant models and connecting the underlying automation data and systems. An overview of the product can be found in the document SIMATIC IT Unified Architecture Foundation, available on the website www.siemens.com.

(15) The present invention now provides a solution for considering the energy consumption of a production facility/line with respect to a specific product lot as a direct component of the production thereby providing an automated instance that correlates all data relevant for the determination of the energy consumption in a flexible and repeatable way.

(16) For its realization, a specific data model has been designed and implemented that correlates the production data and the energy consumption data as well as the algorithms to expose the information to an optimization tool (if any). The data model and the algorithms form a software application that is hereinafter referred to as an energy resource management system (ERMS). The ERMS defines the framework infrastructure, the data model and the set of commands to manage the data. The ERMS is a self-consistent application that can be used stand-alone or embedded in the Manufacturing Execution System (as mentioned above) of a production facility.

(17) The input data for the ERMS are:

(18) i) run time production and energy data retrieved from sensors connected to a production line and its resources resp.;

(19) ii) product lot information provided from a Manufacturing Execution System or a surrounding system, such as an ERP (enterprise resource planning system), or from a dedicated interface; and

(20) iii) energy cost profile provided by a third party system, such as a cost table from an energy supplier.

(21) The output data of the ERMS are the processed data sets of production and energy data associated to a given production lot for each production step. Typically, the ERMS outputs feed optimization tools to manage the production, in particular to plan and schedule production steps to achieve the best energy efficiency and/or to produce at the lowest energy cost.

(22) The data model is conceived to set the production data, the resource data and the energy fluctuation into a programmed relationship. The interoperability of the data model is guaranteed by a set of commands that manage the correlated data and by a framework that can be queried. In the implementation disclosed in these examples, the commands are RESTful web APIs and the queries are executed via ODATA, simplifying dramatically the integration into the Manufacturing Execution System (MES or MOM).

(23) An ERMS framework 2 according to the example shown in FIG. 1 is integrated in the MES system for controlling production lines 4 and 6 (here columns in the petrochemical industry). The production lines 4 to 6 are managed by local automation systems, such as Simatic, WinCC, Teamcenter and PCS-7 which are TIA portal products of Siemens Aktiengesellschaft. The production lines 4, 6 are connected to a connection layer 8 which manages the flow of data according to the defined data model. The ERMS 2 is connected via RESTful web APIs 12 and other data query commands 14 to the connection layer 10. The ERMS 2 contains a direct link to the MES 10 and an ERP system 16 (such as SAP). Further, the ERMS 2 is connected to an optimization tool 18 which is enabled to recalculate the order of the production process according to predefined optimization criteria, such as delivery date, resource availability, energy cost and the like. Results of the optimization tool 18 can be evaluated in both the ERP system 16 and the MES 10, such as rescheduling of production activities.

(24) The data model for the product lot-wise reflection of the energy cost provides an entire entity/relationship structure that is suitable for most of the possible requirements of an external application. In the following the data model is described in more detail by describing the entities and their relationships logically grouped in three main clusters:

(25) i) Equipment model view: provides the definition of the physical hierarchy of the production resources, defining equipment and utilities (such as sensors, meters, etc.);

(26) ii) Production model view: provides a snapshot of the runtime production data;

(27) iii) Cluster model view: simply describes how a cluster of production lines is related to the physical plants and how they will collaborate among each other.

(28) FIG. 2 schematically shows an entity/relationship diagram for an equipment model view on the data model of the energy resource management system ERMS 2. This view explains the design of the involved entities and describes how they are connected to each other. In particular:

(29) PLANT represents the production unit;

(30) LINE defines the logical/physical cluster of equipment that is inside a production unit;

(31) LINE ASSIGNMENT represents the link between LINEs and the equipment therein;

(32) EQUIPMENT defines the production and power equipment that are part of the LINE in a production unit (PLANT);

(33) UTILITY represents any utility, linked to an unit of EQUIPMENT that can perform measurements;

(34) UTILITY TYPE defines the type of UTILITY and comprises the general specification of the UTILITY (i.e. flow meter, temperature meter, current meter, pressure meter etc.); and

(35) MEASURE represents the runtime measurement collected by the available sensors in the UTILITIES. This might be a calculation already derived that is performed by the optimization tool 18.

(36) FIG. 3 schematically shows an entity/relationship diagram for a production model view on the data model of the energy resource management system. Again, this diagram provides the design of the involved entities and describes how they are connected to each other. In particular:

(37) BATCH represents the main element of the production operations; with other words the product lot to be produced;

(38) CONTROL RECIPE defines the recipe used to produce the final material/product;

(39) OPERATION represents the production step that is performed into a piece of EQUIPMENT in the production unit;

(40) MATERIAL defines the input and/or output materials managed in the OPERATION; MATERIAL represents the runtime entity that indicates the material actually consumed or produced with its quantity and the related UTILITY of MEASURE;

(41) CONFIGURATION PARAMETER represents the production step parameters, which are related to the production operations and/or to the resources/machines;

(42) SCHEDULED OPERATION is linked to the OPERATION entity, it is an entity where the optimization tool can host its results and/or its calculations during a learning phase (data collection phase);

(43) SCHEDULED MATERIAL is linked to the SCHEDULED OPERATION entity, it is an entity where the optimization tool can store its outcomes according to the SCHEDULED OPERATION;

(44) SCHEDULED CONFIGURATION PARAMETER is linked to the SCHEDULED OPERATION entity, it is an entity where the optimization tool can store its outcomes according to the SCHEDULED OPERATION; and

(45) ENERGY PRICING provides an entity the energy rates offered day-by-day by the energy provider as an energy supplier of the production unit.

(46) FIG. 4 schematically represents an entity/relationship diagram for a cluster model view on the data model of the energy resource management system. In particular:

(47) CLUSTER represents a set of companies that might be grouped in a cluster, in an energy/resources symbiosis based on the possible interconnection they could have among each other;

(48) COMPANY defines the company that owns the production unit (PLANT); and

(49) FLOW TYPE represents the possible interconnections between the companies in a cluster. Flow type entity models the connections and resources involved without detailing their type or nature.

(50) Commands are used to manage the data flow within these data models. The commands are services that comprise the business logic to correlate and expose data according to the needs of an external application. Each command contains an interface and its specific implementation what is called the command handler. The business logic is implemented at command handler level and not at database level. When a command is invoked, the proper command handler is executed within a hosting process called worker. Each worker can simultaneously execute different commands. Commands can be called from the web or from a command handler. They are basic services to be inside any complex Service Oriented Architecture (SOA).

(51) FIG. 5 schematically illustrates a command execution scheme for the interaction with the ERMS 2. The ERMS exposes a set of commands that represents the only way to interact with the ERMS 2. FIG. 5 accordingly shows a worker instance managing the command execution request which is then executed at command handler level. The command handler retrieves the data by single transaction with the database.

(52) FIG. 6 schematically shows the internal interaction components of the ERMS 2. A typical workflow starts when an external application performs a request. A service layer 20 identifies a worker 22 being configured to execute the command; an instance of the worker 22 executes the logic comprised in the command handler 24.

(53) The present invention therefore discloses the design of the self-consistent ERMS 2 to correlate and expose production data, energy consumption data, resource consumption and energy costs in a manufacturing scenario. The aggregated data set allows managing production plan and scheduling to achieve energy efficiency and reduce energy cost. The design is highly flexible allowing the deployment of the system in several operative scenarios and inside different information systems. The main technical advantages are:

(54) Correlate different types of data, usually coming from different, separate sources which are today processed by separate systems;

(55) Compute the direct energy consumption curve for a given product lot;

(56) Expose the data as a service on demand;

(57) The exposed data is provided as real-time response;

(58) Repeatable and automated system;

(59) Operate in a single unit or in a distributed scenario;

(60) Can be embedded in a third party application; and

(61) Can be invoked as an external service in a distributed architecture.

(62) This design makes it possible to achieve the following economic advantages:

(63) Improve the energy efficiency (by reducing demand peaks and fluctuations); and

(64) Reduce energy costs (by re-arranging and optimizing production steps according to the energy cost.

(65) FIG. 7 schematically shows an implementation for the energy resource management system within the environment of a manufacturing execution system. The role of ERMS inside a production environment is described in short as follows.

(66) At level 1, a Production Unit (or Production Line) is made by a set of equipment devoted to perform a specific production step. At level 2, each equipment has an energy meter (sensor) that provides the value of the energy consumed in a given production step. The sensor(s) can be embedded in the equipment or external to the equipment.

(67) At level 3, any Raw Data coming from the sensors are stored in a common data repository (external to ERMS), such as the MES common data repository. At level 4, the ERMS API are used to fill the ERMS Data Model with Energy Data coming from the common data repository. Any process data are collected by the MES. The ERMS associates the Energy data and the Process data to the Lot, for each production step, to create the energy profile of the lot which can be displayed at level 6 on user interface of a plant operator. At level 5, External Applications can be connected to the ERMS to perform specific operations. The ERMS API(s) provides the respective data exchange. At level 6, the ERMS results are displayed as an Energy Consumption profile that associate to the product lot the values of the energy consumed in each step of the production. This Energy Consumption profile can therefore be used to optimize the production steps to achieve a production process aware of the lot-wise energy consumption. Typical measures could be to reduce energy consumption peaks and/or scheduling of production according to the energy cost profile that is applied by the respective energy supplier.