Valentijn has extensive experience in best management practices in process industries. These include chemical, polymer, metals, energy, utilities, food, pharmaceutical, and petroleum manufacturing. His experience includes knowledge of unit processes, simulation and modeling, and business practices utilizing application software designed for manufacturing operations. He also has experience in aligning organizations, strategy, business processes and technical architectures. At ARC, Valentijn’s responsibilities include research and consulting in process industries. His technology focus is on manufacturing operations management, performance management, supply chain management, and the role of the knowledge worker in manufacturing.
Data or information modeling enable information interoperability by specifying the content of specific information and where it is stored. Models for asset information enable these types of data to be managed efficiently.
Certainly, while much work remains to be done, the industry has made significant progress toward standardizing models for managing and sharing asset-related information in recent years. This progress has been driven by specific end user requirements such as the need for accurate and up to date information to support good operational or maintenance-related decisions and historical snapshots to help identify the root cause of incidents.
Importance of Information Modeling for Industrial Operations
Major end user organizations such as Shell continue to contribute to advances in information modeling for operational data. As ARC Advisory Group learned at the recent OSIsoft EMEA user conferences, Shell has created a single data structure organized as an asset hierarchy that can be used uniformly throughout the corporation for any type of operational data.
This provides meaning to operational data for all users in the organization and enables fast global searches and “self-service” analytics. For example, with this data structure, it now takes just a few minutes to look up heat exchanger temperature profiles across the company’s entire global fleet of refineries. Standard calculations are provided and associated with standard asset objects. For example, a mathematical model associated with a single, standard heat exchanger object can be used to compare heat exchanger fouling across all refineries. Advanced analytics applications also leverage the information model.
While it now provides enormous value, it has been a major undertaking for Shell to create this asset structure. Not only did it need to accommodate data of all types of equipment of a class, but also because the company sought to achieve a consensus for the models among a broad cross section of users, each with their own requirements and preferences. For more information, readers can refer to our blogposts on the Shell presentations at the OSIsoft User Conferences in 2016 and 2017.
Developing Standards for Asset Information Modeling
In 2016, ARC published a client report titled, Standards-Based Asset, Information and Performance Management. This provided an overview of the then-current standardization initiatives related to asset management. At the heart of the standardization work is the ISO 15926 standard with a rich history and ongoing extensions. The purpose of ISO 15926 is to support digital asset data exchange between owner-operators, engineering and construction firms, and equipment builders. This standard includes an asset data model. The more recent Capital Facilities Information Handover Specification (CFIHOS) is an initiative to develop an extension of ISO 15926 with the objective to lower the barrier and improve the business case for using the standard. ISO 15926 is mainly used in the design and build phases of the assets. The ISO 18101 and ISO 14224 standards complement ISO 15926 by supporting information and maintenance data exchange with operations and maintenance service providers.
Recently, the NAMUR organization further advanced the standardization on industrial information modeling. At the NAMUR General Assembly held on November 8 and 9, 2018, two topics discussed directly relate to information modeling: the NAMUR Open Architecture (NOA) and the Asset Lifecycle (ALC) data model.
NAMUR Open Architecture Includes Information Model to Transfer Real-time Sensor and Instrument Information
Jan De Caigny from BASF provided an overview of the progress on the NAMUR Open Architecture (NOA) standard for transferring field equipment information. As indicated in the chart below, NOA uses a standardized information model (1) to securely (2) transfer field data from within the control system to cloud or on-premise applications for monitoring and optimization (M+O) purposes. Suggestions from M+O applications will be screened for authenticity (4) when proposed to the control system.
The main purpose of NOA is to reduce the cost and effort required to integrate M+O applications while safeguarding real-time, deterministic process control and instrumentation. NOA demonstrators have shown that the principles behind NOA are sound. Proof-of-concept installations show they can be transformed to technical specifications and standards that could lead to marketable products (3). NAMUR plans to use hackers to test the security of the standard.
The path forward is to formulate the users’ requirements in a NAMUR recommendation while continuing discussions with the German association of electrotechnical product providers (ZVEI) to formulate a technical standard attached to the NAMUR recommendation. After that, the standard must be internationalized and an organization identified to determine compliance and certification.
The Namur Open Architecture could potentially be used within the Module Type Package (MTP) standard in preparation, the Open Group’s Open Process Automation Forum (OPAF), as well as the new Asset Lifecycle (ALC) data information model.
NAMUR Asset Lifecycle Data Model (ALC)
Michael Wiedau of Evonik reported on the ALC Data Management project. The goal is an optimized, harmonized, future-oriented way to deal with the data needed to develop, plan, operate and maintain plant assets in an efficient, reliable, and safe manner and applying sound asset management principles – based as much as possible on existing international standards.
NAMUR used the DEXPI apparatus and machine taxonomy and instrumentation models as a basis. This is based on IEC 62424 and IEC 61987 (NAMUR recommendation NE 100). These standards help in transferring information from process design to electrical and instrumentation engineering and into design tools for automation and electrical (A&E) hardware engineering. In a next step, the NE 150 will be implemented to create the link between A&E hardware and A&E engineering design tools. Siemens will implement and market the data model.
The model will also be aligned with the CFIHOS (Capital Facilities Information Handover Specification), itself closely aligned with the ISO 15926 standard. The resulting standard will be proposed to DEXPI to augment its current model. It will also be aligned with NAMUR’s MTP and the associated NOA.
Wilhelm Otten, also of Evonik, member of the NAMUR board and former president, reported on Evonik’s implementation of the model. The company extended the DEXPI data model to make it as complete and general as required to create a basis for a wide variety of operational and asset performance management applications. For its information, the Evonik instance of the model relies on a wide range of data sources and can be seen as an interrelated set of models that, together, form a digital twin.
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Keywords: Information Model, NAMUR, DXPI ISO 15926, NOA, ALC, Steady-state, Transient, Batch Processing, ARC Advisory Group.
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