Building a Centrifugal Pump Digital Twin for a Chemical Plant

A digital twin represents a physical asset and its function; it contains intelligence to evaluate static and real-time data.

Chemical refining plants are asset intensive and employ a wide range of rotating and static equipment. Chemical refining plants make biofuels, alcohols, automotive coatings and chemicals used downstream in other chemical processes. Centrifugal pumps are used to move fluids, including water, used for steam, cooling water for heat exchangers, mineral oils as feed stock for refining and resulting product. Pumps are an important asset for refineries.

A digital twin of an important asset helps measure and evaluate asset health and performance parameters. With improved measurements (and monitoring), it becomes possible to manage and improve the health and performance of the asset. Financial benefits accrue in the form of lower maintenance costs, increased availability of the asset for production, lower power consumption and lower use of natural resources.A digital twin represents a physical asset and its function. The digital twin contains intelligence to evaluate static and real-time data. Inherently, the digital twin acts like a model, evaluating data to provide actionable information that, when acted upon, maintains the asset’s health and performance.In practice, companies often start the digital journey with equipment and process reliability in mind. A typical initial task is to create an equipment hierarchy down to replaceable or repairable components using ISO 14224, ISA 95, or similar hierarchical maps. The hierarchical maps organize equipment around units, systems, subsystems and equipment (Figure 1).

Digital twin data models contain or connect to engineering data, physics models, real-time data, historical trend data and event data including maintenance activities. Engineering data and physics models describe expected performance and expected behavior of the asset and nearby process. Real time and historical trend data describe the process and asset’s current state and trend history leading to the current state. These inputs to the digital twin are summarized in Figure 2.

The benefits of both data-driven and physics-based methods can be leveraged by using hybrid approaches. Furthermore, the drawbacks can be reduced or mitigated, making hybrid approaches ideal for maximizing the value of analytics technologies. Some of the ways physics and ML can be combined to produce a hybrid approach include the following, also shown in Figure 4.

Digital twin definitions vary depending on the application and expected function. In this article, a digital twin represents a physical asset and its function. The digital twin contains intelligence to evaluate static and real-time data. Inherently, the digital twin acts like a model, evaluating data to provide actionable information that when acted upon maintains the asset’s health and performance.In this article, a combination of engineering data, physics based models, maintenance records, process data, condition monitoring data and data-driven models come together to provide not only actionable information but also the timeframe for which mitigating action should be performed.

Preston Johnson, senior delivery manager at Novity, provides solutions for Industrial IoT with a focus on condition monitoring and predictive maintenance. Preston builds on 28 years in industrial instrumentation and machine condition monitoring systems at National Instruments, four years at Allied Reliability building predictive maintenance programs with digital technologies, and four years at CB Technologies bringing IT to predictive maintenance. He is an active member of the International Society of Automation's Smart Manufacturing and IIoT Division (also known as SMIIoT), the newest and fastest-growing division of ISA aimed at helping members grow professionally and technically. Preston draws from his broad functional background in people and project management, technical and domain expertise in equipment monitoring applications, and business development to create and implement comprehensive business strategies for new product development, market introduction and growth. He works with business development and operations teams to deploy condition monitoring and predictive maintenance systems and services, reliability solutions, training and hardware/software systems that improve machinery uptime, reliability, sustainability and ultimately, production capacity. Preston has held roles in Applications Engineering, Field Sales, Sales Management, Product Management, Business Development, and Field Implementation and Training. At Novity, Preston led the delivery of TruPrognostics solutions including hardware and software for condition monitoring and predictive maintenance. Preston resides and works in Austin, Texas. In his spare time, he enjoys his family, the outdoors and music.

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