Celebrating the Birth of Digital Twins

The ability to virtually model piping system performance is a huge win across your organization

By Ray Hardee PE

In my July 8th post, I offered an overview of what we mean by the term Digital Twin. I’ll recap that definition in a moment, but today I’m going to focus on answering a few fundamental questions, such as:

  •   What can a Digital Twin do for me, as an engineer who designs, builds, and is responsible for technical systems; an operations person, who ensures that those systems perform to their maximum potential (with minimal downtime); or as an executive, charged with P&L responsibilities for my business unit or my entire enterprise?
  •   Why has this technology—pioneered, tested, and proven over the years by NASA—come to the fluids piping universe now, and why is its advent of critical importance?

At PIPE-FLO, we’re celebrating the Digital Twin movement because, primarily and most importantly, it fills a need for our customers who build, own and maintain high-cost, long-life assets (manufacturing plants, public utilities, mines, and more), demand open communication among multiple disciplines, and face financial repercussions when systems falter or fail.

These customers need to allocate their capital wisely, ensure that their investments are designed and built to the right specifications; can adapt to new processes, demands and opportunities; and are operated and maintained properly during their entire lifecycle.

We’re also embracing Digital Twin because it describes what the PIPE-FLO modeling platform has become: the cornerstone of a fluid piping system’s mathematical Digital Twin. [SC1]  

The “elevator pitch”

The 30-second elevator-ride description of a Digital Twin is this: it’s a dynamic, digital representation of a physical system that turns real-world, usually real-time, data from that system into insights that impact an entire project lifecycle.

Those insights affect decisions like capital planning, system design, capacity management, dynamic optimization, system troubleshooting, preventive maintenance, and more.

Its roots lie in the aerospace industry, where it literally earned a mission-critical reputation by simulating the system performance of spacecraft before they launched their human cargo into orbit, and safely brought them back. Recent technology breakthroughs—cloud computing, digital sensors, and Industry 4.0 and the IIoT—have launched the Digital Twin into systems for earthbound things like combined cycle turbines, locomotives, automobiles and trucks, to name a few. It’s enabling those systems to be more reliable, safe, productive, and cost-effective. 

Capabilities it brings to fluids systems

Now it’s moving from those “vertical” categories to cross-industry disciplines like piping system design and operation. Advances like the ones I just mentioned give the Digital Twin the data sources and computational power to address unique challenges that face all organizations with large piping systems: the prevalence of custom-designed and -built systems that created for individual industries, facilities, products, and even processes.

The Digital Twin may be a newcomer to the piping system design world, but that makes adopting it no less urgent.

We’re seeing leading-edge PIPE-FLO customers incorporating our technology into their Digital Twin/predictive analytics initiatives—and they’re already reaping benefits like significant capital and operating cost savings, superior capacity management, cost control, and operational improvements.

A single facility may employ several Digital Twin technologies. For example, a chemical plant may use it to provide insight on the chemical processes, fluid piping system, pipe hangers and restraints, and internal plant layout. The Digital Twin enables those models to share a common source of data and a single source of truth for the entire system.  The predictive analytics of the Digital Twin deliver insights into that system that lead to better decisions and outcomes.  So, what does that mean to you, as a stakeholder? 

For an executive

System optimization is a critical need if you’re entrusted with the business health of your organization. You may have risen from the ranks of operations or technical department heads, but you have big-picture concerns now. Through its ability to model capacity scenarios, monitor system performance, identify possible maintenance issues, and provide measurable analysis, the Digital Twin can help you address challenges such as:

  •   Prudent allocation of capital expenditures
  •   Optimization of existing capacity, to meet strategic growth and revenue opportunities
  •   Operating cost reduction and long-term control
  •   Asset ROI optimization.

For engineers who design and “own” piping systems

The Digital Twin is invaluable across the project management lifecycle (PLM) because it helps you:

  •   “Right-size” new systems based on the company’s strategic goals
  •   Support the dynamic optimization required of existing systems in “nimble manufacturing” settings
  •   Identify energy efficiency and capture opportunities
  •   Maintain the highest levels of throughout, overall performance, cost-efficiency, and cost control—and measure those achievements.
     

For operations personnel

Its performance monitoring and predictive modeling capabilities enable people tasked with keeping systems running smoothly to:

  •   Troubleshoot potential issues
  •   Reduce unplanned outages
  •   Continuously improve operations and maintenance processes.

Across specific industries, the Digital Twin clearly can deliver massive benefits for the design and operation of fluids systems, whether they’re on the drawing board or in operation. I’ll elaborate on its impact across the project management lifecycle in my next post.

 

Ray Hardee, PE, is the co-founder of PIPE-FLO and Engineered Software Inc. and a recognized expert on piping systems modeling and predictive analytics. Look for his e-book DIGITAL TWIN: THE FUTURE OF FLUID PIPING SYSTEM MANAGEMENT from PIPE-FLO later this year.

Ray Hardee P.E. stamp

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