Five Decades of Evolution in Process Digital Twin Technology: From Legacy to the Next Frontier

perspective

Oct 19

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The journey of the process digital twin from simple simulation tool to essential competitive advantage spans decades of technological advancement and industrial innovation. David Vickery, current SIMACRO Board Member, provides insights that demonstrate this technology's fundamental transformation based on 40 years of field experience.

David Vickery: Expert in Process Digital Twin Development

David Vickery started his career as Process Simulation developer at a small start-up company and then moved to ChemShare Corporation in 1988. His first experience as an Optimization Engineer was at the DMC (Dynamic Matrix Control) Corporation. Following DMC's merger with AspenTech in 1996, he had roles as RT-Opt andAspen Plus Development Manager, introducing Equation-based Solver and establishing the foundation for Aspen Plus EO methodology.Later, he led various projects as Senior Technology Fellow at Dow Chemical, then returned to AspenTech to oversee the Next Generation Digital Twin product family within AT’s Performance Engineering Suite. Currently, he serves as a SIMACRO Board Member, playing a crucial role in PMv solution development.

The Critical Role and Future Direction of Process Digital Twin

How did the process digital twin evolve into billion-dollar business solutions that drive competitive advantage?At a recent SIMACRO event, David Vickery shared the evolution of digital twin based on four decades of hands-on field experience. He detailed how simple 1970s mainframe batch jobs evolved into real-time optimization systems, and how they now converge with Gen-AI to unlock new possibilities.This article examines Vickery's presentation insights, covering five decades of process digital twin evolution, billion-dollar success stories from global companies including Dow Chemical, and the current integration challenges facing digital twin—along with SIMACRO ProcessMetaverse™ 's solution approach.

1970s - Mid/Late 1980s: Beginning with Offline Simulation

Process digital twin technology began in the 1970s, primarily as offline simulation tools. These simulations ran on mainframe computers through batch processing, generating tables and graphs for operators to reference during plant operations.Technical constraints were significant during this period. Limited processing power of mainframe computers and the absence of advanced process control systems kept digital twin applications far from real-time operations.

Late 1980s - Early 2000s: Introduction of Real-Time Optimization

The late 1980s through early 2000s marked a technological advancement period in process digital twin history. Introduction of Distributed Control Systems (DCS) and Advanced Control Systems enabled real-time data utilization, supported by rapid computing advances.

Key Technological Changes

Processors: Evolution from 8-bit → 16-bit → 32-bit → 64-bit
Clock speeds: Advancement from 1MHz to multiple GHz
Hardware: Transition from Mainframe → VAX → PCs
Operating Systems: Evolution from VMS, UNIX, MS-DOS to Windows
Modeling Technology: Introduction of Equation Oriented Modeling

Dow Chemical: Over $1 Billion in Impact

Vickery experienced his first RTO system implementation firsthand when he joined DMC Corporation in 1992, and subsequently witnessed these technological "growing pains" through AspenTech's acquisition of DMC in 1996 and his later move to Dow Chemical in 2002.The introduction of Equation Oriented Modeling was particularly significant. It enabled entire processes to converge simultaneously, dramatically improving model execution speed and making Real-Time Optimization (RTO) systems feasible.

From “Discover How The Dow Chemical Company Realized Operational Excellence With Real-Time Optimization”, Webinar November 9, 2017, Available on Aspen Tech Website

In 2009, Pat and Banholzer [efn_note]Improving Energy Efficiency in the Chemical Industry, Jeremy J. Pat and William Banholzer, The Bridge, Summer 2009[/efn_note] reported that these solutions achieved:

Production Capacity Increase: Typically 3-5% improvement
Energy Intensity Reduction: Usually 4-6% decrease
Cumulative Economic Impact: Over $1 billion achieved

BP, ExxonMobil, and LyondellBasell among other global petrochemical leaders adopted similar digital twin technologies with substantial results. This demonstrates that process digital twin are proven business solutions, not experimental technologies.

2010s to Present: Expanded Digital Twin Roles

Since the 2010s, the scope and functionality of process digital twin have expanded significantly. Moving beyond Real-Time Steady State Optimization, they now encompass new areas:

Emergence of New Digital Twin Types

Asset Performance Management (APM):
- AI/ML-powered equipment failure prediction
- Preventive maintenance optimization for improved availability
Dynamic Models:
- Time-based process behavior prediction
- Transient state optimization and control
Soft Sensors:
- Virtual measurements where physical sensors are impractical
- Advanced applications like distillation column internal state estimation

Integration of Advanced Technologies

Modern process digital twin integrate Gen-AI, Machine Learning, and Augmented Reality to unlock new possibilities. Digital twin as knowledge transfer and preservation mechanisms have become particularly important. With the mass retirement of baby boomers, decades of accumulated operational know-how faces the risk of disappearing. Digital twin serve as essential tools for systematizing and preserving this tacit knowledge.Core Requirements:

Right Information at Right Time: Delivering accurate information to appropriate personnel at optimal timing
Role-Based Access: Differentiated KPIs based on position and responsibilities
Actionable Insights: Automated response and decision support
Interactivity: AI-powered user guidance capabilities
Secure Environment: Safe data processing and storage
Flexibility and Customization: Optimization for individual operational environments

Current Challenge: The Need for Integration

Vickery identifies the greatest challenge facing current process digital twin as "bringing all tools and capabilities together." Over the years, one of the major stumbling blocks for long-term success of Real-Time Optimization systems has been the expertise required to maintain them. Therefore, connecting these technologies into a secure, user-friendly integrated platform that simplifies deployment, operation, and maintenance is now essential. This is precisely where SIMACRO’s ProcessMetaverse™ provides innovation.

ProcessMetaverse™: The Integrated Solution

SIMACRO's response to this challenge is ProcessMetaverse™ (PMv). PMv provides a unified digital twin environment through four core modules:Process Canvas™: Infinite Collaborative Space - provides integrated visualization of industrial big data, customizable user dashboards, and real-time collaboration and sharing capabilities.Digital Twin Model Manager: Real-Time Simulation Engine - supports Aspen engineering products as online digital models, enables seamless integration of real-time data and simulation results, and facilitates what-if analysis and scenario simulation.Python Editor: Gen-AI/ML Integration Environment - offers real-time data access and custom algorithm development, hybrid implementation of 1st Principles models with Gen-AI/ML, and GPGPU-powered high-speed computation support.Process Agent™: Gen-AI Copilot - provides continuous learning from real-time data, model data, and operational history, becoming increasingly intelligent and plant-aware over time. It includes three sub-agents: Knowledge Repository, Python Code Generation, and Report Generation.

Performance Metrics: 10X Faster Deployment, 5X Enhanced Efficiency

PMv delivers significant performance improvements compared to traditional solutions:

10X Faster Online Deployment: Dramatically reduced implementation time
5X Enhanced User Flexibility and Efficiency: Intuitive interface and automated workflows
5X Reduced Digital Twin Model Maintenance Costs: Automated model management system

Conclusion: A New Direction for Process Digital Twin

As David Vickery's 40-year experience demonstrates, the evolution of process digital twin results from continuous interaction between technological advancement and field requirements. The journey that began with offline simulation in the 1970s has now reached the new stage of Gen-AI-powered Intelligent Plant Operation.The over $1 billion economic impact achieved at Dow Chemical demonstrates that process digital twin are proven business solutions, not experimental technologies. With such success stories accumulating, the validity of process digital twin technology itself is no longer in question.However, as the scope and functionality of digital twin expanded rapidly since the 2010s, new challenges emerged. The current challenge facing process industries is not the lack of individual technology performance, but rather integrating these technologies into a unified platform.SIMACRO's ProcessMetaverse™ addresses this integration challenge, presenting a new operational approach of "Intelligent Plant Operation" to process industries. This represents not just technological innovation but a fundamental transformation in operational approach, indicating that the future of the process digital twin will evolve into intelligent partners that learn and adapt in real-time.With headquarters in Boston and Seoul, SIMACRO has completed over 90 commercial modeling projects across 40 companies since 2018. Collaborating with global technology leaders such as AspenTech, Emerson, and OLI, SIMACRO is committed to advancing digital innovation in the process industry.About SIMACRODesignerDavid Vickery serves as SIMACRO Board Member and former Director of Next Generation Digital Twin at Aspen Technology, bringing 40 years of expertise in process industry solutions. His leadership experience at DMC, AspenTech, and Dow Chemical has contributed to the advancement of process digital twin technology, providing strategic guidance for PMv technology development.About David VickeryAbout David Arbeitel