ProcessMetaverse™
ProcessMetaverse™
ProcessMetaverse™ is an industrial SaaS solution designed for online digital twin
ProcessMetaverse™ would enable:
Faster online deployment over traditional solutions
Reduced costs for digital twin model maintenance
Improved user experience, efficiency and flexibility
Industrial SaaS Solution Designed for Online Digital Twins
Solution Highlights:
Process Canvas™
An infinite canvas for integrating and analyzing industrial big data and digital twin metadata, empowered by various widgets & apps, advanced visualization and AI-driven insights
Digital Twin Model Manager
A user-friendly simulation widget for configuring first-principles process models (e.g., Aspen) to enable real-time representation (so-called “mirror-model”), real-time optimization, and short or long-term predictions
Python Editor
A user-friendly python widget for real-time creation and utilization of ML-based data models, enhancing process insights and optimization
Process Agent™
A generative AI-powered copilot that optimizes intelligent workflows, driving operational efficiency and productivity through advanced assistance and automated solutions
Process Canvas™
Core Features and Capabilities:
An infinite, interactive canvas workspace (so-called “Layout”) within the PMv solution, designed to integrate, analyze, and visualize industrial big data and digital twin metadata
Revolutionizing the readability of complex industrial data, it delivers insights quickly to users while providing a foundation for running simulation-based and ML-driven digital twin models using real-time data
Enables collaborative workflows with easy sharing and customizable layouts
Digital Twin Model Manager
Core Features and Capabilities:
Instantiates online executable digital twin models that seamlessly integrate with live process data, delivering accurate and dynamic representations of process operations at real-time
Enables what-if scenario testing to predict outcomes under various operational conditions, starting from the current real-time conditions
Python Editor
Core Features and Capabilities:
Real-time, online deployment of reinforcement learning ML models using live sensor data or a combination of sensor data and Aspen digital twin metadata for advanced process optimization and decision-making
Seamless integration and reuse of existing Python-based machine learning models
Simplifies the creation of customized models tailored to specific user needs with minimal effort
Process Agent™ (Gen-AI Copilot)
Core Features and Capabilities:
Provides context-aware assistance, generating tailored recommendations using real-time process data and historical insights
Automates detailed process reports, model summaries, and operational insights with precision and consistency
This powerful AI companion not only enhances the usability of the PMv solution but also drives value by making industrial processes smarter, more efficient, and more adaptive to change
Applicable Areas
Core Features and Capabilities:
ProcessMetaverse™ (PMv) is particularly well-suited for processes that involve repetitive production and monitoring
It excels in industries where simulation tools, such as Aspen modeling software, have already proven effective
Going beyond existing solutions, PMv delivers exceptional value in industries where process efficiency is impacted by variability, such as raw material variability and seasonal or daily weather changes
With its powerful online monitoring, prediction and optimization capabilities at real-time, PMv ensures reliable and efficient operations, even in dynamic and VUCA (volatile, uncertain, complex, and ambiguous) environments
Applicable Areas
By industry classification:
Optimizing complex reaction and separation processes (e.g., catalytic reactions, distillation processes)
Improving process and energy efficiencies through the optimal management of large-scale heat exchanger networks, such as refinery preheat trains
Dynamic monitoring and optimization of batch and continuous bioprocesses, including fermentation, cell culture, and downstream purification steps