Spiro Digital Twin
Spiro Digital Twin provides deep insight into process performance, allowing operators to optimize their operations in real-time. The result is lower emissions and improved operating margins.
Spiro Digital Twin
Spiro Digital Twin provides deep insight into process performance, allowing operators to optimize their operations in real-time. The result is lower emissions and improved operating margins.
AI-driven machine learning and first-principles thermodynamic models
Spiro Digital Twin utilizes AI-driven machine learning, thermodynamic models, and real-time data integration to optimize production processes. It helps process operators to manage emissions, improve operational efficiency, address production constraints and bottlenecks, and to make informed planning decisions.
Meeting the needs of process plant operators
Spiro helps process operators, planners and supervisors to make accurate and consistent decisions. Spiro Digital Twin blends physics- and data-driven AI to support optimization throughout the product’s lifecycle. We take a complete, open, and flexible approach that enables your digital transformation vision on your terms.
Meeting the needs of process plant operators
Spiro helps process operators, planners and supervisors to make accurate and consistent decisions. Spiro Digital Twin blends physics- and data-driven AI to support optimization throughout the product’s lifecycle. We take a complete, open, and flexible approach that enables your digital transformation vision on your terms.
Hybrid Digital Twin Models in an Open-Equation Framework
Spiro Digital Twin uses a hybrid modelling approach integrating traditional process flow-sheeting with data-driven machine learning and artificial intelligence. Our software uses an equation-oriented modelling environment, that combines a comprehensive library of standard unit operations and a physical property package with data-driven inferential and surrogate models. By enforcing basic physical laws—such as energy and mass balances, and elemental conservation—our model approach eliminates the need to train these principles explicitly.
Digital Twin meets the needs of process plant operators
Operating objectives:
- Ensure safe, reliable operations while minimizing emissions and flaring.
- Achieve production targets with optimal product quality, yields, and resource efficiency.
Digital Twin meets the needs of process plant operators
Operating objectives:
- Ensure safe, reliable operations while minimizing emissions and flaring.
- Achieve production targets with optimal product quality, yields, and resource efficiency.
Daily Challenges:
- Fluctuations in feedstock availability, pricing, and consumer demand.
- Equipment availability issues due to changing conditions, maintenance, or reliability concerns.
Daily Challenges:
- Fluctuations in feedstock availability, pricing, and consumer demand.
- Equipment availability issues due to changing conditions, maintenance, or reliability concerns.
Deep Learning Models
Spiro’s deep learning models are capable of modeling complex reaction units, such as ethylene furnaces. The training techniques enforce key constraints like heat and mass balance, ensuring that the resulting models always align with physical principles. Once trained, the furnace model can be reliably deployed in closed-loop operations to optimize furnace severity. It can also be integrated into a broader olefins process model to predict effluent composition for downstream separation models or provide accurate inferential variables used as APC constraints.
Enhanced Decision-Making and Process Optimization
Spiro Digital Twin provides dynamic optimization of refinery and olefins processes, delivering a real-time solution that is continuously updated with evolving process conditions. The key functionalities include:
- Real-time optimization through online models that solve complex non-linear problems.
- What-if scenario analysis for predictive decision-making, allowing engineers to test various operational conditions with minimal effort.
- KPI dashboards that enhance awareness of constraints and optimization opportunities, empowering shift leaders and planners to act on real-time data.
USE CASE
Shift managers and planners on a petrochemical processes rely on a digital twin to make timely, informed decisions that optimize production and maximize profits. At the start of a new shift, the shift leader needs to quickly assess not only the performance of their plant but also the status of any connected processes and storage facilities.
The digital twin helps anticipate the impact of changes in feedstock or inventory and provides recommendations for optimal adjustments throughout the shift. Even factors like the weather forecast can influence these decisions and are incorporated into the model.
By reviewing dashboards powered by validated data from the digital twin, the shift leader can evaluate overall process performance through key indicators such as energy consumption and net carbon emissions. Additionally, data analysis helps predict equipment outages and adjust maintenance plans accordingly.
Shift managers and planners on a petrochemical processes rely on a digital twin to make timely, informed decisions that optimize production and maximize profits. At the start of a new shift, the shift leader needs to quickly assess not only the performance of their plant but also the status of any connected processes and storage facilities.
The digital twin helps anticipate the impact of changes in feedstock or inventory and provides recommendations for optimal adjustments throughout the shift. Even factors like the weather forecast can influence these decisions and are incorporated into the model.
By reviewing dashboards powered by validated data from the digital twin, the shift leader can evaluate overall process performance through key indicators such as energy consumption and net carbon emissions. Additionally, data analysis helps predict equipment outages and adjust maintenance plans accordingly.
Why should you power digital twins with Spiro Control?
Why should you power digital twins with Spiro Control?
Provides an accurate model that reflects real process conditions and molecular components.
Uses real-time data to ensure that decisions reflect the current process state and the latest planning information.
Ensures that all plant constraints and operating objectives are accounted for.
Provides what-if tools to evaluate future operating scenarios and to audit past events for improved understanding and knowledge.
Allows users to save and share what-if cases.
Provides dashboards to monitor and benchmark current performance.
Provides time series trends to evaluate past scenarios.
A simple tool that is accessible to all stakeholders for improved decision-making.
Or schedule a live demo
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Or schedule a live demo
Use the calendar below to schedule a meeting with a member of our team.
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