DT2.0: Process the AI
Ramanathan B
Consulting for Digitalization Services | Industrial Automation and Control | Technical Sales | Predictive Maintenance | Operator Training Simulators | Industry 4.0|
An extra-terrestrial beginning
The concept of a?digital twin?is not new. The Apollo 13 mission almost 50 years ago perhaps showcased the first implementation of “Digital twin” allowing NASA mission controllers to simulate and adapt in near real-time a damaged spacecraft in space.
In the aftermath of an explosion affecting a spacecraft 200,000 miles away in space, Mission Control faced the challenge of diagnosing and resolving issues without direct human intervention. To safely return the astronauts home, Mission Control and the crew had to collaborate closely to navigate a severely damaged spacecraft operating beyond its intended design limits. They needed to devise creative solutions for conserving power, oxygen, and water while ensuring the survival of both the astronauts and the spacecraft systems. Additionally, they faced the unique challenge of restarting a command module that was not designed to be turned off in space.
The training simulators used during this crisis are purported as early examples of "digital twins,". These systems allowed for the exploration of possible solutions and various failure scenarios in a controlled environment but very closely mimicked the actual conditions of the spacecraft, thus demonstrating flexibility and adaptability of simulation and ability to handle crisis situations.
The Apollo 13 mission, which could have been NASA's greatest disaster, ultimately became a triumph thanks to the relentless efforts of hundreds of engineers, controllers and the simulators that gave the astronauts and “Digital Twin” a renewed life. From extraterrestrial beginnings to back on earth nearly half-a-century later, the concept of digital twins has evolved significantly, transitioning from aerospace to the industrial sector.
Hybrid Modeling?
Hybrid Modeling is an important aspect of Digital Twins, think of this concept as a journey to a new destination, where you have both a map and a local guide. In the context of AI and mechanistic simulation, the map represents AI models that provide a broad overview and general predictions of traffic guidance etc., while the local guide symbolizes mechanistic simulations that offer detailed insights and specific knowledge about underlying processes. For a traveler, this can mean recommendations on sightseeing places and must try local cuisines etc. As a result, the overall journey brings in an engaging user experience that exceeds the sum of its parts. Analogously, AI provides the overarching analysis, while mechanistic simulation offers depth and context. Hybrid modeling focuses on merging various approaches, resulting in a more comprehensive and enriching experience.
Mechanistic simulation would typically involve creating mathematical models that represent the physical processes of the system. This would help understand how the system would respond to changes in inputs or process conditions and integrate this with Artificial Intelligence, the solution can provide data driven insights and predictions. Hybrid Models help build an improved Digital Twin; this will mean bringing together the power of real time data, mechanistic simulation, and Artificial Intelligence to drive operational efficiency and better decision making.
Role in Process Industries
For industries, hybrid modeling in form of operational Digital Twin will mean an ability to provide insight, foresight, and hindsight to the operation cycle of industrial assets. The solution employs a timeline approach that integrates past historical data to understand past asset behavior, real-time data to assess current asset conditions, and leverages mechanistic simulations to determine the expected asset performance compared to actual performance. By combining these elements, the solution predicts the asset behavior with risk of failures and recommends actions to enhance outcomes based on current operating conditions.
Potentials of Hybrid Models in Digital Twins
Digital Twins through its synergetic approach of Hybrid models can help drive operational excellence in industrial operations through the following:
·??????? Detect Issues with Insight
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·??????? Impact and risk assessment using Knowledge
·??????? Forecast Remaining Useful Life/ Plan urgency of Assets
·??????? Optimize outcomes Through Operational Guidance
Let’s delve into each of these aspects in more detail.
Detect Issues with Insight: Hybrid models that use mechanistic simulation models can help create surrogate training datasets for predictive analytics. These models can also act as virtual sensors to calculate hard-to-measure parameters (e.g. heat transfer coefficients), thereby offering insights into system behavior. They also compute Key Performance Indicators (KPIs) that are crucial for assessing asset performance, enabling comparisons between ideal design conditions and actual operational data (e.g. Head vs Flow for centrifugal pumps). This approach helps identify process deviations and facilitates in-depth operational analysis.
Impact and risk assessment using Knowledge: The solution enables the execution of What-if scenarios to evaluate the effects of identified asset issues. It helps pinpoint root causes and potential disruptions while analyzing the costs associated with deviations and the likelihood of failure. Understanding the financial implications of equipment degradation is vital and it supports informed decision-making and prioritizes maintenance efforts more effectively.
For example: My heat exchanger is fouling and the lower heat transfer requires me to burn more fuel to achieve the desired setpoint which is costing x INR/day. How quickly should I take into line my standby heat exchanger to sustain optimized operations by saving fuel?
Forecast Remaining Useful Life/ Plan urgency of Assets: AI analyses asset data to predict remaining useful life by examining wear patterns and operational conditions. When combined with simulations, organizations can model various future scenarios, considering different strategies and maintenance schedules for more accurate maintenance urgency forecasts. Once economic impacts of identified issues are assessed, customers can decide to either wait for the next planned shutdown or take immediate action if costs are too high.
An example could be in furnace operations; let’s say the model predicts that we will breach a safe operating limit for tube skin metal temperature within two weeks but by 10% reduction in duct burner firing we can generate enough clearance in tube skin temperatures to continue operation and avoid an immediate forced outage and as the asset operates, we continue to monitor for further degradation.
Optimize outcomes Through Operational Guidance: Hybrid models deliver actionable insights by simulating the results of various operational decisions based on AI predictions. They can recommend optimal maintenance schedules, resource allocation, and process adjustments, enabling organizations to make data-driven choices that enhance efficiency and lower costs. To avoid unplanned downtime, organizations can extend operations during abnormal situations by derating until the next scheduled outage. Additionally, managing and optimizing maintenance events allows for prioritizing recurring activities, like gas turbine filter changes, to maximize maintenance investment returns.
The solution can help track cumulative costs of gas turbine filter fouling and assess economic life of filters and payback times for filter replacements, thereby force a change only when the payback in terms of gas consumption benefit is worth it, this can help drive optimized maintenance intervals and push down unnecessary maintenance expenses.
Conclusion
The rise of Industry 4.0 is transforming manufacturing and industrial processes through the integration of advanced technologies, with the digital twin concept as an important cog in the wheel. By combining AI with process simulation, digital twins enable real-time monitoring, analysis, and optimization of operations. This integration enhances decision-making by leveraging historical data and real-time inputs, facilitating predictive maintenance and minimizing unplanned downtime.
As businesses seek sustainable and efficient operations, AI-driven simulations help anticipate potential issues and optimize performance, ultimately leading to improved productivity and cost savings. Embracing this transformative approach fosters a proactive culture that harnesses data-driven insights for continuous improvement in the evolving landscape of Industry 4.0.
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