Asset Simulation Analytics With Real-Time ML Feedback
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Solution Overview
Problem
Current systems for monitoring and simulating equipment in processing facilities face challenges in efficiently displaying graphical information and measurement data, and in providing accurate simulations and recommended operations, particularly due to the lack of real-time data updates and static training data.
Innovation Solution
A method involving a machine learning model that identifies target operational parameters, correlates them using historical and real-time data, and provides recommended actions through a user interface, enabling efficient monitoring and simulation of equipment operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static training data is used for machine learning models, then model development is simpler and faster, but simulation accuracy and relevance to current operations deteriorate
Solution Approach 1:
The system implements dynamic training data updates by automatically fetching real-time operational data from equipment sensors and incorporating it into the machine learning model's training dataset. This allows the model to adapt to changing operational conditions while maintaining development efficiency through automated data pipelines and incremental retraining mechanisms.
Solution Approach 2:
The system establishes a feedback loop where simulation results and actual equipment performance data are continuously compared. This feedback is used to identify discrepancies and trigger automated retraining of the machine learning model with updated real-world data, ensuring the model remains accurate and relevant without requiring manual intervention.
2Reliability
If real-time data updates are implemented, then simulation accuracy and recommendation relevance improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary data validation, cleaning, and preprocessing operations as data is ingested from sensors and external sources. By preparing the data in advance before it reaches the machine learning model, the system reduces the computational burden during simulation and avoids the need for complex real-time processing architectures.
Solution Approach 2:
The system introduces intermediate data processing layers including data validation modules, feature extraction components, and caching mechanisms that mediate between raw sensor data and the machine learning model. These intermediaries simplify the overall system architecture by handling data preparation tasks separately from the core simulation engine.
3Reliability
If comprehensive operational parameters are monitored, then simulation accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system extracts and focuses on the most critical operational parameters that have the greatest impact on simulation accuracy. By identifying and isolating key parameters through feature importance analysis, the system processes only the essential data elements, reducing overall processing time while maintaining simulation quality.
Solution Approach 2:
The system applies different processing levels to different parameters based on their importance and characteristics. Critical parameters receive more rigorous validation and processing, while less important parameters undergo simplified handling. This localized quality approach optimizes the balance between comprehensive monitoring and processing efficiency.
Data Source
AI summary
A system identifies a target value of one or more first operational parameters associated with an asset. The system provides the target value of the one or more first operational parameters to a machine learning model. The system receives a second target value of one or more second operational parameters associated with the asset. The system simulates one or more operations of the asset using the second target value of the one or more second operational parameters. The system provides, via a user interface, one or more recommended actions in response to a result associated with simulating the one or more operations.