Digital Twin Plant Optimization for Real-Time Set-Point Adjustment
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Solution Overview
Problem
Monitoring and optimizing the operation of processing plants is challenging due to changing conditions and the complexity of managing asset performance, leading to suboptimal efficiency and difficulty in identifying effective adjustments for maintaining optimal performance.
Innovation Solution
A data processing system that analyzes operating conditions and asset settings using models, such as digital twins, to provide real-time adjustments and optimize plant performance by selecting specific performance parameters and set-points, allowing for efficient configuration and monitoring of plant operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and analysis of plant assets is performed, then operators can identify performance issues, but the complexity of managing asset performance and identifying effective adjustments increases significantly
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical plant assets that replicates their behavior and performance characteristics. This digital model allows operators to monitor, analyze, and optimize asset performance without directly managing the complexity of the physical assets, thereby improving monitoring reliability while reducing management complexity.
Solution Approach 2:
The digital twin serves as an intermediary between the physical assets and the operators. It processes complex asset data, simulates performance scenarios, and provides actionable insights, thereby simplifying the operator's task of identifying effective adjustments while maintaining reliable performance monitoring.
2Reliability
If operators manually analyze operating conditions to maintain optimal performance, then they can make adjustments, but the time required to identify effective adjustments increases
Solution Approach 1:
The digital twin is pre-configured with asset performance models, historical data, and optimization algorithms. When performance issues arise, the system can immediately simulate and identify effective adjustments without requiring operators to perform time-consuming manual analysis, thus maintaining optimal performance while reducing the time to identify adjustments.
Solution Approach 2:
The system continuously monitors actual asset performance against the digital twin model and provides real-time feedback on performance deviations and recommended adjustments. This automated feedback loop enables rapid identification of effective adjustments while maintaining reliable optimal performance, eliminating the need for slow manual analysis.
3Measurement precision
If detailed monitoring of all asset parameters is implemented, then performance issues can be detected, but the amount of data to be analyzed increases complexity
Solution Approach 1:
The digital twin creates a virtual representation of all asset parameters and their interrelationships. This model automatically processes and correlates detailed measurement data, transforming complex multi-parameter data into actionable performance insights. Operators gain precise measurement capabilities without bearing the complexity of analyzing raw detailed data.
Solution Approach 2:
The digital twin model acts as an intermediary that receives detailed asset parameter measurements, processes them through performance models and algorithms, and outputs simplified performance assessments and adjustment recommendations. This intermediary layer maintains high measurement precision while eliminating data analysis complexity for operators.
4Productivity
If real-time optimization of plant operation is achieved, then efficiency improves, but the need for complex optimization algorithms and models increases
Solution Approach 1:
The digital twin provides a virtual environment where complex optimization algorithms can be executed and tested without affecting actual plant operations. The model captures asset behavior and constraints, allowing automated real-time optimization while shielding operators from the complexity of the underlying algorithms and models.
Solution Approach 2:
The digital twin serves as an intermediary that handles complex optimization calculations and model-based analysis automatically. It processes optimization algorithms in the virtual model and translates results into practical adjustment recommendations for the physical plant, thereby achieving real-time efficiency improvement while hiding optimization model complexity from users.
Data Source
AI summary
A data processing system enables a user to select performance indicators in terms of which a plant operation is to be optimized. The system receives data from instruments monitoring plant operation and a user selection of a performance indicators to optimize in accordance with one or more set-points for assets at the plant. The system generates a plurality of values for the performance indicator via the model and based on the data and a plurality of test values for the set-point. The system determines one or more settings for one or more set-points of at least one of the plurality of assets at the plant based on the plurality of values for the performance indicator and the plurality of test values for the set-point and provides the one or more settings for the one or more set-points to adjust the performance of the plant.