Just-In-Time Raw Material Characterization for Adaptive Plant Control
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Solution Overview
Problem
Existing industrial process control systems fail to account for real-time variations in raw material quality, leading to unpredictable deviations in plant performance and output quality, particularly when mixing different raw materials, and lack effective methods to optimize operations during material transitions.
Innovation Solution
A processor-based system that analyzes plant data to detect changes in raw materials, classify them into defined or new classes, predict material characteristics, and select predictive models to optimize plant performance by generating recommendations based on actual performance thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control systems use design materials for control parameters, then system simplicity is maintained, but plant performance deviates when material quality varies
Solution Approach 1:
The control system dynamically adapts control parameters based on real-time raw material quality characteristics. Instead of using fixed design material parameters, the system continuously updates control settings according to actual material properties detected during operation, enabling the plant to maintain optimal performance despite material variations
Solution Approach 2:
The system changes control parameters based on measured raw material quality parameters. By establishing relationships between material characteristics and optimal process parameters, the system automatically adjusts operational settings to match the specific material being processed, resolving the conflict between maintaining simple control logic and adapting to material variations
2Productivity
If real-time raw material quality measurement is implemented, then plant optimization is improved, but measurement cost increases
Solution Approach 1:
The system uses intermediary parameters that are easier and cheaper to measure than direct material quality characteristics. By measuring process response variables that correlate with material quality, the system indirectly infers raw material properties without requiring expensive direct measurement equipment, thus achieving plant optimization at lower cost
Solution Approach 2:
The system replaces expensive physical measurement equipment with data-driven modeling approaches. By using machine learning models that predict material quality from readily available process data, the system eliminates the need for costly real-time material analysis instruments while maintaining the ability to optimize plant operations
3Reliability
If material transition tracking is performed during blending periods, then performance control is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary tracking and prediction during the material transition period by detecting changes in process parameters that indicate blending is occurring. By identifying transition periods early through parameter monitoring and preparing appropriate control adjustments in advance, the system maintains performance control without requiring complex real-time intervention systems during the actual blending process
Data Source
AI summary
In an industrial plant, various equipment are used to handle processing of raw materials. Considering complexities involved in the processes and the equipment, constant monitoring is required to obtain desired results. The disclosure herein generally relates to industrial process and equipment monitoring, and, more particularly, to data analysis for Just In Time (JIT) characterization of raw materials in any process industry. The system collects real-time plant data among other inputs, and performs characterization of raw materials being used in the plant. The characterization involves categorizing the raw materials into different classes. The class information is further used to predict performance of the industrial plant, and in turn to generate recommendations for optimization of the industrial plant.