Adaptive Process Control for Variable Input Materials
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Solution Overview
Problem
Existing process control methods struggle to maintain predictability due to significant variations in input materials, leading to deviations in process parameters and final product quality, resulting in potential losses for producers.
Innovation Solution
A method employing an adaptive control system that uses an adaptive control model to predict targets, adapt parameters based on inputs, and preprocess signals for non-linear parameter adaptation, target prediction, and driver tuning, incorporating manipulated and non-manipulated variables to optimize process control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional process control models are used, then basic process regulation is achieved, but process predictability deteriorates due to significant variations in input materials
Solution Approach 1:
The control model transitions from static to dynamic by continuously adapting parameters based on real-time input material characteristics. The system dynamically adjusts process parameters to maintain predictability despite variations in input materials, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The system changes parameters of the control model itself to adapt to different input materials. By modifying model parameters in real-time based on input material analysis, the system maintains process predictability across varying conditions, addressing both reliability and adaptability requirements.
2Manufacturing precision
If process parameters are strictly controlled to maintain product quality, then manufacturing precision is improved, but loss of time increases due to frequent adjustments and corrections
Solution Approach 1:
The system performs preliminary analysis of input materials and pre-adjusts process parameters before deviations occur. By predicting required parameter changes in advance based on input material characteristics, the system maintains product quality without requiring frequent corrective adjustments, thus reducing time loss.
Solution Approach 2:
The system implements continuous feedback loops that monitor both input material properties and process parameters. This feedback mechanism enables proactive parameter adjustments that maintain manufacturing precision while minimizing the time and frequency of corrections needed.
3Adaptability or versatility
If complex adaptive control systems are implemented to handle input variations, then adaptability is improved, but device complexity increases
Solution Approach 1:
The control system performs self-adjustment by automatically adapting its parameters based on input material analysis. This self-service capability reduces the need for external intervention and complex manual control mechanisms, achieving high adaptability without proportionally increasing device complexity.
Solution Approach 2:
The control model is designed with universal adaptability to handle various types of input material variations through a single unified framework. This multi-functional approach allows the system to adapt to different scenarios without requiring separate specialized subsystems, thereby limiting the increase in device complexity.
Data Source
AI summary
The invention relates to method for controlling a process, the method comprising an adaptive control model and at least one process input and at least one process output, the control model comprising predicting the relevant targets in the process; and selecting the relevant drivers for the process based on the target prediction, where the method preferably comprises adapting a number of parameters based on one or more inputs, and using the adapted parameters as an input for the target prediction.