Filling Valve Control for Pressure Disturbances in Food Packaging
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Solution Overview
Problem
Conventional control techniques, such as PID controllers, struggle to adapt to changing conditions in food packaging machines, leading to filling issues and pressure disturbances, requiring manual tuning and recalibration, which is time-consuming and inefficient.
Innovation Solution
Implementing a method that uses a deep reinforcement learning model, incorporating both local and remote variable values from the food packaging machine and external systems to adjust control parameters, allowing for precise control of the filling valve and handling unexpected pressure changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If PID controllers are used for filling valve control, then the control system is simple and easy to implement, but the system cannot adapt to changing conditions and requires manual tuning
Solution Approach 1:
The control system automatically adjusts control parameters by receiving feedback about system state changes and autonomously determining remediation actions, eliminating the need for manual intervention and enabling adaptation to changing conditions without increasing operational complexity
Solution Approach 2:
The system continuously monitors system state changes (such as pressure disturbances) and uses this feedback to automatically adjust control parameters, enabling the filling valve to adapt to changing conditions while maintaining a relatively simple control architecture
2Manufacturing precision
If manual tuning and recalibration of PID controllers is performed, then control precision can be improved, but significant time and manual input are required
Solution Approach 1:
The control system automatically detects system state changes and adjusts control parameters without requiring manual intervention, maintaining high filling control precision while eliminating the time-consuming manual tuning and recalibration processes
Solution Approach 2:
The system proactively monitors for system state changes and automatically implements control parameter adjustments before filling precision deteriorates, preventing the need for reactive manual recalibration and maintaining continuous high-precision operation
3Reliability
If conventional control techniques are used, then the control system is easy to operate, but filling issues and pressure disturbances occur
Solution Approach 1:
The system continuously monitors system state changes and automatically adjusts control parameters based on detected disturbances, significantly improving filling process reliability while maintaining ease of operation through automated remediation that requires no additional operator complexity
Solution Approach 2:
The system replaces conventional reactive control mechanisms with an automated intelligent control approach that detects system state changes and autonomously implements remediation, improving reliability without requiring complex manual intervention procedures
4Adaptability or versatility
If PID controller parameters are optimized for specific working range, then control performance is good within that range, but the system cannot handle unforeseen circumstances outside the working zone
Solution Approach 1:
The control system dynamically adapts control parameters based on detected system state changes, allowing the system to maintain high control precision across varying operating conditions and handle unforeseen circumstances that fall outside the original PID controller's optimized working range
Data Source
AI summary
Methods and apparatus, including computer program products, are described for filling packages (112) in a food packaging machine (100) with a food product, wherein the food packaging machine (100) comprises a plurality of sub-systems. One or more local variable values (116) are received, which indicate measurements by the food packaging machine (100) of one or more physical parameters for a local filling sub-system (300). One or more remote variable values (204) are received, which indicate measurements by the food packaging machine (100) of one or more physical parameters for one or remote sub-systems. One or more control parameter values are determined for the local filling sub-system (300) of the food packaging machine (100), by processing the remote (204) and local (116) variable values using a reinforcement learning model (206) and a local control model (210). One or more control parameters of the local filling sub-system (300) are adjusted in accordance with the determined control parameter values. The filling of packages (112) with food product by the food packaging machine (100) is controlled in accordance with the adjusted one or more control parameters.