Food Plant Subsystem Control Configuration via Digital Twin Tuning
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Solution Overview
Problem
Configuring control devices in food production plants is a time-consuming and inefficient process, often relying on manual tuning by experienced engineers, which can result in less than perfect operation due to the complexity of handling variations in ingredients and equipment configurations.
Innovation Solution
A computer-implemented method using a digital twin of the processing operation to facilitate the tuning of control devices by creating a virtual representation of the sub-system, allowing for separate and automated adjustment of control parameters, reducing reliance on manual trial-and-error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning by commissioning engineers is used to configure control devices, then the control devices can be configured with existing expertise, but the process is time-consuming and results are inconsistent
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the food production sub-system that replicates the physical system's behavior. This digital model can be tuned and simulated without affecting the actual production process, allowing multiple configuration scenarios to be tested and optimized before deploying to the real control devices, thereby reducing both time and improving accuracy.
Solution Approach 2:
The patent performs control parameter tuning and optimization in advance using the digital twin model before actual deployment. By conducting simulations, identifying optimal parameters, and validating configurations beforehand, the system eliminates time-consuming trial-and-error tuning on the actual production line while ensuring reliable control device configuration.
2Adaptability or versatility
If manual trial-and-error tuning is used for control devices, then existing engineering expertise can be utilized, but the process lacks consistency and requires high expertise
Solution Approach 1:
The digital twin provides a virtual environment where control configurations can be freely experimented with without risking actual production. Engineers can test multiple approaches, learn from simulated results, and develop optimized configurations in a risk-free setting, making the tuning process more systematic and less dependent on individual expertise.
Solution Approach 2:
The system implements automated feedback loops that simulate control device performance in the digital twin and provide quantitative performance metrics. This automated feedback replaces subjective engineer judgment with objective data, guiding the tuning process systematically and reducing reliance on individual expertise while maintaining adaptability.
3Adaptability or versatility
If multiple control scenarios are handled manually, then various food production requirements can be addressed, but the process becomes highly time-consuming
Solution Approach 1:
The digital twin enables parallel simulation of multiple control scenarios that would otherwise require sequential manual tuning. Different food production requirements and control scenarios can be tested simultaneously in the virtual environment, dramatically increasing configuration throughput while maintaining comprehensive scenario coverage.
Solution Approach 2:
The system pre-configures and pre-validates multiple control scenarios in the digital twin before actual deployment. By preparing various control configurations in advance through simulation and validation, the system can rapidly deploy appropriate configurations for different food production requirements without time-consuming manual tuning for each scenario.
Data Source
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AI summary
A method is implemented on a computer device to configure a control device for closed-loop control of a sub-system in a plant for production of food products. The computer device obtains (401) definition data, DD, which indicates a task performed by the sub-system in the production of food products and equipment in the sub-system for performing the task; derives (402) a candidate process model, CPM, of the sub-system based on DD; obtains (405) measurement data, MD, generated by the sub-system when operated in accordance with a test sequence, TS; estimates (406) constant parameter(s) of differential equation(s) in the CPM based on TS and MD; defines (407) a final process model, APM, for the sub-system based on the differential equation(s) and the constant parameter(s); and operates (408) a tuning algorithm on APM to determine control parameter(s) of the control device (12) for closed-loop control.