Heater System State Detection Using Intermediate Control Data
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Solution Overview
Problem
Existing thermal systems in industrial processes face challenges in accurately controlling temperature and process variables due to external factors that are uncontrollable by the thermal system, leading to difficulties in obtaining reliable data for process monitoring.
Innovation Solution
A method is introduced that involves monitoring intermediate data from the process control system, associating it with correlation data, and generating a model to define the relationship between these data sets. This model is used to identify the state of the heater system, allowing for selective corrective actions to be taken based on the identified state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PID control is used to monitor process variables, then basic temperature control is achieved, but accurate and reliable data for external factors cannot be obtained
Solution Approach 1:
The patent introduces an intermediary data processing layer that sits between the traditional PID control system and the external factor monitoring. This intermediary layer processes intermediate data from the PID controller and correlates it with external factors, enabling accurate measurement of external influences without requiring direct sensors for each factor. The intermediary processing transforms complex monitoring requirements into manageable data correlations.
Solution Approach 2:
The patent replaces physical sensors and direct mechanical measurement systems with a data-driven modeling approach. Instead of installing sensors for every external factor (gases, pressure differentials, wafer types), the system uses mathematical models that correlate PID intermediate data with these external factors, substituting physical measurement infrastructure with computational analysis.
2Reliability
If more sensors are added to monitor external factors, then measurement accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent makes the existing PID controller multi-functional by extracting intermediate data that serves dual purposes: maintaining temperature control and providing information about external factors. The same PID controller that regulates temperature also generates intermediate data that, when processed, reveals information about gases, pressure differentials, and other external factors, eliminating the need for separate monitoring systems.
Solution Approach 2:
The system uses its own intermediate data to monitor external factors. The PID controller's internal data processing generates information that serves both its primary control function and the secondary function of external factor monitoring, making the system self-sufficient for both purposes without requiring additional external monitoring infrastructure.
3Manufacturing precision
If intermediate data processing and modeling are implemented, then control accuracy and diagnostics improve, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary data processing by continuously collecting and pre-processing intermediate data from the PID controller during normal operation. Correlation data is accumulated and prepared in advance, so when diagnostic or control adjustments are needed, the modeling process can proceed quickly using pre-organized data rather than collecting raw data from scratch.
Solution Approach 2:
The system changes the parameters being monitored from raw sensor readings to processed intermediate data from the PID controller. By transforming the data representation and using derived parameters (proportional gain data, integral gain data, derivative gain data) instead of raw measurements, the system achieves better precision while the computational load remains manageable due to the structured nature of PID intermediate calculations.
Data Source
AI summary
A method of controlling a thermal system of an industrial process includes monitoring intermediate data, associating the intermediate data with correlation data, wherein the correlation data includes an internal process control input, an external heater control input, the output control, or a combination thereof. The method further includes generating a model that defines a relationship between the intermediate data and the correlation data, identifying a state of the heater system based on the model, and selectively performing a corrective action based on the identified state of the heater system.