Furnace Predictive Temperature Control Without Molten Sensors
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Solution Overview
Problem
Combustion furnaces rely on physical sensors that provide lagging temperature measurements, leading to sub-optimal future operation.
Innovation Solution
A method and system that utilize sensors to collect operating data, input it into a model trained on historical data to predict molten batch material temperature, and adjust furnace parameters automatically based on the prediction, without direct temperature sensors in the production cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical sensors are used to monitor furnace temperature, then current temperature conditions can be detected, but the measurements are lagging and lead to sub-optimal future furnace operation
Solution Approach 1:
The predictive model performs preliminary calculations to forecast future temperature conditions before they actually occur. By analyzing current operating parameters (fuel flow rate, air flow rate, batch material properties) and historical data, the model predicts temperature trends ahead of time, allowing operators to take preventive actions rather than reactive actions based on lagging sensor data.
Solution Approach 2:
A predictive modeling system acts as an intermediary between physical sensors and control decisions. Instead of directly using sensor readings, the system processes these readings through a trained model that incorporates thermal dynamics, heat transfer equations, and historical patterns to generate predicted future temperatures, thereby eliminating the lag inherent in direct sensor measurement.
2Measurement precision
If physical temperature sensors are placed in molten batch material, then direct temperature measurements can be obtained, but sensor wear and failure increase
Solution Approach 1:
Instead of directly measuring temperature with physical sensors in the harsh molten material environment, the system creates a virtual copy of the temperature measurement through predictive modeling. The model replicates the temperature measurement function by calculating predicted temperatures based on operating parameters and thermal dynamics, eliminating the need for physical sensors to be exposed to corrosive conditions.
Solution Approach 2:
The patent replaces the mechanical/physical sensing system with a computational model. Instead of using physical temperature sensors that require direct contact with molten batch material, the system uses a trained predictive model that processes operating parameters (fuel flow, air flow, batch composition) to generate temperature predictions, substituting computational methods for physical measurement devices.
3Loss of information
If more physical sensors are installed to improve monitoring, then measurement coverage increases, but system complexity and cost increase
Solution Approach 1:
The predictive model serves multiple functions simultaneously: it predicts future temperature, identifies optimal operating parameters, detects potential problems before they occur, and provides insights into furnace behavior. This single computational system replaces what would otherwise require multiple specialized sensors and analysis systems, reducing overall system complexity while improving information availability.
Data Source
AI summary
A method for operating a furnace includes: providing a furnace comprising an inlet into which batch materials are fed and an outlet from which a product emerges, the batch materials within the furnace melt to form a molten batch material; monitoring a parameter at which the furnace operates using a sensor to collect operating data, the parameter monitored not including a temperature measurement of the molten batch material in the furnace; inputting the collected operating data into a model configured to generate a predicted temperature of the molten batch material in the furnace at a first time in the future, the model trained on historical operating data of the furnace; generating the predicted temperature of the molten batch material in the furnace at the first time; and automatically adjusting the parameter based on the predicted temperature of the molten batch material in the furnace at the first time.