AI Temperature Estimation in Mixed Fluid Chamber
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Solution Overview
Problem
Conventional temperature measurement methods in mixed fluid chambers, such as those used in process control industries, face challenges with accuracy and response time, often requiring manual interventions and failing to provide continuous, reliable measurements.
Innovation Solution
An artificial intelligence-based temperature measurement system that utilizes sound velocity information from acoustic pyrometers, chemical sensor data, and thermocouple data, employing Bernoulli and state space fluid models to generate ground truth temperature data and train AI models for accurate, continuous monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional instruments such as thermocouples are used for temperature measurement, then temperature data can be obtained, but the measurement accuracy and response time are insufficient and manual interventions are required
Solution Approach 1:
The patent replaces conventional mechanical contact-based thermocouples with a non-contact acoustic pyrometer system that uses sound velocity measurements to determine temperature. This substitution eliminates the need for physical contact with the mixed fluid, enabling continuous automated measurement without manual intervention while improving both accuracy and response time.
Solution Approach 2:
The patent introduces sound velocity as an intermediary parameter to indirectly measure temperature. Instead of directly measuring temperature with contact sensors, the system measures sound velocity in the mixed fluid and uses the known relationship between sound velocity and temperature to determine the temperature, thereby achieving non-contact continuous measurement.
2Productivity
If non-contact acoustic pyrometer based technique is used, then continuous automated temperature measurement is achieved, but inherent drift and noise affect measurement reliability
Solution Approach 1:
The patent combines multiple acoustic pyrometer measurements taken at different locations within the mixed fluid chamber to generate a composite temperature profile. This merging of multiple measurements compensates for local drift and noise, improving overall measurement reliability while maintaining continuous automated operation.
Solution Approach 2:
The system implements feedback mechanisms where temperature measurements from acoustic pyrometers are continuously monitored and compared against expected values. When drift or noise is detected, the system adjusts measurements and uses fluid dynamics models to compensate, thereby maintaining measurement reliability over time.
3Reliability
If multiple acoustic transmitter and receiver pairs are deployed, then measurement reliability is improved by reducing drift and noise, but device complexity increases
Solution Approach 1:
The patent divides the mixed fluid chamber into multiple measurement zones, each monitored by dedicated acoustic transmitter and receiver pairs. This segmentation allows independent measurement in each zone, improving overall reliability through spatial distribution while managing complexity by organizing sensors into modular units corresponding to specific regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate, continuous temperature monitoring and feedback, improving process control and reducing emissions by enhancing the precision and reliability of temperature measurements in mixed fluid chambers.
Implementation Method 1
sound velocity information determined using an acoustic pyrometer based technique
Implementation Method 2
generating ground truth temperature data for the mixed fluid chamber using a gradient based heat diffusion model
Data Source
AI summary
Temperature measurement is an important part of many potential applications in process industry. Conventional temperature measurement methods require manual intervention for process monitoring and fail to provide accurate and precise measurement of temperature of an enclosed mixed fluid chamber. The present disclosure provides artificial intelligence based temperature measurement in mixed fluid chamber. A plurality of inputs pertaining to the mixed fluid chamber are received to build a fluid based model. The fluid based model is used to generate one or more fluid parameters. The one or more fluid parameters are used along with a ground truth temperature data and the received plurality of inputs for training an artificial intelligence (AI) based model. However, the AI based model is trained with and without knowledge of fluid flow. The trained AI based model is further used to accurately estimate temperature of the mixed fluid chamber for a plurality of test input data.