Flare Emission Monitoring With Adaptive Combustion Efficiency Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing flare emission monitoring systems rely on static models that do not account for changes in environmental and process conditions, leading to inaccurate estimates of combustion efficiency and emissions.
Innovation Solution
A method and system that use real-time measurements of process and environmental conditions to select between empirical and non-parametric machine learning models to determine combustion efficiency, incorporating multi-spectral imaging for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static flare design models are used for emission monitoring, then the system complexity is low and ease of operation is maintained, but measurement precision and reliability of emission estimates deteriorate due to inability to account for dynamic environmental and process conditions
Solution Approach 1:
The system transitions from static emission models to dynamic modeling that continuously adapts to changing environmental conditions (wind speed, temperature, humidity) and process conditions (gas composition, flow rate, pressure). The combustion efficiency model is updated in real-time using online measurements, allowing the system to maintain high measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The system implements feedback loops where online sensors continuously measure combustion parameters (flame temperature, gas composition, flow rates) and environmental conditions, which are then fed back to update the emission estimates. This closed-loop approach improves measurement precision by continuously correcting for deviations from static model assumptions.
2Measurement precision
If multi-spectral imaging is used for direct emission measurement, then measurement precision improves through direct detection of combustion products, but device complexity and cost increase significantly
Solution Approach 1:
Instead of using complex multi-spectral cameras to directly measure emissions, the system uses simpler online sensors (infrared cameras, gas chromatographs, flow meters) to measure combustion parameters and environmental conditions. These measurements are then used to calculate combustion efficiency and emissions through computational models, creating a virtual copy of the emission measurement process that avoids the complexity of direct optical detection.
Solution Approach 2:
The system replaces the mechanical/optical complexity of multi-spectral imaging systems with computational methods. Rather than using complex optical filters and detectors to directly image combustion products, the system uses standard sensors combined with radiative transfer modeling and chemical kinetics calculations to infer emission rates, substituting mechanical complexity with computational processing.
3Adaptability or versatility
If empirical models are used for combustion efficiency estimation, then device complexity is low and ease of operation is maintained, but adaptability to changing process conditions deteriorates
Solution Approach 1:
The system employs multiple combustion efficiency models (empirical, semi-empirical, and detailed chemical kinetics models) that can be selected based on the specific operating conditions. When process conditions are stable, simpler empirical models are used. When conditions change dynamically, the system switches to more sophisticated models that account for variable gas composition, temperature, and pressure, thereby improving adaptability while managing computational complexity through conditional model selection.
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
Provides more accurate estimates of combustion efficiency and emissions by adapting to dynamic conditions, optimizing the use of online sensors and computational resources.
Implementation Method 1
the vented gas and/or its combustion products absorb light at a unique combination of frequencies (a unique spectrum) that can be detected
Implementation Method 2
Flaring is a process of combusting gases in an industrial process
Data Source
AI summary
Systems and methods for monitoring emissions of a combusted gas are provided. The method includes determining a first net heating value of a flare gas. The method also includes determining a second net heating value of a combustion gas including the flare gas. The second net heating value can be determined based upon the first net heating value and a volumetric flow rate of the flare gas. Based upon the value of the second net heating value, an empirical model or a non-parametric machine learning model can be selected. A combustion efficiency of the combustion gas can be determined using the selected model, the second net heating value, and selected ones of the process conditions and the environmental conditions. Total emissions of the combustion mixture can be further determined from the combustion efficiency and a volumetric flow rate of the combustion gas.