Flare Smoke and Noise Reduction via MPC Assist Gas Control
Solution Overview
Problem
Current flaring processes in petroleum and petrochemical industries face challenges in reducing smoke and noise emissions due to inefficient combustion and flare tip degradation over time, leading to non-compliance with environmental regulations and resulting in financial penalties.
Innovation Solution
The implementation of a Model Predictive Control (MPC) model-based feedback control system that automatically adjusts assist gas flow using sensors and mathematical modeling to minimize smoke and noise emissions by interacting one-to-one models of smoke, noise, and process gas flow, allowing for real-time control and adherence to regulatory limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If flaring process is used to dispose waste gases, then environmental pollution is reduced, but smoke and noise emissions increase
Solution Approach 1:
The patent implements a feedback control system that continuously monitors smoke levels using optical sensors and noise levels using acoustic sensors. The control system adjusts the assist gas flow rate based on real-time measurements of smoke density and noise intensity, creating a closed-loop control mechanism that dynamically optimizes combustion conditions to minimize harmful emissions while maintaining waste gas disposal effectiveness
Solution Approach 2:
The patent changes key combustion parameters including assist gas flow rate, steam injection rate, and flare tip temperature to optimize combustion efficiency. By dynamically adjusting these parameters based on real-time sensor feedback, the system achieves more complete combustion of waste gases, thereby reducing smoke and noise emissions while maintaining the primary function of waste gas disposal
2Device complexity
If manual control of assist gas flow is used, then system complexity is reduced, but smoke reduction effectiveness is insufficient
Solution Approach 1:
The patent employs feedback control where optical sensors continuously measure smoke density at the flare stack and feed this information back to the control system. The controller automatically adjusts assist gas flow rates based on the measured smoke levels, creating a self-regulating system that maintains optimal combustion conditions without requiring constant manual intervention
Solution Approach 2:
The patent replaces manual mechanical control of assist gas flow with an automated electronic control system. The system uses optical sensors, microprocessors, and electronic control valves to automatically adjust combustion parameters, substituting human-operated mechanical systems with automated electromechanical systems that provide more precise and consistent smoke control
3Reliability
If flare tip design changes are made, then initial performance is improved, but performance degrades over time due to changing gas composition
Solution Approach 1:
The patent transitions from static flare tip design to dynamic control where assist gas flow rates are continuously adjusted based on real-time combustion conditions. The system adapts to changing waste gas compositions by monitoring combustion effectiveness and modifying assist gas injection rates, ensuring consistent performance despite variations in feedstock composition over time
Solution Approach 2:
The patent implements continuous adjustment of combustion parameters including assist gas flow rate, steam injection rate, and oxygen supply based on real-time monitoring of combustion efficiency. This dynamic parameter adjustment allows the system to maintain optimal performance across varying operating conditions and waste gas compositions, overcoming the limitations of fixed design parameters
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
This approach effectively reduces both smoke and noise emissions to predetermined limits, enhancing compliance with environmental regulations and reducing operational costs by automating the control of flaring processes, thereby minimizing the burning of valuable hydrocarbons and noise production.
Implementation Method 1
A smoke detector in the feedback loop receives an input signal from a camera or other sensor that detects a flaring process
Implementation Method 2
Disclosed embodiments include systems and Model Predictive Control (MPC) model-based methods for reducing emissions of both smoke and noise during a gas-assisted flaring process
Implementation Method 3
Flaring is generally performed only as a year-end process for several weeks up to about one month. More generally, flaring as commonly used in the petrochemical and oil industry is a volatile organic compound (VOC) combustion process used to safely dispose of flammable waste gases
Data Source
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AI summary
A method (100) of reducing plant emissions includes providing (101) a MPC model for a flaring process including one-to-one models between controlled variables (CVs) including a smoke count and/or a flare count (CV1) and a noise level (CV2), and flow of assist gas as a manipulated variable (MV) and another process gas flow as a disturbance variable (DV). The MPC model receives sensed flare-related parameters (102) during the flaring process including a measure of CV1 (CV1*) and CV2 (CV2*). Provided CV1* is above a minimum setpoint for CV1 (CV1 setpoint) and CV2* is above a setpoint for CV2 (CV2 setpoint), the flaring process is automatically controlled (103) using the MPC model which determines an updated flow setpoint for MV from CV1* and CV2*, the CV1 and CV2 error, and the identified one-to-one models.