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To optimize gas flaring in Kirkuk refinery in various seasons via artificial intelligence techniques
A Zoeir1, J Qajar1, Y Kazemzadeh2
1Department of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran.
Scientific Reports
|August 17, 2023
Summary
Optimizing flare stack layout at the Kirkuk refinery reduces harmful pollutant emissions. The study used simulations and neural networks to find the best arrangement for air quality near cities and farms.
Area of Science:
- Environmental Engineering
- Chemical Engineering
- Computational Fluid Dynamics
Background:
- Flaring in oil refineries releases significant pollutants, posing risks to nearby urban and agricultural areas.
- The Kirkuk refinery's proximity to residential and farm lands necessitates urgent flare stack layout optimization.
Purpose of the Study:
- To develop and apply a computational method for optimizing refinery flare stack layout.
- To minimize environmental impact by managing toxic gas emissions and propagation.
Main Methods:
- Developed MATLAB codes for simulating oxidation and pollutant generation reactions.
- Utilized FLUENT software for pollutant dispersion modeling considering seasonal air currents.
- Employed a neural network approach for fast layout optimization based on simulation data.
Main Results:
- Optimization effectiveness is dependent on air current velocity and direction.
- Optimal layouts at intermediate airflows involve utilizing a subset of existing flare stacks.
- Heuristic techniques like particle swarm and artificial immune systems are effective for intermediate air currents (summer/autumn), while Monte Carlo suits low-flow winter conditions.
Conclusions:
- A data-driven approach combining simulation and neural networks can effectively optimize refinery flare stack layouts.
- Tailoring optimization strategies to seasonal air current patterns is crucial for maximizing emission reduction and environmental protection.
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