Partitioning of Mercury, Arsenic, Selenium, Boron, and Chloride in a Full-Scale Coal Combustion Process Equipped with Selective Catalytic Reduction, Electrostatic Precipitation, and Flue Gas Desulfurization Systems†

2009 ◽  
Vol 23 (10) ◽  
pp. 4805-4816 ◽  
Author(s):  
Chin-Min Cheng ◽  
Pauline Hack ◽  
Paul Chu ◽  
Yung-Nan Chang ◽  
Ting-Yu Lin ◽  
...  
Keyword(s):  
Selective Catalytic Reduction ◽  
Flue Gas ◽  
Coal Combustion ◽  
Catalytic Reduction ◽  
Combustion Process ◽  
Full Scale ◽  
Flue Gas Desulfurization ◽  
Electrostatic Precipitation

Full Scale Evaluation and Suppression of Mercury Re-Emission in Wet Flue Gas Desulfurization System

2013 ◽  
Vol 316-317 ◽  
pp. 354-357 ◽  
Author(s):  
Cheng Li Wu ◽  
Yan Cao ◽  
Han Xu Li ◽  
Wei Ping Pan
Keyword(s):  
Flue Gas ◽  
Environmental Protection Agency ◽  
Catalytic Reduction ◽  
Field Testing ◽  
The United States ◽  
Full Scale ◽  
Flue Gas Desulfurization ◽  
Emission Rates ◽  
Forced Oxidation ◽  
Ontario Hydro

The full-scale of PC/Cyclone Boilers with common wet flue gas desulfurization (WFGD) with limestone forced oxidation (LSFO) was studied. Ontario Hydro Method (OHM) recommended by the United States Environmental Protection Agency (USEPA) was used to determine mercury emission and speciation at these two full-scale WFGD systems, and OHM quality assurance/quality control (QA/QC) was followed during the field testing. WFGD re-emission problems were repeatedly observed at this unit. Selective catalytic reduction (SCR) had significant effects on mercury removal and Hg0 re-emission rates across WFGD. Effects of injection of continuous chemicals additive containing HS-, S2- or I- on mercury re-emission control were also conducted at this unit.

Influence of flue-gas desulfurization systems on coal combustion by-product quality at kentucky power stations burning high-sulfur coal

1998 ◽  
Vol 17 (8) ◽  
pp. 523-533 ◽  
Author(s):  
James C. Hower ◽  
Uschi M. Graham ◽  
Amy S. Wong ◽  
J.David Robertson ◽  
Bethel O. Haeberlin ◽  
...  
Keyword(s):  
Product Quality ◽  
Flue Gas ◽  
Coal Combustion ◽  
Flue Gas Desulfurization ◽  
Power Stations

scholarly journals Selective Catalytic Reduction System Ammonia Injection Control Based on Deep Deterministic Policy Reinforcement Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Peiran Xie ◽  
Guangming Zhang ◽  
Yuguang Niu ◽  
Tianshu Sun
Keyword(s):  
Reinforcement Learning ◽  
Selective Catalytic Reduction ◽  
Virtual Environment ◽  
Flue Gas ◽  
Catalytic Reduction ◽  
Thermal Power ◽  
Gas Treatment ◽  
Intelligent Controller ◽  
Secondary Pollution ◽  
Ammonia Injection

The control of flue gas emission in thermal power plants has been a topic of concern. Selective catalytic reduction technology has been widely used as an effective flue gas treatment technology. However, precisely controlling the amount of ammonia injected remains a challenge. Too much ammonia not only causes secondary pollution but also corrodes the reactor equipment, while too little ammonia does not effectively reduce the NOx content. In recent years, deep reinforcement learning has achieved better results than traditional methods in decision making and control, which provides new methods for better control of selective catalytic reduction systems. The purpose of this research is to design an intelligent controller using reinforcement learning technology, which can accurately control ammonia injection, and achieve higher denitrification effect and less secondary pollution. To train the deep reinforcement learning controller, a high-precision virtual denitration environment is first constructed. In order to make the virtual environment more realistic, this virtual environment was designed as a special structure with two decoders and a unique approach was used in fitting the virtual environment. A deep deterministic policy agent is used as an intelligent controller to control the amount of injected ammonia. To make the intelligent controller more stable, the actor-critic framework and the experience pool approach were adopted. The results show that the intelligent controller can control the emissions of nitrogen oxides and ammonia at the outlet of the reactor after training in virtual environment.

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