scholarly journals Identification of Microbiological Activities in Wet Flue Gas Desulfurization Systems

2021 ◽  
Vol 12 ◽  
Author(s):  
Gregory Martin ◽  
Shagun Sharma ◽  
William Ryan ◽  
Nanda K. Srinivasan ◽  
John M. Senko

Thermoelectric power generation from coal requires large amounts of water, much of which is used for wet flue gas desulfurization (wFGD) systems that minimize sulfur emissions, and consequently, acid rain. The microbial communities in wFGDs and throughout thermoelectric power plants can influence system performance, waste processing, and the long term stewardship of residual wastes. Any microorganisms that survive in wFGD slurries must tolerate high total dissolved solids concentrations (TDS) and temperatures (50–60°C), but the inocula for wFGDs are typically from fresh surface waters (e.g., lakes or rivers) of low TDS and temperatures, and whose activity might be limited under the physicochemically extreme conditions of the wFGD. To determine the extents of microbiological activities in wFGDs, we examined the microbial activities and communities associated with three wFGDs. O2 consumption rates of three wFGD slurries were optimal at 55°C, and living cells could be detected microscopically, indicating that living and active communities of organisms were present in the wFGD and could metabolize at the high temperature of the wFGD. A 16S rRNA gene-based survey revealed that the wFGD-associated microbial communities included taxa attributable to both thermophilic and mesophilic lineages. Metatranscriptomic analysis of one of the wFGDs indicated an abundance of active Burholderiaceae and several Gammaproteobacteria, and production of transcripts associated with carbohydrate metabolism, osmotic stress response, as well as phage, prophages, and transposable elements. These results illustrate that microbial activities can be sustained in physicochemically extreme wFGDs, and these activities may influence the performance and environmental impacts of thermoelectric power plants.

2018 ◽  
Author(s):  
Achyut Paudel ◽  
Joshua Richey ◽  
Jason Quinn ◽  
Todd M. Bandhauer

2018 ◽  
Vol 53 ◽  
pp. 04005 ◽  
Author(s):  
Ding Yang ◽  
Yi Luo ◽  
XingLian Ye ◽  
WeiXiang Chen ◽  
Jun Guo ◽  
...  

SO3 is one of the main precursors of atmospheric PM2.5, and its emission has attracted more and more attention in the industry. This paper briefly analyzes the harm of SO3 and the method of controlled condensation to test SO3. The effect of cooperative removal of SO3 by ultra-low emission technology in some coal-fired power plants has been tested by using the method of controlled condensation. The results show that the cooperative removal of SO3 by ultra-low emission technology in coal-fired power plants is effective. The removal rate of SO3 by low-low temperature electrostatic precipitators and electrostatic-fabric integrated precipitators can be exceeded 80%, while the removal rate of SO3 by wet flue gas desulfurization equipment displays lower than the above two facilities, and the wet electrostatic precipitator shows a better removal effect on SO3. With the use of ultra-low emission technology in coal-fired power plants, the SO3 emission concentration of the tail chimney reaches less than 1 mg / Nm3.


2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Ghulam Moeen Uddin ◽  
Syed Muhammad Arafat ◽  
Waqar Muhammad Ashraf ◽  
Muhammad Asim ◽  
Muhammad Mahmood Aslam Bhutta ◽  
...  

Abstract The emissions from coal power plants have serious implication on the environment protection, and there is an increasing effort around the globe to control these emissions by the flue gas cleaning technologies. This research was carried out on the limestone forced oxidation (LSFO) flue gas desulfurization (FGD) system installed at the 2*660 MW supercritical coal-fired power plant. Nine input variables of the FGD system: pH, inlet sulfur dioxide (SO2), inlet temperature, inlet nitrogen oxide (NOx), inlet O2, oxidation air, absorber slurry density, inlet humidity, and inlet dust were used for the development of effective neural network process models for a comprehensive emission analysis constituting outlet SO2, outlet Hg, outlet NOx, and outlet dust emissions from the LSFO FGD system. Monte Carlo experiments were conducted on the artificial neural network process models to investigate the relationships between the input control variables and output variables. Accordingly, optimum operating ranges of all input control variables were recommended. Operating the LSFO FGD system under optimum conditions, nearly 35% and 24% reduction in SO2 emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. Similarly, nearly 42% and 28% reduction in Hg emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. The findings are useful for minimizing the emissions from coal power plants and the development of optimum operating strategies for the LSFO FGD system.


Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4249
Author(s):  
Xuan Yao ◽  
Man Zhang ◽  
Hao Kong ◽  
Junfu Lyu ◽  
Hairui Yang

After the implementation of the ultra-low emissions regulation on the coal-fired power plants in China, the problem of the excessive ammonia-slipping from selective catalytic reduction (SCR) seems to be more severe. This paper analyzes the operating statistics of the coal-fired plants including 300 MW/600 MW/1000-MW units. Statistics data show that the phenomenon of the excessive ammonia-slipping is widespread. The average excessive rate is over 110%, while in the small units the value is even higher. A field test data of nine power plants showed that excessive ammonia-slipping at the outlet of SCR decreased following the flue-gas process. After most ammonia reduced by the dust collector and the wet flue-gas desulfurization (FGD), the ammonia emission at the stack was extremely low. At same time, a method based on probability distribution is proposed in this paper to describe the relationship between the NH3/NOX distribution deviation and the De–NOX efficiency/ammonia-slipping. This paper also did some original work to solve the ammonia-slipping problem. A real-time self-feedback ammonia injection technology using neural network algorithm to predict and moderate the ammonia distribution is proposed to decrease the NH3/NOX deviation and excessive ammonia-slipping. The technology is demonstrated in a 600-MW unit and works successfully. The excessive ammonia-slipping problem is well controlled after the implementation of the technology.


Sign in / Sign up

Export Citation Format

Share Document