Decreasing the NOx Emissions at Coke Plants

2020 ◽  
Vol 63 (5) ◽  
pp. 241-246
Author(s):  
V. I. Rudyka ◽  
S. A. Kravchenko ◽  
S. J. Abdullin ◽  
S. J. Stelmachenko ◽  
O. I. Makei
Keyword(s):  
2020 ◽  
Vol 63 (7) ◽  
pp. 351-355
Author(s):  
V. I. Zasel’skiy ◽  
D. V. Popolov ◽  
G. L. Zaytsev ◽  
D. V. Sagalay
Keyword(s):  

1999 ◽  
Vol 8 (ASAT CONFERENCE) ◽  
pp. 1-11
Author(s):  
H. Mahmoud ◽  
Sh. Hammed ◽  
M. Nosier ◽  
A. Wandan ◽  
S. Abd EI-Ghany

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1036 ◽  
Author(s):  
Xinying Xu ◽  
Qi Chen ◽  
Mifeng Ren ◽  
Lan Cheng ◽  
Jun Xie

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.


2021 ◽  
Vol 9 (2) ◽  
pp. 123
Author(s):  
Sergejus Lebedevas ◽  
Lukas Norkevičius ◽  
Peilin Zhou

Decarbonization of ship power plants and reduction of harmful emissions has become a priority in the technological development of maritime transport, including ships operating in seaports. Engines fueled by diesel without using secondary emission reduction technologies cannot meet MARPOL 73/78 Tier III regulations. The MEPC.203 (62) EEDI directive of the IMO also stipulates a standard for CO2 emissions. This study presents the results of research on ecological parameters when a CAT 3516C diesel engine is replaced by a dual-fuel (diesel-liquefied natural gas) powered Wartsila 9L20DF engine on an existing seaport tugboat. CO2, SO2 and NOx emission reductions were estimated using data from the actual engine load cycle, the fuel consumption of the KLASCO-3 tugboat, and engine-prototype experimental data. Emission analysis was performed to verify the efficiency of the dual-fuel engine in reducing CO2, SO2 and NOx emissions of seaport tugboats. The study found that replacing a diesel engine with a dual-fuel-powered engine led to a reduction in annual emissions of 10% for CO2, 91% for SO2, and 65% for NOx. Based on today’s fuel price market data an economic impact assessment was conducted based on the estimated annual fuel consumption of the existing KLASCO-3 seaport tugboat when a diesel-powered engine is replaced by a dual-fuel (diesel-natural gas)-powered engine. The study showed that a 33% fuel costs savings can be achieved each year. Based on the approved methodology, an ecological impact assessment was conducted for the entire fleet of tugboats operating in the Baltic Sea ports if the fuel type was changed from diesel to natural gas. The results of the assessment showed that replacing diesel fuel with natural gas achieved 78% environmental impact in terms of NOx emissions according to MARPOL 73/78 Tier III regulations. The research concludes that new-generation engines on the market powered by environmentally friendly fuels such as LNG can modernise a large number of existing seaport tugboats, significantly reducing their emissions in ECA regions such as the Baltic Sea.


2021 ◽  
Vol 279 ◽  
pp. 116931
Author(s):  
Xia Li ◽  
Naifang Bei ◽  
Bo Hu ◽  
Jiarui Wu ◽  
Yuepeng Pan ◽  
...  

2021 ◽  
Vol 230 ◽  
pp. 111434
Author(s):  
Edwin Goh ◽  
James Li ◽  
Nam Y. Kim ◽  
Tim Lieuwen ◽  
Jerry Seitzman

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 900
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Arjo Segers ◽  
Astrid Manders ◽  
Dimitris Balis ◽  
...  

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively).


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1107
Author(s):  
Stefano d’Ambrosio ◽  
Roberto Finesso ◽  
Gilles Hardy ◽  
Andrea Manelli ◽  
Alessandro Mancarella ◽  
...  

In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NOx model has been implemented to improve robustness in terms of NOx prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NOx emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NOx emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.


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