Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System

Document Type : Chemistry Article

Authors

1 Master's student in Chemical Engineering, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran

2 Assistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran

Abstract

Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction.  In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24%

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References

]1] R.J. Blaszczak. "Nitrogen Oxides(NOx), Why And How They Are Controlled:. EPA-456/F-99-006R " 1999.
[2] J. Son, H. Yang, G. Kim, S. Hwang, and H. You. "Technology development for the reduction of NOx in flue gas from a burner-type vaporizer and its application." Korean J. Chemical Engineering 34, no. 6 (2017): 1619-1629.
[3] Y.S. Mok and H.J. Lee. "Removal of sulfur dioxide and nitrogen oxides by using ozone injection and absorption-reduction technique." Fuel Process Technol 87, no. 7 (2006): 591-597.
[4] X. Zhang, R. Wang, H. Zhu, and Y. Chen. "Performance of NOx capture with Dawson polyoxometalate H6P2W18O62· 28H2O." Chemical Engineering Journal 400 (2020): 125880.
[5] Y. Dongpeng, Q. Chang, L. Liu, W. Zhang, S. Chen, and Z.L. Zhijun Li. "Reduce NOx Emissions by Adsorber-Reduction Catalyst on Lean Burn Engine." TELKOMNIKA (Telecommunication Comput Electron Control 11, no. 3 (2013):481-488.
[6] S.H. Jo, K.M. Kim, S. Hee Seo, T.H. Kim, S. Yu, T.H. Kim, and Y.S. Son. "A study on additives to improve electron beam technology for NOx and SO2 reduction." Radiation Physics and Chemistry 183 (2021): 109397.
[7] A. Shimizu, O. Hirokazu ,K. Yosuke, M. Kazuyuki, and Y. Day. "Development of a Nitrogen-enrichment/Humidification Membrane System for NOx Emission Reduction in a Marine Diesel Engine". Mar Eng 48, no. 3 (2013).
[8] S. Bhattacharyya, and R.K. Das. "Catalytic control of automotive NOx: a review." International Journal of Energy Research 23, no. 4 (1999): 351-369.
[9] K. Masera, and A.K. Hossain. "Modified selective non-catalytic reduction system to reduce NOx gas emission in biodiesel powered engines." Fuel 298 (2021): 120826.
[10] I.S.I. Anufriev, E.P. Kopyev, I.S. Sadkin, and M.A. Mukhina. "NOx reduction by steam injection method during liquid fuel and waste burning." Process Safety and Environmental Protection 152 (2021): 240-248.
[11] E. Ikonen, K. Najim, and U. Kortela. "Neuro-fuzzy modelling of power plant flue-gas emissions." Engineering Applications of Artificial Intelligence 13, no. 6 (2000): 705-717.
[12] J. Mohammadhassani, S. Khalilarya, M. Solimanpur, and A. Dadvand. "Prediction of NOx emissions from a direct injection diesel engine using artificial neural network." Modelling and Simulation in Engineering 2012, no. 1 (2012): 830365.
[13] M. Fischer. "Transient NOx estimation using artificial neural networks." IFAC Proceedings Volumes 46, no. 21 (2013): 101-106.
[14] H. Taghavifar, H. Taghavifar, A. Mardani, and A. Mohebbi. "Modeling the impact of in-cylinder combustion parameters of DI engines on soot and NOx emissions at rated EGR levels using ANN approach." Energy Conversion and Management 87 (2014): 1-9.
[15] T. Anan, H. Higuchi, and N. Hamada. "New artificial intelligence technology improving fuel efficiency and reducing CO2 emissions of ships through use of operational big data." Fujitsu Sci. Tech. J 53, no. 6 (2017): 23-28.
[16] Y. Shin, Z. Kim, J. Yu, G. Kim, and S. Hwang. "Development of NOx reduction system utilizing artificial neural network (ANN) and genetic algorithm (GA)." Journal of Cleaner Production 232 (2019): 1418-1429.
[17] V.Y. Valera, M.C. Codolo, and T.D. Martins. "Artificial neural network for prediction of SO2 removal and volumetric mass transfer coefficient in spray tower." Chemical Engineering Research and Design 170 (2021): 1-12.
[18] D. Farhud, and H. Pourkalhor. "Artificial intelligence & genetics." Laboratory and Diagnosis 12, no. 50 (2021): 70-73. (inPersian).
[19] O. Obodeh, and C.I. Ajuwa. "Evaluation of artificial neural network performance in predicting diesel engine NOx emissions." European Journal of Scientific Research 33, no. 4 (2009): 642-653.
[20] I. Arsie, D. Marra, C. Pianese, and M. Sorrentino. "Real-time estimation of engine NOx emissions via recurrent neural networks." IFAC Proceedings Volumes 43, no. 7 (2010): 228-233.
[21] J.O. Lira, H.G. Riella, N. Padoin, and C. Soares. "Computational fluid dynamics (CFD), artificial neural network (ANN) and genetic algorithm (GA) as a hybrid method for the analysis and optimization of micro-photocatalytic reactors: NOx abatement as a case study." Chemical engineering journal 431 (2022): 133771.
[22] Z. Ghaemi, M. Talei, M. Farnaghi and Q. Javadi. "Prediction of air pollution in Tehran using the combination of fuzzy-adaptive neural network and principal component analysis." Iran's remote sensing and GIS 9, no. 3 (1396):21-39 (inPersian).
[23] C. Qi, W. Wang, B. Wang, Y. Kuang, and J. Xu. "Performance analysis of submerged combustion vaporizer." Journal of Natural Gas Science and Engineering 31 (2016): 313-319.
[24] A. Golshani, and M. Quaidi. "Fuzzy method in research, a bridge between quantitative and qualitative research methods." Psychological Methods and Models, 4, 14 (1392):45-65 (inPersian).
[25] L.A. Zadeh. "Outline of a New Approach to the Analysis of Complex Systems and Decision Processes". IEEE Trans Syst Man Cybern, vol. SMC-3, no. 1 (1973): 28–44.
[26] P.C. Nayak, K.P. Sudheer, D.M. Rangan, and K.S. Ramasastri. "A neuro-fuzzy computing technique for modeling hydrological time series."           Journal of Hydrology, vol. 291 (2004): 52–66.
[27] J.S. Roger Jang. "ANFIS : Adap tive-Network-Based Fuzzy Inference System." IEEE Transactions On Systems, Man, And Cybernetics 23, no. 3 (1993): 665­-685.
[28] W. Ahmad. "Thyroid Diseases Forecasting Using a Hybrid Decision Support System Based on ANFIS, k-NN and Information Gain Method." J. Appl Environ Biol Sci 7, no. 10 (2017): 78–85.
[29] M. Shams Javi. Implementation and solution of practical problems with genetic algorithm. Aria project,1390 (inPersian).
 
Journal of Modeling in Engineering
Volume 22, Issue 76
May 2024
Pages 197-211

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History

  • Receive Date: 10 May 2023
  • Revise Date: 15 July 2023
  • Accept Date: 01 October 2023

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