Mathematical and Artificial Neural Network Simulation of NOx Selective Catalytic Reduction in a Monolithic Reactor

Document Type : Research Article

Authors

Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran

Abstract

Worldwide development of industries and increase of energy consumption, have resulted fast increase in the emission of nitrogen oxides pollutants. Therefore, removal of nitrogen oxides is a very important issue. In this study, modeling and simulation of selective catalytic reduction of nitrogen oxides in a monolithic catalytic reactor at both steady-state and dynamic-state was performed. Steady- state results showed that because of intense effect of temperature on nitrogen oxides conversion and competition of the main reaction with ammonia oxidation reaction, conversion of nitrogen oxides requires a catalytic filter in the range 300-350°C. Results showed that nitrogen oxide conversion increases with decreasing gas hourly space velocity and increasing inlet nitrogen oxide concentration. At dynamic-state, the effect of changes in some parameters including gas hourly space velocity, inlet nitrogen oxide concentration, and ammonia /nitrogen oxide ratio were investigated. Also, steady-state simulation of the process was performed using an artificial neural network and conversions of nitrogen oxides and ammonia were estimated as a function of gas hourly space velocity, reactor temperature, and nitrogen oxide concentration. 96 networks with different neurons and two different activation functions in hidden layer were trained. The resulted optimum network showed maximum mean square error of about 0.01 compared to mathematical modeling results indicating high performance of neural network for prediction of process performance.

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References

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Amirkabir Journal of Mechanical Engineering
Volume 52, Issue 9
December 2020
Pages 2465-2478

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