Performance Forecasting for SCR Honeycomb Catalyst Based on BP Neural Network

doi:10.11930/j.issn.1004-9649.2016.10.127.05

Abstract

Abstract: Currently, the selective catalytic reduction (SCR) is one of the most widely used technologies for flue gas denitification. However, its De-NOx efficiency relies heavily on the catalyst. In this paper, take the example of a commercial SCR honeycomb monoliths, five sets of parameters, i.e., GHSV, temperature, oxygen content, NH3/NO ratio and initial NO concentration, are chosen in the De-NOx performance testing on a homegrown testing bed to study its effect on the De-NOx efficiency. Based on the testing data, the prediction model of De-NOx efficiency of SCR honeycomb monoliths is established by applying BP neural network. The results show that the convergence results are satisfactory when the topology structure is formulated as 5×7×1; When it is used in the performance forecasting, the absolute error is about 8% by average while the average relative error is 11% respectively, which proves the good fitting effect of BP neural network.

Key words: coal-fired power plant, flue gas denitrification, honeycomb catalysts, SCR, BP neural network, prediction model

CLC Number:

TM621
X511

YANG Biyuan, ZHAO Jinxiao, WEI Hongge, WANG Yanpeng, ZHU Yue. Performance Forecasting for SCR Honeycomb Catalyst Based on BP Neural Network[J]. Electric Power, 2016, 49(10): 127-131.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.10.127.05

https://www.electricpower.com.cn/EN/Y2016/V49/I10/127

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