基于工业数据的催化裂化装置选择性催化还原脱硝机理模型

石油炼制与化工 ›› 2022, Vol. 53 ›› Issue (8): 91-96.

基于工业数据的催化裂化装置选择性催化还原脱硝机理模型

戴宁锴¹,王杰¹,欧阳福生¹,王建平²,焦云强²,裴旭²

1. 华东理工大学化工学院石油加工研究所
2. 石化盈科信息技术有限责任公司

收稿日期:2022-03-04 修回日期:2022-04-08 出版日期:2022-08-12 发布日期:2022-07-30
通讯作者: 欧阳福生 E-mail:ouyfsh@ecust.edu.cn

MODELING THE DENITRIFICATION MECHANISM OF SELECTIVE CATALYTIC REDUCTION IN CATALYTIC CRACKING UNIT BASED ON INDUSTRIAL DATA

Received:2022-03-04 Revised:2022-04-08 Online:2022-08-12 Published:2022-07-30
Contact: Fu-Sheng OUYANG E-mail:ouyfsh@ecust.edu.cn

摘要/Abstract

摘要： 通过研究选择性催化还原（SCR）技术机理，建立催化裂化（FCC）装置再生烟气SCR系统脱硝机理微分方程组模型。基于大量工业SCR系统数据，利用龙格库塔吉尔（RKG）方法对脱硝机理微分方程组进行求解，并结合遗传算法对模型参数进行寻优。结果表明，模型对FCC装置SCR系统出口氮氧化物（NO_x）浓度预测的平均绝对误差为5.75%，模型预测值与装置实际值拟合的可决系数为0.906。这说明所建SCR脱硝机理模型具有较强的泛化能力和较高模拟精度，可用于优化FCC装置再生烟气SCR系统的操作条件，实现NO_x排放达标。

关键词: 选择性催化还原, 烟气脱硝, 氮氧化物, 机理模型, 龙格库塔吉尔法, 遗传优化算法

Abstract: Based on the mechanism of selective catalytic reduction (SCR) technology, a set of denitrification mechanism modeling equations of regenerator flue gas SCR system in fluidcatalytic cracking unitwere established. According to a large amount data of SCR system, the mechanism model equations were solved by Runge-Kutta-Gill method, and the model parameters were optimized by combining genetic algorithm. The verification results show that the mean absolute percentage error of the model is 5.75% and the coefficient of determination is 0.906, which indicate that the established SCR denitrification mechanism model has strong generalization ability and high simulation accuracy. The model will be expected to play an important role in optimizing the operating conditions of the SCR systems for achievement standard ofnitrogen oxides emission of the treated flue gas.

Key words: selective catalytic reduction, flue gas denitrification, nitrogen oxides, mechanism model, Runge-Kutta-Gill method, genetic algorithm method

戴宁锴王杰欧阳福生王建平焦云强裴旭. 基于工业数据的催化裂化装置选择性催化还原脱硝机理模型[J]. 石油炼制与化工, 2022, 53(8): 91-96.

参考文献

[1] 石君君.流化催化裂化（FCC）再生过程NOx生成机理研究[D]. 华南理工大学，2016
[2] 毕文峰.浅析催化再生烟气氮氧化物排放控制措施[J].资源节约与环保, 2019, (11):5-5
[3]赵萍萍, 李纳.锅炉大气污染物排放标准研究[J].广州化工, 2021, 49(19):104-106
[4] 王远, 金光旭, 孙继达.影响SCR脱硝效率因素的分析与探讨, 2017(15): 70-71[J].化工管理, 2017, (15):70-71
[5]LIETTI L, NOVA I, CAMURRI S, et al.Dynamics of the SCR-DeNOx reaction by the transient-reponse method[J].Aiche Journal, 1997, 43(10):2559-2570
[6]CIARDELLI C, NOVA I, TRONCONI E, et al.SCR-DeNOx for diesel engine exhaust aftertreatment:unsteady-state kinetic study and monolith reactor modelling[J].Chemical Engineering Science, 2004, 59(2223):5301-5309
[7] Irfan M F, Mjalli F S, Sang D K.Modeling of NH3-NO-SCR reaction over CuO/γ-Al2O3 catalyst in a bubbling fluidized bed reactor using artificial intelligence techniques[J].Fuel, 2012, 93:245-251
[8]Si F, Romero C E, Yao Z, et al.Optimization of coal-fired boiler SCRs based on modified support vector machine modeals and genetic algorithms[J].Fuel, 2009, 88(5):806-816
[9]秦天牧, 刘吉臻, 杨婷婷, 张维.火电厂烟气脱硝系统建模与运行优化仿真[J].中国电机工程学报, 2016, 36(10):2699-2703
[10]姚楚, 龙东腾, 吕游, 刘吉臻.火电厂脱硝系统机理建模与控制研究[J].热能动力工程, 2018, 33(05):78-84
[11] 秦天牧.燃煤电站SCR烟气脱硝系统建模与喷氨量优化控制[D]. 华北电力大学(北京), 2017
[12]饶德备, 谭鹏, 李壮扬, 胡昌镁, 曹楠, 张成, 方庆艳, 陈刚.燃煤电站脱硝系统机理建模[J].热力发电, 2019, 48(08):36-41
[13] 马康丰.火电机组SCR脱硝系统全工况建模与优化控制研究[D]. 华北电力大学(北京), 2020
[14] 黄华江.实用化工计算机模拟[M]. 北京化学工业出版社教材出版中心, 2004
[15] 周国标，谢建利.数值计算[M]. 北京高等教育出版社, 2013
[16] Holland J H.Adaptation in natural and artificial systems[M]. MIT Press, 1975
[17]S.C. OliveiraEvaluation of effectiveness factor of immobilized enzymes using Runge-Kutta-Gill method: how to solve mathematical undetermination at particle center point?[J].Bioprocess Engineering, 1999, 20(2):185-187
[18] 朱博.浅谈遗传算法的研究与发展[J].电脑迷, 2018, (2):85-86
[19] 董妍汝.基于选择算子的遗传算法改进[J].办公自动化, 2015, 16(8):59-61
[20] Schrijvers T.Analyses, optimizations and extensions of Constraint Handling Rules: Ph.D. summary[C]. International Conference on Logic Programming. Springer-Verlag, 2005
[21] 黄卫华，许小勇，范建坤.实数编码遗传算法中常用变异算子的Matlab实现及应用[J].广西轻工业, 2007, 23(1):77-78