Mechanism modeling and real-time estimation application of thermal efficiency for delayed coking furnace

doi:10.11949/0438-1157.20191072

Abstract

Abstract:

Coking heating furnace is the key plant in the delayed coking unit. Thermal efficiency is one of the most important key performance indications. In this work, the thermal efficiency dynamic model is established based on the first principles of heat transfer equations and heat balance conditions. A joint particle filtering method combined with Kalman filter and particle filter is proposed to correct the model prediction and the convergence of the proposed method has been analyzed. The application results confirmed the feasibility and effectiveness of the proposed method, and laid the foundation for the advanced control of the delayed coking unit.

Key words: prediction, control, tubular heating furnace, first principle modeling, parameter estimation, thermal efficiency, joint particle filtering, united estimation

摘要：

在延迟焦化装置中，焦化炉热效率是操作先进性关键评价指标，然而实际工业生产中难以在线测得加热炉热效率。本文基于机理建模，推导了管式加热炉状态空间预测模型。为降低干扰影响，提出了一种基于粒子滤波的联合估计方法来修正加热炉热效率模型预测，并分析了其收敛性。实际应用结果证实了所提方法的可行性和有效性，为实现延迟焦化装置先进控制奠定了基础。

关键词: 预测, 控制, 管式加热炉, 机理建模, 参数估计, 加热炉热效率, 粒子滤波, 联合估计

CLC Number:

TP 273

Jian HUANG, Zhong ZHAO. Mechanism modeling and real-time estimation application of thermal efficiency for delayed coking furnace[J]. CIESC Journal, 2020, 71(7): 3140-3150.

黄健, 赵众. 延迟焦化加热炉热效率的机理建模与实时估计应用[J]. 化工学报, 2020, 71(7): 3140-3150.

Figures/Tables 10

References 33

1	张伟勇 . 延迟焦化装置先进控制方法研究与应用[D]. 北京: 北京化工大学, 2008.
	Zhang W Y . Research and application of advanced control methods for delayed coking unit[D]. Beijing: Beijing University of Chemical Technology, 2008.
2	龚朝兵, 周雨泽, 宋金宝, 等 . 延迟焦化装置的能耗分析及节能优化实践[J]. 中外能源, 2013, 18( 1): 84- 88.
	Gong C B , Zhou Y Z , Song J B , et al . Energy consumption analysis and energy saving optimization practice of delayed coking unit[J]. Sino-Global Energy, 2013, 18( 1): 84- 88.
3	王培超 . 延迟焦化装置有效能分析[J]. 中外能源, 2013, 18( 3): 85- 88.
	Wang P C . Effective energy analysis of delayed coking unit[J]. Sino-Global Energy, 2013, 18( 3): 85- 88.
4	Wang C S , Zhou Y , Liang Z J , et al . Heat transfer simulation and thermal efficiency analysis of new vertical heating furnace[J]. Case Studies in Thermal Engineering, 2019, 13( 4): 1- 7.
5	Qi J , Liu Z W , Wang X Y , et al . Compensation and simulation of restrictive memory least square method for BOD soft measurement mechanism model [R]. Beijing: Natural Computation, 2009.
6	梁光川, 黄兴, 王勇, 等 . 燃气加热炉热效率计算方法的改进及应用[J]. 油气储运, 2016, ( 5): 560- 563.
	Liang G C , Huang X , Wang Y , et al . Improvement and application of calculation method for thermal efficiency of gas heating furnace[J]. Oil & Gas Storage and Transportation, 2016, ( 5): 560- 563.
7	Hammersley J M , Morton K W . Poor man s Monte Carlo[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1954, 16( 1): 23- 38.
8	Gordon N , Salmond D , Smith A . Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J]. IEE Proceedings F, 1993, 140( 2): 107- 113.
9	袁泽剑, 郑南宁, 贾新春 . 高斯-厄米特粒子滤波器[J]. 电子学报, 2003, 31( 7): 970- 973.
	Yuan Z J , Zheng N N , Jia X C . Gauss-Hermit particle filter[J]. Acta Electronica Sinica, 2003, 31( 7): 970- 973.
10	莫以为, 萧德云 . 基于粒子滤波算法的混合系统监测与诊断[J]. 自动化学报, 2003, 29( 5): 641- 648.
	Mo Y W , Xiao D Y . Hybrid system monitoring and diagnosing based on particle filter algorithm[J]. Acta Automatica Sinica, 2003, 29( 5): 641- 648.
11	颉刚, 侯岩松, 王赓 . 延迟焦化装置先进控制系统的开发与应用[J]. 电脑知识与技术, 2011, 7( 3): 678- 680.
	Xie G , Hou Y S , Wang G . Development and application of advanced control system for delayed coking unit[J]. Computer Knowledge and Technology, 2011, 7( 3): 678- 680.
12	朱柏青 . 基于热效率的加热炉综合优化控制的应用研究[D]. 北京: 北京化工大学, 2013.
	Zhu B Q . Application research on comprehensive optimization control of heating furnace based on thermal efficiency[D]. Beijing: Beijing University of Chemical Technology, 2013.
13	裴召华 . 提高管式加热炉热效率的措施[J]. 油气储运, 2009, 28( 11): 52- 54.
	Pei Z H . Measures to improve the thermal efficiency of tubular heating furnaces[J]. Oil & Gas Storage and Transportation, 2009, 28( 11): 52- 54.
14	杨光炯 . 管式炉辐射室传热计算——罗伯-依万斯与别洛康方法的比较[J]. 石油炼制, 1984, ( 12): 6- 12.
	Yang G J . Calculation of heat transfer in radiation chamber of tube furnace—Comparison of Rob-Evans and Belokan methods[J]. Petroleum Refining, 1984, ( 12): 6- 12.
15	曾祥龙 . 工业管式加热炉优化控制方法研究[D]. 北京: 北京化工大学, 2016.
	Zeng X L . Research on optimal control method of industrial tubular furnace[D]. Beijing: Beijing University of Chemical Technology, 2016.
16	中国石油化工总公司 . 石油化工工艺管式炉效率测定法: SHF 0001—90 [S]. 北京: 中国石油化工总公司生产部, 1990.
	Group Sinopec . Method for measuring efficiency of tubular furnace in petrochemical process:SHF 0001—90 [S]. Beijing: Production Department of Sinopec Group, 1990.
17	蒋国芳 . 过剩空气系数对加热炉的影响[J]. 石油化工设备技术, 2002, 23( 5): 29- 31.
	Jiang G F . Influence of excess air coefficient on heating furnace[J]. Petrochemical Equipment Technology, 2002, 23( 5): 29- 31.
18	叶树珍, 张占渔 . 排烟热损失计算方法探讨[J]. 能源, 1983, 3( 1): 24- 26.
	Ye S Z , Zhang Z Y . Discussion on calculation method of heat loss of exhaust smoke[J]. Energy, 1983, 3( 1): 24- 26.
19	庄富山 . 加热炉热效率数学模型[J]. 计算技术与自动化, 1983, 3( 3): 21- 39.
	Zhuang F S . Mathematical model of heating furnace thermal efficiency[J]. Computing Technology and Automation, 1983, 3( 3): 21- 39.
20	王刚, 夏友山 . 管式加热炉排烟温度升高的原因与防治[J]. 设备管理与维护, 2018, 4( 7): 42- 43.
	Wang G , Xia Y S . Causes and prevention of temperature rise of flue gas in tube heating furnace[J]. Plant Maintenance Engineering, 2018, 4( 7): 42- 43.
21	卢时述, 周大军 . 炼油加热炉排烟热损数学模型的确定[J]. 工业炉, 2004, 26( 4): 33- 36.
	Lu S X , Zhou D J . Determination of mathematical model for heat loss of flue gas in refining heating furnace[J]. Industrial Furnace, 2004, 26( 4): 33- 36.
22	杨丹 . 卡尔曼滤波器设计及其应用研究[D]. 长沙: 湘潭大学, 2014.
	Yang D . Kalman filter design and its application[D]. Changsha: Xiangtan University, 2014.
23	Kalman R E . A new approach to linear filtering and prediction problems[J]. Transactions of the ASME-Journal of Basic Engineering, 1960, 82(Series D): 35- 45.
24	Kalman R E , Bucy R S . New results in linear filtering and prediction theory[J]. Transactions of the ASME-Journal of Basic Engineering, 1961, 83(Series D): 95- 107.
25	Gordon N , Clapp T , Maskell S , et al . A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Transactions on Signal Processing, 2002, 50( 2): 174- 188.
26	Oppenheim G , Philippe A , Rigal J D . The particle filters and their applications [J]. Chemometrics and Intelligent Laboratory Systems, 2008, 91( 2): 87- 93.
27	Sun J , Blom H A P , Ellerbroek J , et al . Particle filter for aircraft mass estimation and uncer-tainty modeling[J]. Transportation Research Part C-Emerging Technologies, 2019, 105( 3): 145- 162.
28	Leeuwen V , Jan P . Particle filtering in geophysical systems [J]. Monthly Weather Review, 2009, 137( 12): 4089- 4114.
29	Xin W G , Guo Y X , Yu F W , et al . Particle filter-based prediction for anomaly detection in automatic surveillance[J]. IEEE Access, 2019, 7( 5): 107550-107559.
30	Kwok C , Fox D , Meila M . Real-time particle filters [J]. Proceedings of the IEEE, 2004, 92( 3): 469- 484.
31	Ya B X , Ke X , Jian W W , et al . Research on particle filter tracking method based on Kalman filter[C]// 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference. Xi an, 2018.
32	Moral P D , Guionnet A . On the stability of measure valued processes with applications to filtering[J]. Comptes Rendus de l Académie des Sciences - Series I – Mathematics, 1999, 329( 5): 429- 434.
33	Papavasiliou A . A uniformly convergent adaptive particle filter [J]. Journal of Applied Probability, 2004, 42( 4): 1053- 1068.

化验分析数据	模型预测值	相对误差	误差百分比
94.1283	93.97047	0.157898	0.17
94.0898	93.96148	0.128333	0.14
94.0552	94.06848	-0.01326	0.02
94.0855	93.92905	0.156486	0.17
94.1410	93.89776	0.243265	0.26
94.1543	93.88981	0.264489	0.28
94.0818	93.92463	0.157184	0.17
94.0240	94.04803	-0.02398	0.03
93.9775	94.01946	-0.04191	0.05
94.1575	94.08129	0.076286	0.08
94.1625	94.08595	0.076617	0.08
94.1695	94.07200	0.097571	0.10
94.1224	94.03293	0.089539	0.10
94.1636	94.06520	0.098439	0.11
94.1688	94.05069	0.118199	0.13
94.0913	94.10989	-0.0185	0.02
93.9819	94.07428	-0.09235	0.10
93.9182	94.06282	-0.14457	0.15
93.9344	94.06899	-0.13455	0.14
94.1415	94.03183	0.109697	0.12

化验分析数据	模型预测值	相对误差	误差百分比
94.1283	93.97047	0.157898	0.17
94.0898	93.96148	0.128333	0.14
94.0552	94.06848	-0.01326	0.02
94.0855	93.92905	0.156486	0.17
94.1410	93.89776	0.243265	0.26
94.1543	93.88981	0.264489	0.28
94.0818	93.92463	0.157184	0.17
94.0240	94.04803	-0.02398	0.03
93.9775	94.01946	-0.04191	0.05
94.1575	94.08129	0.076286	0.08
94.1625	94.08595	0.076617	0.08
94.1695	94.07200	0.097571	0.10
94.1224	94.03293	0.089539	0.10
94.1636	94.06520	0.098439	0.11
94.1688	94.05069	0.118199	0.13
94.0913	94.10989	-0.0185	0.02
93.9819	94.07428	-0.09235	0.10
93.9182	94.06282	-0.14457	0.15
93.9344	94.06899	-0.13455	0.14
94.1415	94.03183	0.109697	0.12

滤波	热效率标准误差
KF	0.0603
PF	0.0778
JPF	0.0232

滤波	热效率标准误差
KF	0.0603
PF	0.0778
JPF	0.0232

粒子数	RMSE	运行时间
50	0.0786/0.0231	2.3650/2.5360
100	0.0256/0.0157	2.4410/2.8600
150	0.0227/0.0123	2.4830/3.0600
200	0.0184/0.0099	2.8520/3.1840
300	0.0112/0.0073	3.3480/3.5290
500	0.0096/0.0045	3.9350/4.0370