基于混合建模的变换反应器产品预测与优化

doi:10.16085/j.issn.1000-6613.2020-2564

摘要/Abstract

摘要：

石化行业智能化建设是“中国制造2025”的重要内容，加强信息物理系统（CPS）建设是实现石化行业智能化的重要保障。本文基于某炼化企业CPS建设需要，以变换装置建模为重点，采用混合建模方法进行模型的研发。通过Aspen plus模拟生产流程并进行灵敏度分析，模拟产生系列运行数据；利用混合建模方法对训练组数据进行挖掘，估算了反应器R2204的动力学参数，建立了混合模型，并采用预测组数据进行验证。结果表明，通过该方法所得模型在预测反应器出口最关键组分CO组成时相对偏差较小（25组数据平均偏差为2.78%，最大一组数据偏差不超过10%），证明模型可靠；基于此模型，对反应器进口流股S2220的温度和H₂O/CO摩尔比值进行了模拟优化，结果表明进料温度为201℃、H₂O/CO摩尔比值为51.35时，反应器出口CO的干基物质的量分数最小为0.406%，满足技术生产指标。本文采用混合建模方法建立的模型不仅可以准确地预测反应器的产品CO组成，还可以集成到工厂CPS系统中，为后续的决策优化提供依据。

关键词: 信息物理系统（CPS）, 混合建模, 数据挖掘, 参数估计, 优化

Abstract:

The intelligent construction of the petrochemical industry is an important part of “Made in China 2025”. Strengthening the construction of CPS is an important guarantee for the realization of intelligence. Based on the CPS construction needs of a refining, this paper focuses on the modeling of transformation devices, and uses a hybrid modeling method to develop the model. Through the Aspen plus simulation of the production process and sensitivity analysis, a series of data were generated. A hybrid model was developed by mining the training set data to estimate the kinetic equation parameters of the R2204 reactor, and the model was validated using the prediction set data. Results showed that the relative deviation of the model in predicting the composition of CO at the reactor outlet was relatively small (the average deviation was 2.78%; the maximum deviation of a group does not exceed 10%), which proved that the model was reliable. Finally, based on this model, the temperature of the reactor inlet stream S2220 and the H₂O/CO ratio were optimized. Results showed that when the feed temperature was 201℃ and the H₂O/CO ratio was 51.35, the CO mole fraction at the outlet of the reactor was 0.406%, which met the technical production indicators. The model established by the hybrid modeling method in this paper can not only accurately predict the CO composition of the product composition of the reactor, but also can be integrated into the factory CPS system to provide a basis for subsequent decision-making optimization.

Key words: cyber-physical systems (CPS), hybrid modeling, data mining, parameter estimation, optimization

中图分类号:

TQ015

高石磊, 潘艳秋, 李鹏飞, 张春超, 俞路, 王振兴. 基于混合建模的变换反应器产品预测与优化[J]. 化工进展, 2021, 40(12): 6604-6612.

GAO Shilei, PAN Yanqiu, LI Pengfei, ZHANG Chunchao, YU Lu, WANG Zhenxing. Product prediction and optimization of shift reactor based on hybrid modeling[J]. Chemical Industry and Engineering Progress, 2021, 40(12): 6604-6612.

图/表 14

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对比指标	S2220（进口）				S2221（出口）
对比指标	设计值	模拟值	相对偏差%	设计值	模拟值	相对偏差%
组成（物质的量分数）/%
H₂	36.7590	36.7594	0.0011	37.1850	37.1853	0.0008
CO	0.7150	0.7152	0.0280	0.2890	0.2893	0.1038
CO₂	27.9890	27.9793	0.0347	28.4150	28.4052	0.0345
H₂O	31.9060	31.9154	0.0295	31.4800	31.4895	0.0302
其他	2.6310	2.6307	0.0114	2.6310	2.6307	0.0114
流量/kmol·h^-1	16905.6	16905.6	0.0000	16905.6	16905.6	0.0000
温度/℃	210.0000	208.6808	0.6282	214.2000	214.2000	0.0000
压力/MPa	3.4500	3.4500	0.0000	3.4100	3.4100	0.0000

对比指标	S2220（进口）				S2221（出口）
对比指标	设计值	模拟值	相对偏差%	设计值	模拟值	相对偏差%
组成（物质的量分数）/%
H₂	36.7590	36.7594	0.0011	37.1850	37.1853	0.0008
CO	0.7150	0.7152	0.0280	0.2890	0.2893	0.1038
CO₂	27.9890	27.9793	0.0347	28.4150	28.4052	0.0345
H₂O	31.9060	31.9154	0.0295	31.4800	31.4895	0.0302
其他	2.6310	2.6307	0.0114	2.6310	2.6307	0.0114
流量/kmol·h^-1	16905.6	16905.6	0.0000	16905.6	16905.6	0.0000
温度/℃	210.0000	208.6808	0.6282	214.2000	214.2000	0.0000
压力/MPa	3.4500	3.4500	0.0000	3.4100	3.4100	0.0000

序号	S2220 /kmol·h^-1	T2220 /℃	组成（物质的量分数）/%
序号	S2220 /kmol·h^-1	T2220 /℃	CO_in	H₂O_in	H_2，in	CO_2，in	CO_out
1	13956.80	201.55	0.7675	26.9378	39.4469	30.0248	0.3104
2	14362.24	203.33	0.7548	29.0003	38.3333	29.1772	0.3017
—	—	—	—	—	—	—	—
75	16319.02	207.55	0.7225	31.2196	37.1351	28.2652	0.2922
76	16724.47	208.91	0.7050	32.8871	36.2348	27.5800	0.2852
—	—	—	—	—	—	—	—
100	18557.80	213.55	0.6519	37.9358	33.5089	25.5052	0.2637

序号	S2220 /kmol·h^-1	T2220 /℃	组成（物质的量分数）/%
序号	S2220 /kmol·h^-1	T2220 /℃	CO_in	H₂O_in	H_2，in	CO_2，in	CO_out
1	13956.80	201.55	0.7675	26.9378	39.4469	30.0248	0.3104
2	14362.24	203.33	0.7548	29.0003	38.3333	29.1772	0.3017
—	—	—	—	—	—	—	—
75	16319.02	207.55	0.7225	31.2196	37.1351	28.2652	0.2922
76	16724.47	208.91	0.7050	32.8871	36.2348	27.5800	0.2852
—	—	—	—	—	—	—	—
100	18557.80	213.55	0.6519	37.9358	33.5089	25.5052	0.2637

优化算法	目标函数值	动力学参数
优化算法	目标函数值	k₀	E/kJ·kmol^-1	a	b	c	d
GA	0.05526	18.28179	747.97970	1.99999	0.00002	-0.00001	-0.00001
PSO	0.05457	15.18988	0	2.00000	0	0	0
SA	0.05792	15.00843	26.27020	1.99968	0.00017	-0.00001	-0.00054