进化响应面驱动的CO2加氢制甲醇工艺与氢网络同步优化

doi:10.16085/j.issn.1000-6613.2024-1926

摘要/Abstract

摘要：

复杂工艺流程与氢网络的协同优化是炼厂氢网络集成中的难题。为此，本文提出了一种基于进化响应面的协同优化方法，同步优化甲醇工艺流程与氢网络。该方法构建了CO₂加氢制甲醇流程中反应器、闪蒸罐和精馏塔的机理模型，基于此得到的样本集建立相应的进化响应面模型，利用机理模型验证响应面模型的优化结果并进行修正，提出高效的优化流程框架。将此方法应用于某炼厂的氢网络集成优化，综合考虑了甲醇产量、设备制造成本和用氢成本等因素。结果表明，该方法能够在提升甲醇产量的同时优化炼厂的氢资源分配，降低设备制造与用氢成本，提升了炼厂七百多万元的年度经济效益。该方法求解高效，优化结果的准确性较传统方法显著提高，为炼厂氢网络与甲醇工艺流程的协同优化提供了有效的解决方案。

关键词: 进化响应面, 氢网络, 甲醇, 优化, 炼厂

Abstract:

One major challenge of hydrogen network integration in refinery is the optimization of hydrogen network with complex process flows. To address this issue, a collaborative optimization method based on evolutionary response surface method is proposed to simultaneously optimize the methanol production process and the hydrogen network. This method establishes mechanistic models for the reactor, flash tank, and distillation column in the CO₂ hydrogenation to methanol process. Based on these, this method constructs corresponding evolutionary surrogate models, which are validated and refined using mechanistic model results. An efficient optimization framework is proposed to enhance model accuracy and algorithmic iteration efficiency. This method is applied to the hydrogen network integration optimization of a refinery, considering factors such as methanol yield, equipment manufacturing costs, and hydrogen consumption. Results demonstrate that the method effectively increases methanol production while optimizing hydrogen network within the refinery, reducing equipment manufacturing and hydrogen consumption costs, and achieving an annual economic benefit of more than 7 million CNY. The method is computationally efficient and significantly improves the accuracy of optimization results compared to traditional approaches, providing an effective solution for the optimization of refinery hydrogen network with methanol production processes.

Key words: evolutionary response surface, hydrogen network, methanol, optimization, refinery

中图分类号:

黄灵军, 朱卿宇, 张宇, 孙维祺, 窦东阳, 王启立. 进化响应面驱动的CO₂加氢制甲醇工艺与氢网络同步优化[J]. 化工进展, 2025, 44(8): 4688-4700.

HUANG Lingjun, ZHU Qingyu, ZHANG Yu, SUN Weiqi, DOU Dongyang, WANG Qili. Simultaneous optimization of hydrogen network with CO₂ hydrogenation to methanol process based on evolutionary response surface method[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4688-4700.

图/表 23

图1 基于进化响应面法的氢网络优化流程

图2 CO2加氢制甲醇典型加工方案

表1 推动力项常数表达式与计算结果

反应	M₁的表达式	M₁值	M₂表达式	M₂值
式(1)	K_CO	2.16×10^-5exp(46800/RT)	K_CO/K₁	9.11×10⁷exp(-51638/RT)
式(2)	$K C O 2$	7.05×10^-7exp(61700/RT)	$K C O 2 / K 2$	6.59×10^-9exp(101384.87/RT)
式(3)	$K C O 2$	7.05×10^-7exp(61700/RT)	$K C O 2 / K 3$	2.78×10⁴exp(2946.87/RT)

表1 推动力项常数表达式与计算结果

反应	M₁的表达式	M₁值	M₂表达式	M₂值
式(1)	K_CO	2.16×10^-5exp(46800/RT)	K_CO/K₁	9.11×10⁷exp(-51638/RT)
式(2)	$K C O 2$	7.05×10^-7exp(61700/RT)	$K C O 2 / K 2$	6.59×10^-9exp(101384.87/RT)
式(3)	$K C O 2$	7.05×10^-7exp(61700/RT)	$K C O 2 / K 3$	2.78×10⁴exp(2946.87/RT)

表2 用于Aspen Plus的推动力常数系数

反应	ln M₁		ln M₂
反应	A	B	A	B
式(1)	-10.7428	5629.06	18.3272	-6210.97
式(2)	-14.1651	7421.22	-18.8370	12194.48
式(3)	-14.1651	7421.22	10.2330	354.45

表3 吸附项常数系数

项	常数	A	B	$∏ c j v j$
1	1	0	0	p $H 2 1 / 2$
2	$K H 2 O / K H 2 1 / 2$	-18.8717	10103.44	$p H 2 O$
3	K_CO	-10.7428	5629.06	$p C O p H 2 1 / 2$
4	$K C O K H 2 O / K H 2 1 / 2$	-29.6144	15732.50	$p C O p H 2 O$
5	$K C O 2$	-14.165	7421.22	$p C O 2 p H 2 1 / 2$
6	$K C O 2 K H 2 O / K$ $H 2 1 / 2$	-33.037	17524.66	$p C O 2 p H 2 O$

表3 吸附项常数系数

项	常数	A	B	$∏ c j v j$
1	1	0	0	p $H 2 1 / 2$
2	$K H 2 O / K H 2 1 / 2$	-18.8717	10103.44	$p H 2 O$
3	K_CO	-10.7428	5629.06	$p C O p H 2 1 / 2$
4	$K C O K H 2 O / K H 2 1 / 2$	-29.6144	15732.50	$p C O p H 2 O$
5	$K C O 2$	-14.165	7421.22	$p C O 2 p H 2 1 / 2$
6	$K C O 2 K H 2 O / K$ $H 2 1 / 2$	-33.037	17524.66	$p C O 2 p H 2 O$

图3 甲醇工艺流程模型

图4 氢网络超结构模型

图5 响应面模型构建流程

图6 反应器响应面模型输入和输出变量

图7 闪蒸罐1响应面模型输入和输出变量

图8 闪蒸罐2响应面模型输入和输出变量

图9 精馏塔响应面模型输入和输出变量

图10 氢网络优化流程

表4 氢源和氢阱参数

装置	氢源		装置	氢阱
装置	摩尔分数	流量/kmol·h^-1	装置	摩尔分数	流量/kmol·h^-1
SRU	0.9300	623.80	HCU	0.8061	2495.00
CRU	0.8000	415.80	NHT	0.7885	180.20
HCU	0.7500	1801.90	DHT	0.7757	554.40
NHT	0.7500	138.60	CNHT	0.7514	720.70
DHT	0.7300	346.50	MS	0.9000	8582.41
CNHT	0.7000	457.40
MS	0.6253	2232.74
HP	0.9500	7782.42

表5 决策变量取值上下限

取值	反应器温度/℃	反应器压力/bar	闪蒸罐1温度/℃	闪蒸罐1压力/bar	闪蒸罐2温度/℃	闪蒸罐2压力/bar
上限	280	120	45	119	45	3
下限	220	80	35	79	35	1

表6 模拟结果与实验结果对比

实验次数	反应体积空速（GHSV）/m³·kg^-1·h^-1	P/bar	T/℃	组分分压/bar				转换率（实验）/%		选择性（实验）/%		转换率（模拟）/%		选择性（模拟）/%
实验次数	反应体积空速（GHSV）/m³·kg^-1·h^-1	P/bar	T/℃	H₂	CO₂	N₂	Ar	CO₂	H₂	CH₃OH	CO	CO₂	H₂	CH₃OH	CO
1	7800	50	200	34.9	8.9	6.2	0	4 .6	2.4	56	44	4.6	2.5	57	43
2	7800	50	210	34.9	8.9	6.2	0	7.7	3.4	45	55	7.2	3.5	46	54
3	7800	50	220	34.9	8.9	6.2	0	11.1	4.8	43	57	11.1	5.0	38	62
4	7800	50	230	34.9	8.9	6.2	0	15.8	6.3	38	62	15.8	6.7	33	67
5	7800	50	210	12.7	6.2	6.2	24.8	5.1	2.9	20	80	5.2	4.3	34	66
6	7800	50	210	18.8	6.2	6.2	18.7	5.9	3.1	33	67	6.7	3.9	39	61
7	7800	50	210	24.4	6.2	6.2	13.1	7.2	3.1	39	61	8.0	3.7	42	58
8	7800	50	210	31.3	6.2	6.2	6.3	9.3	3.2	42	58	9.4	3.5	45	55
9	7800	50	210	37.5	6.2	6.2	0	9.9	3.4	48	52	10.6	3.4	48	52
10	7800	50	210	24.4	12	6.2	7.4	4.8	3.9	37	63	4.3	3.9	41	59
11	7800	50	210	24.4	8.1	6.2	11.3	6.1	3.3	38	62	6.2	3.8	42	58
12	7800	50	210	24.4	4.9	6.2	14.5	8.8	2.9	38	62	9.8	3.7	43	57
13	7800	50	210	24.4	4.1	6.2	15.3	9.8	2.7	40	60	11.5	3.6	43	57
14	7800	65	210	31.7	8.1	8.1	17.1	8.6	4.4	48	52	8.8	4.3	46	54
15	7800	80	210	39	10	10	21	9.9	5.3	51	49	9.6	4.8	49	51
16	11700	50	210	24.4	6.3	6.3	13.1	5.6	2.6	39	61	5.8	2.8	41	59
17	23400	50	210	24.4	6.3	6.3	13.1	2.8	1.4	46	54	3.3	1.6	41	59

图11 反应器出口组分流量模拟值与实验值的对比

图12 甲醇产量随温度的变化

图13 甲醇组分流量和摩尔分数响应面模型与真实结果偏差

图14 优化结果

图15 进化响应面最优氢网络匹配图

图16 贝叶斯算法最优氢网络匹配图

表7 贝叶斯算法与进化响应法结果对比

项目	贝叶斯算法	进化响应面法
反应器温度/℃	247.19	237.9
反应器压力/bar	112.42	120
闪蒸罐1温度/℃	35	35
闪蒸罐1压力/bar	111.42	119
闪蒸罐2温度/℃	39.9	35
闪蒸罐2压力/bar	1.76	1.4
氢气公用工程用量/kmol·h^-1	7829.81	8034.51
甲醇产量/kmol·h^-1	3291.56	3295.32
闪蒸罐1材料成本/CNY	482114.57	494919.77
年度总效益增量/CNY	5722022.60	7163708.71
最优点迭代次数	272	100
计算时间/s	5333	4507

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进化响应面驱动的CO₂加氢制甲醇工艺与氢网络同步优化

Simultaneous optimization of hydrogen network with CO₂ hydrogenation to methanol process based on evolutionary response surface method

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