考虑原油性质波动的炼厂氢气网络集成优化

doi:10.11949/0438-1157.20211657

摘要/Abstract

摘要：

针对原油性质的不确定性，提出了一种基于质量传递机理的随机规划建模框架，以实现炼厂氢气网络在经济效益和抗扰能力上的同步优化。该框架耦合了常减压蒸馏、加氢精制以及闪蒸分离等过程单元，从微观上解析原油性质波动对网络运行的影响；采用了代理模型技术增设脱硫模块，并利用了二阶段随机规划方法改造管网，从宏观上优化氢气网络以满足生产要求。为验证所提方法的有效性和适用性，对某一现有的炼厂氢网络进行了改造设计研究。结果表明，集成过程单元的多场景优化策略能够有效提升网络的经济性能，并且能使其灵活应对因原油性质波动引起的操作场景的改变。

关键词: 氢气网络, 优化设计, 集成, 过程系统, 代理模型技术, 随机规划

Abstract:

A stochastic programming modeling framework based on mass transfer mechanism was proposed, which took the uncertainty of crude oil properties into consideration, to achieve the simultaneous optimization of economic performance and disturbance rejection ability of refinery hydrogen network. The framework integrated process units such as atmospheric and vacuum distillation, hydrofining and flash separation to analyze the impact of crude oil properties fluctuations on network operation microscopically; adopted surrogate-assisted techniques to embed desulfurization unit and used the two-stage stochastic programming approach to retrofit network, so as to optimize hydrogen network macroscopically to meet production requirements. To verify the effectiveness and applicability of the proposed method, studies on the optimization design of an industrial hydrogen system were presented. The results show that the multi-scenario optimization strategy integrated process units can effectively improve the economic performance of hydrogen network. And it can flexibly respond to changes in operating scenarios caused by fluctuations in crude oil properties.

Key words: hydrogen network, optimal design, integration, process systems, surrogate modeling techniques, stochastic programming

中图分类号:

TE 68

张欣, 周利, 王诗慧, 吉旭, 毕可鑫. 考虑原油性质波动的炼厂氢气网络集成优化[J]. 化工学报, 2022, 73(4): 1631-1646.

Xin ZHANG, Li ZHOU, Shihui WANG, Xu JI, Kexin BI. Integrated optimization of refinery hydrogen networks with crude oil properties fluctuations[J]. CIESC Journal, 2022, 73(4): 1631-1646.

图/表 22

图1 南疆原油性质分析数据

Fig.1 Analysis data of crude oil properties in Southern Xinjiang

图2 氢气网络超结构

Fig.2 State-space superstructure of the hydrogen network

图3 炼厂氢气分配网络建模框架

Fig.3 Modeling frameworks for refinery hydrogen distribution network

图4 炼厂现有氢气网络结构图

Fig.4 The existing hydrogen network structure in refinery

表1 各场景下原油中的硫、氮含量数据

Table 1 Sulfur and nitrogen content data of crude oil in each scenario

场景	硫含量/（mg/kg）	氮含量/（mg/kg）	发生概率
1	0.9097	0.2016	0.0400
2	0.9097	0.2058	0.1000
3	0.9097	0.2100	0.0600
4	0.9493	0.2016	0.1000
5	0.9493	0.2058	0.2500
6	0.9493	0.2100	0.1500
7	0.9888	0.2106	0.0600
8	0.9888	0.2058	0.1500
9	0.9888	0.2100	0.0900

表2 加氢处理装置进料流量及操作条件

Table 2 Feed flowrate and operating conditions of hydrotreaters

加氢处理装置	油品流量/(t/h)	T/K	P/bar	LHSV/h^-1
DHT-1	150.00	633	67.2	1.92
DHT-2	373.81	648	70.0	2.00
GHT	216.67	513	27.0	3.00
KHT-1	50.00	553	38.3	2.25
KHT-2	70.00	573	54.5	1.79

表3 预留脱硫塔和氢阱之间的管道距离

Table 3 Piping distances among the reserved location for the desulfurization units and the hydrogen sinks

项目	距离/m
项目	DHT-1	DHT-2	GHT	KHT-1	KHT-2
HP-DS	540	360	350	450	550
LP-DS	480	310	500	750	90

表4 加氢处理装置进料组成

Table 4 Feed composition of hydrotreaters

Item	DHT-1	DHT-2	GHT	KHT-1	KHT-2
DHT1-PC1	0.7041	0	0	0	0
DHT1-PC2	0.2959	0	0	0	0
DHT2-PC1	0	0.7935	0	0	0
DHT2-PC2	0	0.2065	0	0	0
GHT-PC	0	0	1.0000	0	0
KHT1-PC	0	0	0	1.0000	0
KHT2-PC	0	0	0	0	1.0000

表5 虚拟组分性质

Table 5 Properties of pseudo-components

虚拟组分	相对密度	分子量	馏程/℃
虚拟组分	相对密度	分子量	HK(初馏点)	10%	50%	90%	KK(终馏点)
DHT1-PC1	0.8478	239.567	208	249	300	346	370
DHT1-PC2	0.8647	246.974	225	256	311	354	380
DHT2-PC1	0.8439	233.709	211	248	291	347	370
DHT2-PC2	0.8396	227.525	180	233	288	348	376
GHT-PC	0.7441	95.420	36.9	49.9	105	172	202
KHT1-PC	0.7832	155.505	151	172	189	230	—
KHT2-PC	0.8122	180.524	—	124	257	363	373

表6 脱硫装置的输入变量范围

Table 6 Domain of the input variables for the desulfurization unit

项目	输入变量范围/（kmol/h）
项目	$F H 2 i n$	$F C 1 i n$	$F C 2 i n$	$F C 3 i n$	$F C 4 i n$	$F C 5 i n$	$F H 2 S i n$	$F N H 3 i n$	$F M D E A i n$
HP-DS^U	7000	500	100	90	35	10	40	10	65
HP-DS^L	3000	170	45	40	10	1.5	10	2	10
LP-DS^U	150	40	8.4	5	1	0.5	4.0	0.15	10
LP-DS^L	20	5	0.5	0.1	0.1	0.01	0.1	0.01	2

表6 脱硫装置的输入变量范围

Table 6 Domain of the input variables for the desulfurization unit

项目	输入变量范围/（kmol/h）
项目	$F H 2 i n$	$F C 1 i n$	$F C 2 i n$	$F C 3 i n$	$F C 4 i n$	$F C 5 i n$	$F H 2 S i n$	$F N H 3 i n$	$F M D E A i n$
HP-DS^U	7000	500	100	90	35	10	40	10	65
HP-DS^L	3000	170	45	40	10	1.5	10	2	10
LP-DS^U	150	40	8.4	5	1	0.5	4.0	0.15	10
LP-DS^L	20	5	0.5	0.1	0.1	0.01	0.1	0.01	2

图5 代理模型精度与复杂度对比

Fig.5 Surrogate model comparisons in terms of accuracy and complexity

表7 脱硫单元代理模型验证结果

Table 7 Results from the data correlation of the desulfurization unit

输出变量	HP-DS单元		LP-DS单元
输出变量	RMSE	R²	RMSE	R²
$F H 2 o u t$	0.00023	0.9999	0.00001	0.9999
$F C 1 o u t$	0.00034	0.9999	0.00007	0.9999
$F C 2 o u t$	0.00429	0.9996	0.00333	0.9997
$F C 3 o u t$	0.01490	0.9948	0.00430	0.9996
$F H 2 S o u t$	0.02499	0.9888	0.01656	0.9940
$F f r a o u t$	0.00485	0.9993	0.00034	0.9999

表7 脱硫单元代理模型验证结果

Table 7 Results from the data correlation of the desulfurization unit

输出变量	HP-DS单元		LP-DS单元
输出变量	RMSE	R²	RMSE	R²
$F H 2 o u t$	0.00023	0.9999	0.00001	0.9999
$F C 1 o u t$	0.00034	0.9999	0.00007	0.9999
$F C 2 o u t$	0.00429	0.9996	0.00333	0.9997
$F C 3 o u t$	0.01490	0.9948	0.00430	0.9996
$F H 2 S o u t$	0.02499	0.9888	0.01656	0.9940
$F f r a o u t$	0.00485	0.9993	0.00034	0.9999

图6 HP-DS单元代理模型的残差图

Fig.6 Residual plots of the surrogate model obtained for the HP-DS unit

图7 LP-DS单元代理模型的残差图

Fig.7 Residual plots of the surrogate model obtained for the LP-DS unit

图8 基于确定性场景的氢气网络优化设计

Fig.8 Optimal design of hydrogen network from deterministic model

Table 9 Computational effort of the solver and optimization time for different models

项目	原氢气网络模型	确定性模型	随机规划模型
变量数量	62	97	97
方程个数	68	132	132
CPU计算时间	0.016 s	2.578 s	44.875 s
计算机配置	64位操作系统，16 GB RAM 1.10 GHz Intel Core i7-10710U
求解器	GAMS-DICOPT

表9　各模型的计算量及优化时间

图9 各场景下DHT-1中H2的消耗量及H2S的生成量

Fig.9 Consumption of H2 and production of H2S on each scenario

图10 基于随机规划的氢气网络优化设计

Fig.10 Optimal design of hydrogen network from stochastic programming model

图11 确定性模型与随机规划模型的脱硫单元处理量及脱硫率对比

Fig.11 Comparison of desulfurization unit inlet flowrate and desulfurization rate between deterministic model and stochastic programming model

图12 低硫、低氮场景下的运行策略

Fig.12 Operation strategy of the scenario with low sulfur and low nitrogen

图13 高硫、高氮场景下的运行策略

Fig.13 Operation strategy of the scenario with high sulfur and high nitrogen

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