五维数字孪生汽油调合闭环优化

doi:10.16182/j.issn1004731x.joss.26-0102

摘要/Abstract

摘要：

为应对汽油调合面临的非线性约束严苛、原料波动频繁等挑战，设计并开发了一套基于五维数字孪生理论的自适应调合优化系统。构建五维模型架构，实现调合全流程的虚实映射与协同。提出“模型在线校正-配方动态寻优”的闭环控制策略：采用带约束的递推最小二乘法更新辛烷值调合效应模型参数，解决原料波动导致的模型失配问题；结合线性衰减权重粒子群算法求解多约束下的最优配方，提升优化精度与算法稳定性。仿真结果表明：系统的通信延迟低于250 ms，能够支持大滞后过程的实时监控与动态优化，有效抑制原料扰动，实现关键指标卡边控制，为智能炼厂提供了可控、可视的优化范式。

关键词: 汽油调合, 数字孪生, 五维模型, 粒子群优化算法, 递推最小二乘算法

Abstract:

An adaptive blending optimization system based on five-dimensional digital twin theory is designed and developed to address the challenges of strict nonlinear constraints and frequent raw material fluctuations. A five-dimensional model architecture is constructed to achieve virtual-real mapping and synergy throughout the blending process. A closed-loop control strategy of "online model correction and dynamic recipe optimization" is proposed. The constrained recursive least squares (RLS) algorithm is adopted to update the octane blending effect model parameters, resolving the model mismatch caused by raw material fluctuations. The linear decreasing weight particle swarm optimization algorithm (LDW-PSO) is combined to determine the optimal recipe under multiple constraints, thereby improving optimization accuracy and algorithm stability. Simulation results demonstrate that the system communication latency is less than 250 ms. It supports real-time monitoring and dynamic optimization for large time-delay processes, effectively suppresses raw material disturbances, and achieves boundary control of key indicators. This system provides a controllable and visible optimization paradigm for intelligent refineries.

Key words: gasoline blending, digital twin, five-dimensional model, particle swarm optimization (PSO) algorithm, recursive least squares algorithm

中图分类号:

TP273

房亚宁,何仁初 . 五维数字孪生汽油调合闭环优化[J]. 系统仿真学报, 2026, 38(10): 2840-2856.

Fang Yaning,He Renchu . Five-dimensional Digital Twin System for Closed-loop Gasoline Blending Optimization[J]. Journal of System Simulation, 2026, 38(10): 2840-2856.

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工具名称	作用
Blender	构建储罐、管阀件及附属设施的精细三维网格模型，并进行材质渲染与纹理烘焙
Visual Components	导入几何模型并赋予运动逻辑与行为属性，搭建数字孪生场景
InfluxDB	针对调合过程高频写入、低延迟查询的特性，提供过程数据读写服务，存储调合工艺参数及算法中间变量
KEPServerEX	OPC UA通信网关，消除底层设备与上层应用间的协议壁垒，实现工业协议的标准化转换
Python	算法与控制核心，通过多线程架构调度RLS模型更新与LDW-PSO配方优化算法，充当连接各异构模块的“数据胶水”
Aspen HYSYS	物理实体代理/机理仿真，提供严谨的热力学计算引擎，模拟不同配方下的组分物性变化，在验证阶段替代真实的物理装置

序号	Aspen HYSYS名称	CAS编号	中文名称
1	MTBE	1634-04-4	甲基叔丁基醚
2	p-Xylene	106-42-3	二甲苯
3	24-Mpentane	108-08-7	2,4-二甲基戊烷
4	i-Pentane	78-78-4	异戊烷
5	Benzene	71-43-2	苯
6	2-Eth-1-C5=	3404-71-5	2-乙基-1 -戊烯
7	224-Mpentane	540-84-1	异辛烷
8	n-Octane	111-65-9	正辛烷
9	3-Eth-1-C5=	4038-04-4	1-戊烯
10	n-Hexane	110-54-3	正己烷
11	n-Heptane	142-82-5	正庚烷
12	2-Mpentane	107-83-5	2-甲基戊烷
13	n-Pentane	109-66-0	正戊烷
14	Cyclobutane	287-23-0	环丁烷
15	Toluene	108-88-3	甲苯
16	Thiophene	110-02-1	噻吩

质量指标	国VIB约束界限	实际动态波动区间	运行轨迹均值	最大波动极差
RON	≥92	[92.003 8, 92.005 7]	92.004 5	0.001 9
蒸汽压/kPa	≤70	[69.995 9, 69.999 4]	69.998 5	0.003 5
烯烃含量/%	≤15	[5.739 0, 6.041 8]	5.921 1	0.302 8
芳烃含量/%	≤35	[34.397 8, 34.751 6]	34.576 7	0.353 8
苯含量/%	≤0.8	[0.627 2, 0.640 9]	0.635 17	0.013 7
氧含量/%	≤2.7	[0.271 5, 0.305 5]	0.298 4	0.034 0