Optimization of mixing processing considering crude oil procurement selection

doi:10.16085/j.issn.1000-6613.2024-1518

Chemical Industry and Engineering Progress ›› 2025, Vol. 44 ›› Issue (8): 4648-4656.DOI: 10.16085/j.issn.1000-6613.2024-1518

• Process systems modeling and simulation • Previous Articles

Optimization of mixing processing considering crude oil procurement selection

DONG Fenglian¹^,²(), LI Peng³, WEI Zhiwei¹^,², SUN Xin¹^,², XU Hekai⁴, HE Chang⁵^,⁶()

^1.PetroChina Planning and Engineering Institute, Beijing 100086, China
^2.CNPC Laboratory of Oil &Gas Business Chain Optimization, Beijing 100086, China
^3.CNPC Production and Operation Management Department, Beijing 100007, China
^4.Shanshu Technology (Beijing) Co. , Ltd. , Beijing 100102, China
^5.School of Chemical Engineering and Technology, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
^6.Guangdong Engineering Center for Petrochemical Energy Conservation, Guangzhou 510006, Guangdong, China

Received:2024-09-18 Revised:2024-11-20 Online:2025-09-08 Published:2025-08-25
Contact: HE Chang

考虑原油采购选择的混炼加工优化

董丰莲¹^,²(), 李鹏³, 魏志伟¹^,², 孙鑫¹^,², 徐赫锴⁴, 何畅⁵^,⁶()

^1.中国石油天然气股份有限公司规划总院，北京 100086
^2.中国石油天然气集团有限公司油气业务链优化重点实验室，北京 100086
^3.中国石油天然气集团有限公司生产经营管理部，北京 100007
^4.杉数科技(北京)有限公司，北京 100102
^5.中山大学化学工程与技术学院，广东珠海 519082
^6.广东省石化过程节能工程技术研究中心，广东广州 510006

通讯作者: 何畅
作者简介:董丰莲（1982—），女，硕士，高级工程师，研究方向为石油和天然气行业的运筹学优化、智能计算和大数据分析。E-mail：dongfenglian@petrochina.com.cn。
基金资助:
中国石油天然气股份有限公司科学研究与技术开发项目(2021DJ7703)

Abstract

Abstract:

At present, the decision-making process for crude oil procurement and mixing processing plans relies heavily on manual experience or mathematical planning programming methods, which suffer from issues such as long solution times and inability to consider the overall context. To address these issues, we proposed a mixed-integer nonlinear programming model incorporated the “P model” concept, specifically tailoring for typical mixing processes and crude oil procurement needs in refining scenarios. Additionally, an iterative solution algorithm based on p-norm and interior point method was designed based on the unique characteristics of integer variables. The results showed that, under the optimization background of 10 crude oils, 54 physical properties, and 58 processing units, our method outperformed commercial solvers in terms of both speed and economic efficiency. Furthermore, our approach exhibited superior robustness across multiple scenarios.

Key words: optimization, algorithm, petroleum, mixing processing, bilinear, interior point method, norm smoothing

摘要：

目前，原油采购和混炼加工方案多采用人工经验或数学规划方法进行决策，存在求解时间过长以及无法统筹考虑全局性等问题。针对炼化场景下的典型混炼工艺和原油采购要求，结合“P模型”的概念建立了混合整数非线性模型，并根据整数变量的特性设计了基于p范数和内点法的迭代求解算法。结果表明，在10种原油、54种物性、58套加工装置的优化背景下，与商用求解器优化结果相比，采用以上方法可以在短时间内找到一个经济效益更好的原油采购加工方案并且在多个算例下均展现出了更好的鲁棒性。

关键词: 优化, 算法, 石油, 混炼, 双线性, 内点法, 范数平滑

CLC Number:

TQ013.2

DONG Fenglian, LI Peng, WEI Zhiwei, SUN Xin, XU Hekai, HE Chang. Optimization of mixing processing considering crude oil procurement selection[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4648-4656.

董丰莲, 李鹏, 魏志伟, 孙鑫, 徐赫锴, 何畅. 考虑原油采购选择的混炼加工优化[J]. 化工进展, 2025, 44(8): 4648-4656.

Figures/Tables 9

步骤	具体流程
初始化	令 $k = 1$ ，给定 $λ 、 M 0 、 p 0$ ，给定起始解、最大迭代次数K和误差容忍度 $δ$
第1步	若 $k ≤ K$ ，对于给定起始解，使用内点法求解器求解问题，获取最优解
第2步	计算惩罚项 $σ k = λ (B p k - N)$ ，若 $σ k ≤ 0$ ，进入第3步；否则，设定第2步中的最优解为起始解，返回第1步
第3步	计算0~1变量松弛带来的误差 $ε k = m a x i m i n B i k, 1 - B i k$ ，当 $ε k < δ$ ，求解完成，输出最优解；否则进入第4步
第4步	更新步长 $M k = M k - 1 ε - δ ε$ ，进入第5步
第5步	进行如下更新， $p k = p k - 1 - M k 、 k = k + 1$ ，进入第6步
第6步	设定最优解为起始解，返回第1步
输出	最优解 $X $ 、 $Y $

步骤	具体流程
初始化	令 $k = 1$ ，给定 $λ 、 M 0 、 p 0$ ，给定起始解、最大迭代次数K和误差容忍度 $δ$
第1步	若 $k ≤ K$ ，对于给定起始解，使用内点法求解器求解问题，获取最优解
第2步	计算惩罚项 $σ k = λ (B p k - N)$ ，若 $σ k ≤ 0$ ，进入第3步；否则，设定第2步中的最优解为起始解，返回第1步
第3步	计算0~1变量松弛带来的误差 $ε k = m a x i m i n B i k, 1 - B i k$ ，当 $ε k < δ$ ，求解完成，输出最优解；否则进入第4步
第4步	更新步长 $M k = M k - 1 ε - δ ε$ ，进入第5步
第5步	进行如下更新， $p k = p k - 1 - M k 、 k = k + 1$ ，进入第6步
第6步	设定最优解为起始解，返回第1步
输出	最优解 $X $ 、 $Y $

原油品种	算例1选购量/t	算例2选购量/t	算例3选购量/t
纳波	33.0×10⁴	33×10⁴	33×10⁴
沙重	36.3×10⁴	15.0×10⁴	15.0×10⁴
巴士拉重	—	9.5×10⁴	9.5×10⁴
阿曼	—	50.1×10⁴	50.1×10⁴
卡斯蒂纳	—	12.0×10⁴	12.0×10⁴
卢拉	49.3×10⁴	—	—
科威特	—	—	—
巴士拉中	—	—	—
玛氏	—	—	—
沙轻	—	—	—
可选购的原油种类上限	≤3	≤5	≤7

原油种类上限	p范数	求解策略	目标函数/CNY	计算时间/s
3	—	Gurobi	26871.24×10⁴	1000
3	0.2	本文提出的算法	—	—
3	0.4		24954.75×10⁴	82.04
3	0.5		26871.73×10⁴	210.86
3	0.6		26871.73×10⁴	39.09
3	0.7		26871.73×10⁴	33.54
3	0.8		26871.73×10⁴	66.72

原油种类上限	p范数	求解策略	目标函数/CNY	计算时间/s
5	—	Gurobi	36094.12×10⁴	1000
5	0.2	本文提出的算法	33529.74×10⁴	18.70
5	0.4		36123.50×10⁴	22.87
5	0.5		36123.50×10⁴	21.21
5	0.6		36123.50×10⁴	24.69
5	0.7		36123.50×10⁴	21.39
5	0.8		36123.50×10⁴	21.74

原油种类上限	p范数	求解策略	目标函数/CNY	计算时间/s
7	—	Gurobi	36051.03×10⁴	1000
7	0.2	本文提出的算法	36123.50×10⁴	31.07
7	0.4		36123.50×10⁴	25.81
7	0.5		36123.50×10⁴	25.03
7	0.6		36123.50×10⁴	27.79
7	0.7		36123.50×10⁴	30.93
7	0.8		36123.23×10⁴	29.57

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Optimization of mixing processing considering crude oil procurement selection

考虑原油采购选择的混炼加工优化

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