原油作业过程优化的研究进展

doi:10.11949/0438-1157.20210850

摘要/Abstract

摘要：

原油作业过程是石油供应链的重要组成环节，包括炼油企业生产过程中的原油采购、原油分配、原油输送、原油储存和原油调和等多个工业流程。原油作业过程优化具有很高的学术理论价值与工业应用价值，与其相关的研究工作是目前学术界与工业界共同关注的热点。首先简要描述了原油作业过程，并对其优化问题的难点进行分析；其次，分别从优化模型、优化算法以及不确定性优化方法三个研究角度，重点阐述了原油采购优化、原油储运优化、原油调和优化以及不确定性条件下原油作业优化四个主要研究方向的学术进展，并对当前已有的研究成果进行了归纳总结；最后，对原油作业过程优化当前存在的一些问题提出了建议，并对该领域未来的发展方向与趋势进行了展望。

关键词: 原油作业过程, 优化, 模型, 算法, 不确定性

Abstract:

Crude oil operations are an important part of petroleum supply chain, including multiple industrial processes such as crude oil procurement, crude oil distribution, crude oil transportation, crude oil storage and crude oil blending in the production process of refinery enterprises. The optimization of crude oil operation process has high academic theoretical value and industrial application value, and its related work is the currently focus of academia and industry at present. This paper is divided into three parts to summarize the research progress of crude oil operation process optimization. Firstly, this paper briefly describes the crude oil operation process, and analyzes the difficulties of its optimization problems. Secondly, from the three research perspectives of optimization model, optimization algorithm and uncertainty optimization method, this paper focuses on the four main research directions of crude oil procurement optimization, crude oil storage and transportation optimization, crude oil blending optimization, and crude oil operations optimization under uncertain conditions. Finally, this paper puts forward some suggestions on the current problems in the optimization of crude oil operation process, and prospects the future development direction and trend of this field.

Key words: crude oil operation, optimization, model, algorithm, uncertainty

中图分类号:

TE 624

郑万鹏, 高小永, 朱桂瑶, 左信. 原油作业过程优化的研究进展[J]. 化工学报, 2021, 72(11): 5481-5501.

Wanpeng ZHENG, Xiaoyong GAO, Guiyao ZHU, Xin ZUO. Research progress on crude oil operation optimization[J]. CIESC Journal, 2021, 72(11): 5481-5501.

图/表 7

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建模方法	研究创新点	优缺点
DMA^[5]	克服此前预测模型不允许时变的局限性	克服此前部分预测模型的应用局限性，预测性能的部分回归评价指标优于经典预测模型，但模型精度不够理想，计算较为复杂
EMD^[6]	解决原油价格预测的时滞问题
GEP^[7]	提升原油价格预测的RMSE与MAE预测性能
MSM,GARCH^[8]	提升包含损失函数的原油风险价值预测的准确性
TVP^[9]	提升原油价格预测的MSPE和方向精度的预测性能
Huber^[10]	引入正则化约束避免预测的过拟合问题
Bayes^[11]	考虑影响石油价格的独立变量因素	预测结果可长期保持稳定，但模型精度不够理想，计算时间长
AR,ARMA^[12]	研究样本外的预测性能和多模型组合的预测性能
GASVM,GABP^[13]	结合遗传算法与人工智能并考虑了长期价格影响因素
MMGARCH^[14]	考虑原油价格波动率的结构变化和长记忆性特征
FC-MACRO,FC-TECH,FC-ALL^[15]	研究宏观经济指标下的双变量模型预测问题	预测性能的部分回归评价指标优于单一模型，但组合模型的结构十分复杂，计算过程长且计算难度高
LR,ANN,SVR^[16]	研究多粒度异构组合方法在原油价格预测的应用	预测性能的部分回归评价指标优于单一模型，但组合模型的结构十分复杂，计算过程长且计算难度高

建模方法	研究创新点	优缺点
DMA^[5]	克服此前预测模型不允许时变的局限性	克服此前部分预测模型的应用局限性，预测性能的部分回归评价指标优于经典预测模型，但模型精度不够理想，计算较为复杂
EMD^[6]	解决原油价格预测的时滞问题
GEP^[7]	提升原油价格预测的RMSE与MAE预测性能
MSM,GARCH^[8]	提升包含损失函数的原油风险价值预测的准确性
TVP^[9]	提升原油价格预测的MSPE和方向精度的预测性能
Huber^[10]	引入正则化约束避免预测的过拟合问题
Bayes^[11]	考虑影响石油价格的独立变量因素	预测结果可长期保持稳定，但模型精度不够理想，计算时间长
AR,ARMA^[12]	研究样本外的预测性能和多模型组合的预测性能
GASVM,GABP^[13]	结合遗传算法与人工智能并考虑了长期价格影响因素
MMGARCH^[14]	考虑原油价格波动率的结构变化和长记忆性特征
FC-MACRO,FC-TECH,FC-ALL^[15]	研究宏观经济指标下的双变量模型预测问题	预测性能的部分回归评价指标优于单一模型，但组合模型的结构十分复杂，计算过程长且计算难度高
LR,ANN,SVR^[16]	研究多粒度异构组合方法在原油价格预测的应用	预测性能的部分回归评价指标优于单一模型，但组合模型的结构十分复杂，计算过程长且计算难度高

文献	优化模型	优化算法	算法创新点	算法设计思路与优缺点
[18]	MILP模型	分枝定界法	引入分枝定界的优先队列方法和特殊有序集方法	算法的主要思想在于缩小解的搜索空间，具有算法结构简单的优点，但计算速度慢，对于模型规模存在严格限制，无法求解具有大规模变量的优化模型
[35]	MILP模型	分枝定界法	引入局部分枝割的不等式方法
[19-20]	MINLP模型	迭代算法	高维决策变量迭代分解为低维变量	算法的主要思想在于通过迭代以降低模型决策变量维数及约束条件线性化，具有适用范围广、非线性系统拟合能力强的优点，但求解时间会随迭代次数增加而快速增加，且优化结果性能一般
[21-22]	MINLP模型	迭代算法	提出两阶段的MILP-NLP迭代方法
[24-25]	MILP模型	迭代算法	提出基于组合时间槽的迭代方法
[38]	MINLP模型	迭代算法	非线性约束迭代转化为线性约束
[39-40]	MINLP模型	迭代算法	提出带域约简的MILP-NLP迭代算法
[26]	MINLP模型	松弛算法	引入线性可加性指数与局部松弛策略	算法主要思想在于求出解的下界以缩小解空间，适用于组合优化问题，但优化结果性能较差，且在一些情况下无法得到可行解
[34]	MINLP模型	群搜索算法	提出置换变异、反转变异和插入变异三组变异策略	算法主要思想在于使游荡者跳出局部最优，其全局搜索的质量和效率都较好，但算法结构复杂，优化性能和收敛速度一般，局部搜索能力差
[29]	MILP模型	滚动时域策略	在线进行且沿时间轴反复滚动优化	算法主要思想在于转化解的时域，在较小且重叠的时域范围内求解，具有计算速度快、计算结果精度高的优点，但算法结构通常较为复杂，应用局限性大
[32-33]	MILP模型	滚动时域策略	在简单分解的基础上添加辅助时间片段和安全保障约束
[41]	MINLP模型	遗传算法	提出NSGA-Ⅱ算法并使用一条染色体来编码可行的时间表	算法主要思想在于以决策变量的编码作为运算对象，直接以目标函数值作为搜索信息，具有计算简单，全局搜索性好的优点，但算法的计算效率低，易过早收敛，且需对不可行解采用阈值
[42]	MINLP模型	遗传算法	利用加权函数将多目标问题转化为单目标问题
[43]	MINLP模型	遗传算法	提出NSGA-Ⅲ算法并将多目标优化问题转换为离散的动态资源分配问题
[44]	MINLP模型	遗传算法	提出SAGA算法并使得GA染色体使用基于图的表示形式

文献	优化模型	优化算法	算法创新点	算法设计思路与优缺点
[18]	MILP模型	分枝定界法	引入分枝定界的优先队列方法和特殊有序集方法	算法的主要思想在于缩小解的搜索空间，具有算法结构简单的优点，但计算速度慢，对于模型规模存在严格限制，无法求解具有大规模变量的优化模型
[35]	MILP模型	分枝定界法	引入局部分枝割的不等式方法
[19-20]	MINLP模型	迭代算法	高维决策变量迭代分解为低维变量	算法的主要思想在于通过迭代以降低模型决策变量维数及约束条件线性化，具有适用范围广、非线性系统拟合能力强的优点，但求解时间会随迭代次数增加而快速增加，且优化结果性能一般
[21-22]	MINLP模型	迭代算法	提出两阶段的MILP-NLP迭代方法
[24-25]	MILP模型	迭代算法	提出基于组合时间槽的迭代方法
[38]	MINLP模型	迭代算法	非线性约束迭代转化为线性约束
[39-40]	MINLP模型	迭代算法	提出带域约简的MILP-NLP迭代算法
[26]	MINLP模型	松弛算法	引入线性可加性指数与局部松弛策略	算法主要思想在于求出解的下界以缩小解空间，适用于组合优化问题，但优化结果性能较差，且在一些情况下无法得到可行解
[34]	MINLP模型	群搜索算法	提出置换变异、反转变异和插入变异三组变异策略	算法主要思想在于使游荡者跳出局部最优，其全局搜索的质量和效率都较好，但算法结构复杂，优化性能和收敛速度一般，局部搜索能力差
[29]	MILP模型	滚动时域策略	在线进行且沿时间轴反复滚动优化	算法主要思想在于转化解的时域，在较小且重叠的时域范围内求解，具有计算速度快、计算结果精度高的优点，但算法结构通常较为复杂，应用局限性大
[32-33]	MILP模型	滚动时域策略	在简单分解的基础上添加辅助时间片段和安全保障约束
[41]	MINLP模型	遗传算法	提出NSGA-Ⅱ算法并使用一条染色体来编码可行的时间表	算法主要思想在于以决策变量的编码作为运算对象，直接以目标函数值作为搜索信息，具有计算简单，全局搜索性好的优点，但算法的计算效率低，易过早收敛，且需对不可行解采用阈值
[42]	MINLP模型	遗传算法	利用加权函数将多目标问题转化为单目标问题
[43]	MINLP模型	遗传算法	提出NSGA-Ⅲ算法并将多目标优化问题转换为离散的动态资源分配问题
[44]	MINLP模型	遗传算法	提出SAGA算法并使得GA染色体使用基于图的表示形式

文献	优化模型	优化算法	算法创新点	算法设计思路与优缺点
[65]	MIP模型	广义梯度下降法	—	算法的主要思想在于递归性地逼近最小偏差模型从而得到局部最优解，结构简单、计算快速；但非线性拟合能力差
[67]	MILP模型	迭代算法	MINLP模型被多个连续的MILP 模型近似代替	算法的主要思想在于通过迭代以降低模型决策变量维数及约束条件线性化，具有适用范围广、非线性系统拟合能力强的优点；但求解时间会随迭代次数增加而快速增加，且优化结果性能一般
[77-78]	MINLP模型	迭代算法	提出MILP-NLP和MILP- MINLP 两步求解策略
[68]	MILP模型	松弛算法	引入SPM、MPM及其修正模型以确定解的上下界	算法主要思想在于求出解的下界以缩小解空间，适用于组合优化问题；但优化结果性能较差，且在一些情况下无法得到可行解
[71]	MINLP模型	松弛算法	利用分段线性松弛将混合原油性质计算转化为线性形式
[89]	MINLP模型	松弛算法	通过求解MINLP对非凸最小值的松弛得到解的上下界
[93]	MINLP模型	松弛算法	提出PMCR和NMDT技术以计算全局最优估计
[69]	MIQCP模型	离散化算法	利用IHCD法将MIQCP模型近似为MILP模型	算法的主要思想在于在不改变数据相对大小的前提下对数据进行相应缩小，本质是一种哈希算法，适用于非线性问题且对异常数据鲁棒性较强；但对于数据的区间划分要求极高
[70]	MINLP模型	启发式算法	提出一种基于NLP-MILP两层分解的启发式算法	算法的主要思想在于通过邻域搜索，逼近最优解从而得到相对优解，求解快速有效；但不适用于大规模NP问题且无法保证得到全局最优解
[90-91]	MINLP模型	启发式算法	引入一系列将非线性约束线性化的启发式规则
[72-73]	MINLP模型	序优化算法	利用序优化求解具有顺序-连续变量两层结构优化问题	算法的主要思想在于寻优过程通过解空间采样、粗糙评价和排序比较等策略以得到较好的解，可极大减少求解计算量；但无法保证得到最优解
[80]	MINLP模型	聚类算法	利用K-means聚类算法克服数据中极端值和异常值的影响	算法主要思想在于给定一个数据点集合和聚类数目K，根据距离函数将数据分入K个聚类中，算法简单且易实现；但对数据要求高，在大规模数据上收敛较慢，K值难以选取
[84-85]	MINLP模型	分枝定界法	基于分枝定界的全局优化方法有效减少了模型的变量和约束数量	算法的主要思想在于缩小解的搜索空间，具有算法结构简单的优点；但计算速度慢，对于模型规模存在严格限制，无法求解具有大规模变量的优化模型
[92]	MINLP模型	分枝定界法	提出基于分枝定界法中双线性项的分段线性下界估计理论