炼油厂原油选购多目标优化
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摘要
为保证原油加工装置的平稳生产和取得好的经济效益,大多数炼油厂会通过控制混合性质同时选购几种原油进行混炼.原油选购优化现已成为提高炼油厂经济效益的重要手段.以炼油厂原油选购利润最大以及混合原油与目标原油的性质相对偏差最小作为目标,建立了一个多目标原油选购优化混合整数非线性规划模型.在多目标列队竞争算法(MOLCA)的基础上,改进了目标值的排序方式,并加入变异算子,提出了一种改进的多目标列队竞争算法(IMOLCA).利用IMOLCA优化求解该模型,可以得到两优化目标最优解集的Pareto前沿.通过实例分析,验证了模型和算法的有效性.计算得到的方案可为炼油厂原油选购提供参考.
In order to ensure the steady production and achieve economic benefits of crude oil processing unit, most refineries often need to control the blending properties and blend different types of crude oil simultaneously. Crude oil purchase optimization has become an important means to improve the economic benefits in refineries. Aiming at maximizing the profit of crude oil purchase and minimizing the property relative deviation between blending crude oil and target crude oil, a multi-objective optimization model of crude oil purchase with mixed integer nonlinear programming is established. Based on the basic multi-objective line competition algorithm(MOLCA), the sort means of target values is modified and mutation operator is added, and an improved multi-objective line competition algorithm(IMOLCA) is proposed. Then the IMOLCA is employed to solve the optimization model, which can obtain the Pareto frontier of two optimization target solution sets. By analysis of example, it is proved that the model and algorithm are effective. The blending solution can provide a reference for crude oil purchase in the refinery.
In order to ensure the steady production and achieve economic benefits of crude oil processing unit, most refineries often need to control the blending properties and blend different types of crude oil simultaneously. Crude oil purchase optimization has become an important means to improve the economic benefits in refineries. Aiming at maximizing the profit of crude oil purchase and minimizing the property relative deviation between blending crude oil and target crude oil, a multi-objective optimization model of crude oil purchase with mixed integer nonlinear programming is established. Based on the basic multi-objective line competition algorithm(MOLCA), the sort means of target values is modified and mutation operator is added, and an improved multi-objective line competition algorithm(IMOLCA) is proposed. Then the IMOLCA is employed to solve the optimization model, which can obtain the Pareto frontier of two optimization target solution sets. By analysis of example, it is proved that the model and algorithm are effective. The blending solution can provide a reference for crude oil purchase in the refinery.
引文
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[10] 马巍,杨辉华,李虎,等.多目标原油选择与混合优化方法 [J].计算机仿真,2014,31(11):132-137.MA Wei,YANG Huihua,LI Hu,et al.Method for multiobjective crude oil selection and blending optimization [J].Computer Simulation,2014,31(11):132-137.(in Chinese)
[11] YAN Liexiang,MA Dexian.Global optimization of non-convex nonlinear programs using line-up competition algorithm [J].Computers and Chemical Engineering,2001,25(11/12):1601-1610.
[12] YAN Liexiang,SHEN Kun,HU Shenghua.Solving mixed integer nonlinear programming problems with line-up competition algorithm [J].Computers and Chemical Engineering,2004,28(12):2647-2657.
[2] 王一冠,蒋决根.RPMS在炼化企业中的应用 [J].石油化工技术经济,2007,23(1):45-49.WANG Yiguan,JIANG Juegen.Application of RPMS in petroleum refining and chemical enterprises [J].Techno-Economics in Petrochemicals,2007,23(1):45-49.(in Chinese)
[3] GANJI H,ZAHEDI S,AHMADI MARVAST A,et al.Determination of suitable feedstock for refineries utilizing LP and NLP models [J].International Journal of Chemical Engineering and Applications,2010,1(3):225-310.
[4] DE RUBIO J J,YU Wen.Modelling of crude oil blending via discrete-time neural networks [C] // 2004 1st International Conference on Electrical and Electronics Engineering,ICEEE.Acapulco:IEEE,2004:427-432.
[5] 李进,廖良才,谭跃进.一种简单的混合原油性质估算方法 [J].石油化工自动化,2005(5):37-39.LI Jin,LIAO Liangcai,TAN Yuejin.A simple method for estimating the properties of mixed crude oil [J].Automation in Petro-Chemical Industry,2005(5):37-39.(in Chinese)
[6] DE RUBIO J J.Least square neural network model of the crude oil blending process [J].Neural Networks,2016,78(SI):88-96.
[7] LóPEZ D C,HOYOS L J,MAHECHA C A,et al.Optimization model of crude oil distillation units for optimal crude oil blending and operating conditions [J].Industrial and Engineering Chemistry Research,2013,52(36):12993-13005.
[8] 杜祜康,赵英凯.基于遗传算法的原油混合优化研究 [J].化工自动化及仪表,2010,37(1):8-10.DU Hukang,ZHAO Yingkai.Research of mixed optimization of crude oil based on genetic algorithms [J].Control and Instruments in Chemical Industry,2010,37(1):8-10.(in Chinese)
[9] MUTEKI K,MACGREGOR J F,UEDA T.Rapid development of new polymer blends:the optimal selection of materials and blend ratios [J].Industrial and Engineering Chemistry Research,2006,45(13):4653-4660.
[10] 马巍,杨辉华,李虎,等.多目标原油选择与混合优化方法 [J].计算机仿真,2014,31(11):132-137.MA Wei,YANG Huihua,LI Hu,et al.Method for multiobjective crude oil selection and blending optimization [J].Computer Simulation,2014,31(11):132-137.(in Chinese)
[11] YAN Liexiang,MA Dexian.Global optimization of non-convex nonlinear programs using line-up competition algorithm [J].Computers and Chemical Engineering,2001,25(11/12):1601-1610.
[12] YAN Liexiang,SHEN Kun,HU Shenghua.Solving mixed integer nonlinear programming problems with line-up competition algorithm [J].Computers and Chemical Engineering,2004,28(12):2647-2657.