基于改进蚁群算法的油品调和配方优化研究
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摘要
油品调和是利用炼油厂装置生产出的多种组分产品,按照调和配方生产出符合质量指标的成品汽油。油品调和是炼油厂生产成品油的关键环节,其中,调和配方直接影响到成品油的合格率,关系到炼油企业的经济效益,求解调和配方本身是一个典型的复杂非线性约束函数优化问题。因此,获得满意的调和配方是实现油品调和的难点和关键环节。
本文通过对国内和国外油品调和技术的分析,首先建立了一种新的油品调和优化配方模型,该模型解决了调和优化模型与实际生产过程的不匹配问题,具有较高精确度；然后提出了一种改进的具有交叉算子的蚁群算法,该算法将遗传算法中的交叉算子引入蚁群算法中,加强了蚁群算法的全局搜索能力。在局部搜索过程中,应用Hooke-Jeeves方法改善了算法的寻优性能,加快了算法的收敛速率。仿真结果表明,新算法能够获得非常理想的配方模型、保证对质量指标的卡边要求、获得最大的调和利润。最后,为实现油品调和的实时控制,提出了在线调和解决方案。该方案利用近红外分析仪对油品的质量指标进行实时监测,然后将测得的调和组分指标传送给计算机,实现了对油品配方模型的在线调整,得到了能够实时调整的调和配方。
The gasoline blending process is very important for refineries, and the blending recipe determined the final profits. The blending recipe directly affects the gasoline pass rate, and the blending process is a typical nonlinear constrained function optimization problem. Thus, to get a satisfactory gasoline recipe is the difficulties link and key process for refinery researchers.
It is difficult to obtain satisfying optimum solution by traditional methods. According to this problem, an improved ant colony algorithm, called Ant Colony Optimization algorithm with Crossover operator (ACOC), was presented. The proposed algorithm introduced crossover operator into the ant colony algorithm, and improved the global search ability. In the process of local searching, the ACOC applied the Hooke-Jeeves algorithm to improve the performance of optimization algorithms and improve the convergence speed. The simulation results show that the ACOC method is very effective and adaptive widely. The ideal gasoline recipe can be found quickly by the proposed method, and get the max profit with little quality index margin. What's more, the proposed method broads the application scope of ant colony algorithm.Finally, this paper introduces the near infrared analyzer (INR), and proposed the solution method of the online pipeline oil blending.
本文通过对国内和国外油品调和技术的分析,首先建立了一种新的油品调和优化配方模型,该模型解决了调和优化模型与实际生产过程的不匹配问题,具有较高精确度；然后提出了一种改进的具有交叉算子的蚁群算法,该算法将遗传算法中的交叉算子引入蚁群算法中,加强了蚁群算法的全局搜索能力。在局部搜索过程中,应用Hooke-Jeeves方法改善了算法的寻优性能,加快了算法的收敛速率。仿真结果表明,新算法能够获得非常理想的配方模型、保证对质量指标的卡边要求、获得最大的调和利润。最后,为实现油品调和的实时控制,提出了在线调和解决方案。该方案利用近红外分析仪对油品的质量指标进行实时监测,然后将测得的调和组分指标传送给计算机,实现了对油品配方模型的在线调整,得到了能够实时调整的调和配方。
The gasoline blending process is very important for refineries, and the blending recipe determined the final profits. The blending recipe directly affects the gasoline pass rate, and the blending process is a typical nonlinear constrained function optimization problem. Thus, to get a satisfactory gasoline recipe is the difficulties link and key process for refinery researchers.
It is difficult to obtain satisfying optimum solution by traditional methods. According to this problem, an improved ant colony algorithm, called Ant Colony Optimization algorithm with Crossover operator (ACOC), was presented. The proposed algorithm introduced crossover operator into the ant colony algorithm, and improved the global search ability. In the process of local searching, the ACOC applied the Hooke-Jeeves algorithm to improve the performance of optimization algorithms and improve the convergence speed. The simulation results show that the ACOC method is very effective and adaptive widely. The ideal gasoline recipe can be found quickly by the proposed method, and get the max profit with little quality index margin. What's more, the proposed method broads the application scope of ant colony algorithm.Finally, this paper introduces the near infrared analyzer (INR), and proposed the solution method of the online pipeline oil blending.
引文
[1]Glismann K, G Gruhn. Short-term scheduling and recipe optimization of blending processes [J].Chemical Engineering,2001,25(4-6),627-634.
[2]Jia Z, M Ierapetritou. Mixed-Integer Linear Programming Model for Gasoline Blending and Distribution Scheduling [J].Industrial and Engineering Chemistry Research, 2003,42(4):825-835.
[3]迟天运.汽油调和非线性模型求解及在线调和的研究[D].大连：大连理工大学,2007.
[4]李响.油品调和优化问题的研究[D].大连：大连理工大学,2010.
[5]唐立新.CIMS下多品种小批量主生产计划的可视化系统[J].系统工程理论与实践,1999,19(1)：120-124.
[6]黄彩凤,李信,李少远.基于Ethyl RT-70模型的汽油调和优化[J].控制工程,2007,14(3)：256-259.
[7]Singh A, et al. Model-based real-time optimization of automotive gasoline blending operations [J]. Journal of Process Control,2000,10(1):43-58.
[8]J L Deneubourg, J M Pasteels. The self-organizing exploratory pattern of the argent ant [J]. Journal of Insect Behavior,1990,3(2):159-168.
[9]Dorigo M, Gambardella. Ant colonies for the travelling salesman problem [J]. Bio-Systems,1997,43(2):73-81.
[10]A M Dorigo, V Maniezzo. Distributed optimization by ant colonies [J]. BioSystems, 1991,14(5):36-57.
[11]Dorigo M, L M Gambardella. Ant colony system:A cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation,1997,1(1):53-56.
[12]V M, C A. An ANTS heuristic for the frequency assignment problem [J]. Future Generation Computer Systems,2000,16(18):927-935.
[13]Blum C, Roli A. The hyper-cube framework for ant colony optimization [C]. Proceedings of MIC 2001--etaheuristics International Conference,2001.
[14]Dorigo M, V Maniezzo. Ant system:optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems,1996,26(1):29-41.
[15]B B, H R F. A new rank-based version of ant system:a computational study [J]. Central European Journal for operations Research and Economics,1999,7(1):7-16.
[16]J., L. Z., L. C. Y., S. F. An immunity ant colony optimization algorithms for solving weapon assignment problem [J]. Applied Son Computing Journal,2002,2(1):39-47.
[17]C, F., L. F., M. F. Chaotic sequences in ACO algorithms [C]. Proceedings of the 2004 International Symposium on Circuits and Systems,2004.
[18]T, W., P. B. Improving the ant system by integration with genetic [C]. Proceedings of the 3rd Annual Genetic Programming conference,1998.
[19]B,O., R. A. M. A hvbrid of particle swam and ant colonv optimization algorithms for-reactive power market simulation [J]. Intelligent and Fuzzy Systems,2006, 17(6):557-574.
[20]孔民.蚁群优化算法及其应用[D].上海：上海交通大学,2007.
[21]段海滨.一种求解连续空间优化问题的改进蚁群算法[J].系统仿真学报,2007,(05)：974-977.
[22]杨勇.蚁群算法求解连续空间优化问题[J].控制与决策,2003,8(05)：573-576.
[23]陈德钊,贺益军.连续约束蚁群优化算法的构建及其在丁烯烷化过程中的应用[J].化工学报,2005,1(9)：1708-1713.
[24]Beckers, R., et al. Colony size, communication, and ant foraging strategy[J]. Journal of Computational and Applied Mathematics,96(3-4):1989,239-256.
[25]McCall, J. Genetic algorithms for modeling and optimization [J]. Journal of Computational and Applied Mathematics,2005,96(3-4):205-222.
[26]Yu song. Genetic-combinatorial algorithm of 0-1 programming [J]. Institute of Electrical and Electronics Engineers,2003,6(11):698-701.
[27]陈宝林.最优化理论与算法(第2版)[M].北京：清华大学出版社,2009.
[28]邢文训,谢金星.现代优化计算方法(第2版)[M].北京：清华大学出版社,2005.
[29]张建明,冯建华.两群微粒群算法及其在油品调和优化中的应用[J].2008,59(7)：1721-1726
[30]严衍禄.近红外光谱分析基础与应用[M].北京：中国轻工业出版社,2005.
[31]钱伯章.在线近红外分析仪在多组分汽油调和中的应用[J].炼油化工自动化,1996,3(21)：49-53.
[32]Twu C H. Predict octane numbers using a generalized interaction method [J]. Hydrocarbon Processing,1996,75(2):51-56.
[33]孙根旺,赵小强.汽油在线优化调合控制模型及应用[J].石油炼制与化工学报,1999,3(11)：33-36.
[34]杨名滨.测定汽油规格的良种智能在线分析仪[J].化工自动化及仪表,2000,20(4)：49-53.
[35]陆婉珍,袁洪福.现代近红外光谱分析技术[M].北京：中国石化出版社,2001.
[2]Jia Z, M Ierapetritou. Mixed-Integer Linear Programming Model for Gasoline Blending and Distribution Scheduling [J].Industrial and Engineering Chemistry Research, 2003,42(4):825-835.
[3]迟天运.汽油调和非线性模型求解及在线调和的研究[D].大连：大连理工大学,2007.
[4]李响.油品调和优化问题的研究[D].大连：大连理工大学,2010.
[5]唐立新.CIMS下多品种小批量主生产计划的可视化系统[J].系统工程理论与实践,1999,19(1)：120-124.
[6]黄彩凤,李信,李少远.基于Ethyl RT-70模型的汽油调和优化[J].控制工程,2007,14(3)：256-259.
[7]Singh A, et al. Model-based real-time optimization of automotive gasoline blending operations [J]. Journal of Process Control,2000,10(1):43-58.
[8]J L Deneubourg, J M Pasteels. The self-organizing exploratory pattern of the argent ant [J]. Journal of Insect Behavior,1990,3(2):159-168.
[9]Dorigo M, Gambardella. Ant colonies for the travelling salesman problem [J]. Bio-Systems,1997,43(2):73-81.
[10]A M Dorigo, V Maniezzo. Distributed optimization by ant colonies [J]. BioSystems, 1991,14(5):36-57.
[11]Dorigo M, L M Gambardella. Ant colony system:A cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation,1997,1(1):53-56.
[12]V M, C A. An ANTS heuristic for the frequency assignment problem [J]. Future Generation Computer Systems,2000,16(18):927-935.
[13]Blum C, Roli A. The hyper-cube framework for ant colony optimization [C]. Proceedings of MIC 2001--etaheuristics International Conference,2001.
[14]Dorigo M, V Maniezzo. Ant system:optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems,1996,26(1):29-41.
[15]B B, H R F. A new rank-based version of ant system:a computational study [J]. Central European Journal for operations Research and Economics,1999,7(1):7-16.
[16]J., L. Z., L. C. Y., S. F. An immunity ant colony optimization algorithms for solving weapon assignment problem [J]. Applied Son Computing Journal,2002,2(1):39-47.
[17]C, F., L. F., M. F. Chaotic sequences in ACO algorithms [C]. Proceedings of the 2004 International Symposium on Circuits and Systems,2004.
[18]T, W., P. B. Improving the ant system by integration with genetic [C]. Proceedings of the 3rd Annual Genetic Programming conference,1998.
[19]B,O., R. A. M. A hvbrid of particle swam and ant colonv optimization algorithms for-reactive power market simulation [J]. Intelligent and Fuzzy Systems,2006, 17(6):557-574.
[20]孔民.蚁群优化算法及其应用[D].上海：上海交通大学,2007.
[21]段海滨.一种求解连续空间优化问题的改进蚁群算法[J].系统仿真学报,2007,(05)：974-977.
[22]杨勇.蚁群算法求解连续空间优化问题[J].控制与决策,2003,8(05)：573-576.
[23]陈德钊,贺益军.连续约束蚁群优化算法的构建及其在丁烯烷化过程中的应用[J].化工学报,2005,1(9)：1708-1713.
[24]Beckers, R., et al. Colony size, communication, and ant foraging strategy[J]. Journal of Computational and Applied Mathematics,96(3-4):1989,239-256.
[25]McCall, J. Genetic algorithms for modeling and optimization [J]. Journal of Computational and Applied Mathematics,2005,96(3-4):205-222.
[26]Yu song. Genetic-combinatorial algorithm of 0-1 programming [J]. Institute of Electrical and Electronics Engineers,2003,6(11):698-701.
[27]陈宝林.最优化理论与算法(第2版)[M].北京：清华大学出版社,2009.
[28]邢文训,谢金星.现代优化计算方法(第2版)[M].北京：清华大学出版社,2005.
[29]张建明,冯建华.两群微粒群算法及其在油品调和优化中的应用[J].2008,59(7)：1721-1726
[30]严衍禄.近红外光谱分析基础与应用[M].北京：中国轻工业出版社,2005.
[31]钱伯章.在线近红外分析仪在多组分汽油调和中的应用[J].炼油化工自动化,1996,3(21)：49-53.
[32]Twu C H. Predict octane numbers using a generalized interaction method [J]. Hydrocarbon Processing,1996,75(2):51-56.
[33]孙根旺,赵小强.汽油在线优化调合控制模型及应用[J].石油炼制与化工学报,1999,3(11)：33-36.
[34]杨名滨.测定汽油规格的良种智能在线分析仪[J].化工自动化及仪表,2000,20(4)：49-53.
[35]陆婉珍,袁洪福.现代近红外光谱分析技术[M].北京：中国石化出版社,2001.