常压塔的稳态模拟及多目标优化研究
|
摘要
作为石油炼制加工的“龙头”，常减压蒸馏装置效益的提高对本装置、下游的二次和三次加工装置以及整个石化企业的效益有着十分重要的意义。企业的效益会受到装置运行状况的影响，而石化装置的稳态模拟能够为工艺改造提供依据，石化装置的操作优化能够为提升现有装置的效益提供方向，因此研究常压塔的稳态模拟及多目标优化研究意义重大。本文的主要内容如下：
首先采用首先采用石油物系特征化用一系列假组分和确定的纯组分来表示原油，然后对各平衡级建立各特征化组分MESH方程，最后采用有效的求解策略求解非线性MESH大型方程组，并辅之以行之有效的校正策略，获得塔内各个重要工艺参数的分布，以及各个侧线产品、中段循环、塔顶油气产品的各项性质和状态。为了验证该模型的正确性，在流程模拟软件Aspen Plus上搭建同样的的稳态模型，并给出稳态模拟的结果和分析。
其次在研究了优化算法中的GA、SA，结合GA和SA的优势和劣势的SAGA的基础上，针对自适应模拟退火遗传算法的弱点，提出引入跳跃基因算子的多目标模拟退火遗传算法，形成了改进后的ASAGA-JG算法。通过多种多目标算法性能度量指标和测试函数对改进的算法进行全方位的考察，验证ASAGA-JG的性能。
最后将ASAGA-JG算法应用到常压塔装置的优化上，选取了适当的决策变量，优化目标为最大化利润和最小化能耗，约束条件为装置产品的质量指标和产品馏出率，在设置ASAGA-JG的算法参数之后，实现了基于ASAGA-JG的常压塔多目标优化。仿真结果表明常压塔装置当前的工况不是最优的，通过优化装置可以获得更高的利润和更小的能耗。该方法可靠高效，具备相当的应用价值。
As the key unit in refinery fields, crude oil distillation unit (CDU) is thebasement of refinery enterprises. Its improvance in efficiency has huge impacton the efficiency of units downstream and the total profit of the entireenterprise. As we know, the profit of the entire enterprise is affected by therunning status of the units; in another word, it is affected by the stability andreliability of the control strategy. Aiming at enery saving, profit improving,and process optimization, the research of steady-state simulation andmulti-objective optimization of CDU has great meaning. The main work canbe summaried as follows：
First of all, the author made a brief analysis on steady-state simulationand multi-objective optimization. And then, with rigid mechanism, the authorbuilded a steady-state model of CDU. For comparison with flow simulationsoftwares, the author also builded a steady-state model of the CDU with AspenPlus.
Secondly, after the detailed research of simulated annealing algorithm,genetic algorithm, and simulated annealing genetic algorithm, and kept the advances and weaknesses of those algorithms in mind, proposed jumpinggenes adaption of adaptive simulated annealing genetic algorithm (ASAGA).And with many proven metrics and test problems, the author had tested theperformance of jumping genes adaption of ASAGA, the results showed thatjumping genes adaption of ASAGA is much better than ASAGA, and on sometest problems, it even better than NSGA-II and RJGGA.
Thirdly, based on the rigid mechanism simulation of CDU, the authorselected the right objectives from the view of enterprise, suitable decisionvariables, and constrains. After finish constructing of multi-objective model,the new jumping genes adaption of ASAGA was applied into themulti-ogbjective optimization of CDU. It is observed that current plantoperation is sub-optimal and more profit can be realized for the same energycost using the obtained optimal operating conditions, which are under theconstraints of product quality and total distillate. The simulation resultsdemonstrate that the ASAGA-JG is able to generate non-dominated solutionswith a wide spread along the Pareto-optimal front and good address the issuesregarding convergence and diversity in multi-objective optimization.
首先采用首先采用石油物系特征化用一系列假组分和确定的纯组分来表示原油，然后对各平衡级建立各特征化组分MESH方程，最后采用有效的求解策略求解非线性MESH大型方程组，并辅之以行之有效的校正策略，获得塔内各个重要工艺参数的分布，以及各个侧线产品、中段循环、塔顶油气产品的各项性质和状态。为了验证该模型的正确性，在流程模拟软件Aspen Plus上搭建同样的的稳态模型，并给出稳态模拟的结果和分析。
其次在研究了优化算法中的GA、SA，结合GA和SA的优势和劣势的SAGA的基础上，针对自适应模拟退火遗传算法的弱点，提出引入跳跃基因算子的多目标模拟退火遗传算法，形成了改进后的ASAGA-JG算法。通过多种多目标算法性能度量指标和测试函数对改进的算法进行全方位的考察，验证ASAGA-JG的性能。
最后将ASAGA-JG算法应用到常压塔装置的优化上，选取了适当的决策变量，优化目标为最大化利润和最小化能耗，约束条件为装置产品的质量指标和产品馏出率，在设置ASAGA-JG的算法参数之后，实现了基于ASAGA-JG的常压塔多目标优化。仿真结果表明常压塔装置当前的工况不是最优的，通过优化装置可以获得更高的利润和更小的能耗。该方法可靠高效，具备相当的应用价值。
As the key unit in refinery fields, crude oil distillation unit (CDU) is thebasement of refinery enterprises. Its improvance in efficiency has huge impacton the efficiency of units downstream and the total profit of the entireenterprise. As we know, the profit of the entire enterprise is affected by therunning status of the units; in another word, it is affected by the stability andreliability of the control strategy. Aiming at enery saving, profit improving,and process optimization, the research of steady-state simulation andmulti-objective optimization of CDU has great meaning. The main work canbe summaried as follows：
First of all, the author made a brief analysis on steady-state simulationand multi-objective optimization. And then, with rigid mechanism, the authorbuilded a steady-state model of CDU. For comparison with flow simulationsoftwares, the author also builded a steady-state model of the CDU with AspenPlus.
Secondly, after the detailed research of simulated annealing algorithm,genetic algorithm, and simulated annealing genetic algorithm, and kept the advances and weaknesses of those algorithms in mind, proposed jumpinggenes adaption of adaptive simulated annealing genetic algorithm (ASAGA).And with many proven metrics and test problems, the author had tested theperformance of jumping genes adaption of ASAGA, the results showed thatjumping genes adaption of ASAGA is much better than ASAGA, and on sometest problems, it even better than NSGA-II and RJGGA.
Thirdly, based on the rigid mechanism simulation of CDU, the authorselected the right objectives from the view of enterprise, suitable decisionvariables, and constrains. After finish constructing of multi-objective model,the new jumping genes adaption of ASAGA was applied into themulti-ogbjective optimization of CDU. It is observed that current plantoperation is sub-optimal and more profit can be realized for the same energycost using the obtained optimal operating conditions, which are under theconstraints of product quality and total distillate. The simulation resultsdemonstrate that the ASAGA-JG is able to generate non-dominated solutionswith a wide spread along the Pareto-optimal front and good address the issuesregarding convergence and diversity in multi-objective optimization.
引文
[1]李奎武，汤景凝.炼油厂常减压蒸馏装置流程模拟系统的开发[J].石油炼制与化工，1995，26(4)：36-37
[2] M Bagajewicz，S Ji. Rigorous procedure for the design of conventional atmospheric crudefractionation units. Part I: Targeting[J]. Ind. Eng. Chem. Res.，2001，40：617–626
[3]李向阳.常减压流程装置模拟与操作优化[D].上海：华东理工大学，2010
[4]于晓栋，吕文祥，黄得先，金以惠.基于HYSYS和NSGA-II的常压塔多目标优化[J].化工学报，2008，59(7)：1646-1649
[5] C.M. Fonseca, P.J. Fleming, Multi-objective genetic algorithm made easy: selection, sharing andmating restriction, in: Proceedings of the International Conference on Genetic Algorithm inEngineering Systems: Innovations and Application, UK,1995, pp.12–14
[6] Suppapitnarm A, Seffen K. A. Simulated annealing: An alternative approach to truemultiobjective optimization[J]. Engineering Optimization,2000,33:59
[7] Deb, K., Pratap, A., Agarwal, S.,&Meyarivan, T.(2002). A fast and elitist multiobjective geneticalgorithm: NSGA-II. IEEE Transactions on Evolutionary Computation6,182
[8] J. Horn, N. Nafpliotis, D.E. Goldberg, A niched Pareto genetic algorithm for multiobjectiveoptimization, in: Proceedings of the First IEEE International Conference on EvolutionaryComputation, Piscataway, NJ,1994, pp.82–87
[9] J. Knowles, D. Corne, Approximating the non-dominated front using the Pareto archievedevolution strategy, Evolutionary Computation Journal8(20)(2000)149–172
[10]黄文.原油蒸馏系统的建模仿真和操作优化研究[D].北京：北京化工大学，2011
[11]李峰．常减压装置的流程模拟和优化操作研究[D].上海：华东理工大学，2004
[12]张程.基于Aspen Plus的常减压装置过程模拟与换热网络优化[D].北京：中国石油大学，2011
[13] E.W.Thiele，R.L.Sullivan.Computation of Distillation Apparatus for Hydrocarbon Mixture[J].I&ec，1983，25(3)：289-295
[14] Bonner,J,S.. Am,Petrol.Inst,1956,26(238)
[15] Lyster,W.N., etal. Figure distillation this new way. Hydrocarbon Processing&Petrol.Refiner,1959,38(6):221
[16] R.P.Goldstein，R.B.Standfield.Flexible Method for the Solution of Distillation Design ProblemsUsing the Newton Raphson Technique[J].Ind.Eng.Chem.Process Des.Dev，1980，9(1)：78-84
[17] Sujata.A.D. Absorber-strpiper calculations made easier[J]. Hydrocarbon processing&Petrol.Refiner，1961，40(12)：137
[18] Wang.J.C.， Henke.G.E. Tridiagonal matrix for distillation[J]. ACM，1973，16(12)：760
[19] Amundson.N.R.， Pontinen.A.J. Multi-component distillation on large digital computer[J]. Ind.&Eng.Chem，1958，(5)：450
[20]肖红丽.流程模拟技术在常减压蒸馏中的应用[D].北京：北京化工大学，2010
[21]韦士平.生产装置调优与节能[M].北京：中石化出版社，1992
[22]丁泉，朱宝良，齐旭东，周立岩.用逐步回归分析法预测不同原油最佳掺炼比[J].炼油设计，1995，25(1)：50-52
[23]徐青耀.应用模式识别技术对常减压装置进行系统优化[J].金山油化纤，1995，(2)：20-23.
[24] S. V. Inamdar，S.K. Gupta， D. N. Saraf. Multi-objective optimization of an industrial crudedistillation unit using elitist nondominated sorting genetic algorithm [J]. Chemical EngineeringResearch and Design，2004，82:611–623
[25] J. W. Seo，M. Oh，T. H. Lee.Design Optimization of a Crude Oil DistillationProcess[J].Chem.Eng.Technol，2008，23(2)：157-163
[26]张亚乐，徐博文，方崇智，康飚.一种改进的遗传算法在原油蒸馏过程优化中的应用[J].化工自动化仪表，1997，23(3)：12-17
[27] Raja Kumar More，Vijaya Kumar Bulasara，Ramgopal Uppaluri. Optimization of crudedistillation system using aspen plus: Effect of binary feed selection on grass-root design[J].IChemE，2010，(88)：121-134
[28]王鲁.基于遗传算法的多目标优化算法研究[D].武汉：武汉理工大学，2006
[29] E.Zitzler. Evolutionary Algorithms for multiobjective optimization：Methods and Application.Ph.D. Thesis，Swiss Federal Institute of Technology(ETH)，Zurich，Switzerland，November1999
[30]高媛.非支配排序遗传算法(NSGA)的研究与应用[D].杭州：浙江大学，2006
[31] Khoa Duc Tran.An Improved Multi-Objective Evolutionary Algorithm with AdaptableParameters. Ph.D，Nova Southeastern University，August2006
[32]阮宏博.基于遗传算法的工程多目标优化研究[D].大连：大连理工大学，2007
[33] J.Miquel，F. Castelees. Easy Characterization of Petroleum Fractions. Hydrocarbon Processing.1994，(1)：99-133
[34]楚纪正，张玉梅，胡上序.满足精馏塔实时仿真需要的石油馏分物性简化关联[J].炼油设计，1997，27(1)：51-55
[35]候会峰.原油常减压蒸馏装置的模拟与优化.[D].新疆：新疆大学，2010
[36] Kirkpatrick. S.， Gelatt Jr.，C.D.，Vecchi M. P. Optimization by simulated annealing[J].Science，1983，(220)：671
[37]徐刚.基于模拟退火遗传算法的管网优化设计方法的研究[D].成都：西南交通大学，2005.
[38] Holland. J. Adaptation in Natural and Artificial Systems [M]. University of Michigan Press, AnnArbor, MI,1975；MIT Press, Cambridge, MA,1992
[39]韩瑞峰.遗传算法原理与应用实例[M].北京：兵器工业出版社，2009
[40] K. Deb，H. Beyer.Self-adaptive genetic algorithms with simulated binarycrossover[J].Evolutionary Computation，2001，9(2)：197–221
[41]王慧琳.模拟退火遗传算法优化的BP网络在翘曲量预测中的应用[D].杭州：浙江大学，2011
[42] Sun Hui. Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulatedannealing genetic algorithm[J]. Engineering Applications of Artificial Intelligence，2010，23：27-33
[43] Rahul B Kasat，Santosh K. Gupta. Multi-objective optimization of an industrial fluidized-bedcatalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator [J].Computers and Chemical Engineering，2003，27：1785–1800
[44] McClintock, Barbara (1950)"The origin and behavior of mutable loci in maize". Proceedings ofthe National Academy of Sciences.36：344–55
[45] Chandan Guria， Prashant K. Bhattachary， Santosh K. Gupta. Multi-objective optimization ofreverse osmosis desalination units using different adaptations of the non-dominated sortinggenetic algorithm (NSGA)[J]. Computers and Chemical Engineering29(2005)1977–1995.
[46] Chandan Guria， Prashant K. Bhattachary， Santosh K. Gupta. Multi-objective optimization ofreverse osmosis desalination units using different adaptations of the non-dominated sortinggenetic algorithm (NSGA)[J]. Computers and Chemical Engineering29(2005)1977–1995.
[47] Kazi Shah Nawaz Ripon， Sam Kwong， K.F. Man. A real-coding jumping gene geneticalgorithm (RJGGA) for multiobjective optimization [J]. Information Sciences177(2007)632–654.
[48] Aaditya Agarwal， Santosh K. Gupta. Jumping gene adaptations of NSGA-II and their use in themulti-objective optimal design of shell and tube heat exchangers [J]. IChemE86(2008)123-139.
[49] K.C. Tan，S.C. Chiam，A.A. Mamun，C.K. Goh. Balancing exploration and exploitation withadaptive variation for evolutionary multi-objective optimization[J]. European Journal ofOperational Research，2009，197:701-713.
[50] Suppapitnarm A, Seffen K. A. Simulated annealing: An alternative approach to truemultiobjective optimization[J]. Engineering Optimization,2000,33:59.
[2] M Bagajewicz，S Ji. Rigorous procedure for the design of conventional atmospheric crudefractionation units. Part I: Targeting[J]. Ind. Eng. Chem. Res.，2001，40：617–626
[3]李向阳.常减压流程装置模拟与操作优化[D].上海：华东理工大学，2010
[4]于晓栋，吕文祥，黄得先，金以惠.基于HYSYS和NSGA-II的常压塔多目标优化[J].化工学报，2008，59(7)：1646-1649
[5] C.M. Fonseca, P.J. Fleming, Multi-objective genetic algorithm made easy: selection, sharing andmating restriction, in: Proceedings of the International Conference on Genetic Algorithm inEngineering Systems: Innovations and Application, UK,1995, pp.12–14
[6] Suppapitnarm A, Seffen K. A. Simulated annealing: An alternative approach to truemultiobjective optimization[J]. Engineering Optimization,2000,33:59
[7] Deb, K., Pratap, A., Agarwal, S.,&Meyarivan, T.(2002). A fast and elitist multiobjective geneticalgorithm: NSGA-II. IEEE Transactions on Evolutionary Computation6,182
[8] J. Horn, N. Nafpliotis, D.E. Goldberg, A niched Pareto genetic algorithm for multiobjectiveoptimization, in: Proceedings of the First IEEE International Conference on EvolutionaryComputation, Piscataway, NJ,1994, pp.82–87
[9] J. Knowles, D. Corne, Approximating the non-dominated front using the Pareto archievedevolution strategy, Evolutionary Computation Journal8(20)(2000)149–172
[10]黄文.原油蒸馏系统的建模仿真和操作优化研究[D].北京：北京化工大学，2011
[11]李峰．常减压装置的流程模拟和优化操作研究[D].上海：华东理工大学，2004
[12]张程.基于Aspen Plus的常减压装置过程模拟与换热网络优化[D].北京：中国石油大学，2011
[13] E.W.Thiele，R.L.Sullivan.Computation of Distillation Apparatus for Hydrocarbon Mixture[J].I&ec，1983，25(3)：289-295
[14] Bonner,J,S.. Am,Petrol.Inst,1956,26(238)
[15] Lyster,W.N., etal. Figure distillation this new way. Hydrocarbon Processing&Petrol.Refiner,1959,38(6):221
[16] R.P.Goldstein，R.B.Standfield.Flexible Method for the Solution of Distillation Design ProblemsUsing the Newton Raphson Technique[J].Ind.Eng.Chem.Process Des.Dev，1980，9(1)：78-84
[17] Sujata.A.D. Absorber-strpiper calculations made easier[J]. Hydrocarbon processing&Petrol.Refiner，1961，40(12)：137
[18] Wang.J.C.， Henke.G.E. Tridiagonal matrix for distillation[J]. ACM，1973，16(12)：760
[19] Amundson.N.R.， Pontinen.A.J. Multi-component distillation on large digital computer[J]. Ind.&Eng.Chem，1958，(5)：450
[20]肖红丽.流程模拟技术在常减压蒸馏中的应用[D].北京：北京化工大学，2010
[21]韦士平.生产装置调优与节能[M].北京：中石化出版社，1992
[22]丁泉，朱宝良，齐旭东，周立岩.用逐步回归分析法预测不同原油最佳掺炼比[J].炼油设计，1995，25(1)：50-52
[23]徐青耀.应用模式识别技术对常减压装置进行系统优化[J].金山油化纤，1995，(2)：20-23.
[24] S. V. Inamdar，S.K. Gupta， D. N. Saraf. Multi-objective optimization of an industrial crudedistillation unit using elitist nondominated sorting genetic algorithm [J]. Chemical EngineeringResearch and Design，2004，82:611–623
[25] J. W. Seo，M. Oh，T. H. Lee.Design Optimization of a Crude Oil DistillationProcess[J].Chem.Eng.Technol，2008，23(2)：157-163
[26]张亚乐，徐博文，方崇智，康飚.一种改进的遗传算法在原油蒸馏过程优化中的应用[J].化工自动化仪表，1997，23(3)：12-17
[27] Raja Kumar More，Vijaya Kumar Bulasara，Ramgopal Uppaluri. Optimization of crudedistillation system using aspen plus: Effect of binary feed selection on grass-root design[J].IChemE，2010，(88)：121-134
[28]王鲁.基于遗传算法的多目标优化算法研究[D].武汉：武汉理工大学，2006
[29] E.Zitzler. Evolutionary Algorithms for multiobjective optimization：Methods and Application.Ph.D. Thesis，Swiss Federal Institute of Technology(ETH)，Zurich，Switzerland，November1999
[30]高媛.非支配排序遗传算法(NSGA)的研究与应用[D].杭州：浙江大学，2006
[31] Khoa Duc Tran.An Improved Multi-Objective Evolutionary Algorithm with AdaptableParameters. Ph.D，Nova Southeastern University，August2006
[32]阮宏博.基于遗传算法的工程多目标优化研究[D].大连：大连理工大学，2007
[33] J.Miquel，F. Castelees. Easy Characterization of Petroleum Fractions. Hydrocarbon Processing.1994，(1)：99-133
[34]楚纪正，张玉梅，胡上序.满足精馏塔实时仿真需要的石油馏分物性简化关联[J].炼油设计，1997，27(1)：51-55
[35]候会峰.原油常减压蒸馏装置的模拟与优化.[D].新疆：新疆大学，2010
[36] Kirkpatrick. S.， Gelatt Jr.，C.D.，Vecchi M. P. Optimization by simulated annealing[J].Science，1983，(220)：671
[37]徐刚.基于模拟退火遗传算法的管网优化设计方法的研究[D].成都：西南交通大学，2005.
[38] Holland. J. Adaptation in Natural and Artificial Systems [M]. University of Michigan Press, AnnArbor, MI,1975；MIT Press, Cambridge, MA,1992
[39]韩瑞峰.遗传算法原理与应用实例[M].北京：兵器工业出版社，2009
[40] K. Deb，H. Beyer.Self-adaptive genetic algorithms with simulated binarycrossover[J].Evolutionary Computation，2001，9(2)：197–221
[41]王慧琳.模拟退火遗传算法优化的BP网络在翘曲量预测中的应用[D].杭州：浙江大学，2011
[42] Sun Hui. Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulatedannealing genetic algorithm[J]. Engineering Applications of Artificial Intelligence，2010，23：27-33
[43] Rahul B Kasat，Santosh K. Gupta. Multi-objective optimization of an industrial fluidized-bedcatalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator [J].Computers and Chemical Engineering，2003，27：1785–1800
[44] McClintock, Barbara (1950)"The origin and behavior of mutable loci in maize". Proceedings ofthe National Academy of Sciences.36：344–55
[45] Chandan Guria， Prashant K. Bhattachary， Santosh K. Gupta. Multi-objective optimization ofreverse osmosis desalination units using different adaptations of the non-dominated sortinggenetic algorithm (NSGA)[J]. Computers and Chemical Engineering29(2005)1977–1995.
[46] Chandan Guria， Prashant K. Bhattachary， Santosh K. Gupta. Multi-objective optimization ofreverse osmosis desalination units using different adaptations of the non-dominated sortinggenetic algorithm (NSGA)[J]. Computers and Chemical Engineering29(2005)1977–1995.
[47] Kazi Shah Nawaz Ripon， Sam Kwong， K.F. Man. A real-coding jumping gene geneticalgorithm (RJGGA) for multiobjective optimization [J]. Information Sciences177(2007)632–654.
[48] Aaditya Agarwal， Santosh K. Gupta. Jumping gene adaptations of NSGA-II and their use in themulti-objective optimal design of shell and tube heat exchangers [J]. IChemE86(2008)123-139.
[49] K.C. Tan，S.C. Chiam，A.A. Mamun，C.K. Goh. Balancing exploration and exploitation withadaptive variation for evolutionary multi-objective optimization[J]. European Journal ofOperational Research，2009，197:701-713.
[50] Suppapitnarm A, Seffen K. A. Simulated annealing: An alternative approach to truemultiobjective optimization[J]. Engineering Optimization,2000,33:59.