乙烯裂解炉的模拟和优化方法研究
|
摘要
乙烯是最重要的单体之一,是整个石油化工业的基础。在乙烯生产中裂解炉的操作水平和技术水平直接影响着乙烯厂的经济效益。近年来丙烯的产量已经成为乙烯装置除乙烯产量外所需达到的第二个目标。因此对乙烯裂解炉进行准确的模拟,并实施以乙烯和丙烯产量为目标的操作优化具有十分重要的意义。
目前石脑油裂解过程的一次反应选择性系数z缺乏有效的估计方法,阻碍了模拟优化方法在乙烯生产中的应用。本文首先提出了一种考虑二次反应影响的z的推算方法。该方法可以根据实测的裂解产物分布计算出裂解原料油的z,建立具有若干样本的标准数据库。而后提出了一种模糊匹配法来预测新原料油的z。实例证明此模糊匹配法可以较准确地估计石脑油的一次反应选择性系数。
本文建立了全周期生产操作优化模型,并采用了文中提出的改进的模拟退火法、遗传算法和逐次二次规划法(SQP)分别进行了优化计算。对三种方法的效率对比表明,逐次二次规划法的效率最高,在计算速度上具有明显的优势。证明了对于复杂的工业过程,基于梯度的二次规划法仍然是更有效的方法。
本文首次提出了一种并行的混合多目标遗传算法。该算法结合了多目标进化算法和线性加权法的优点,有效地提高了多目标算法的效率,并使得非劣解集的广泛性得到了明显的改善。这种多目标优化方法对解决复杂的非线性约束问题效率较高,所以可以推广到其他领域的多目标优化问题。
根据实际的生产需求,建立了乙烯裂解炉的乙、丙烯多目标优化模型,并用新的混合算法和NSGA-II法分别解得了非劣解集(Pareto解集)。算法效率的比较再次证明了新的混合算法具有较高的效率并且明显地改善了解集的广泛性。所得到的非劣解集为乙烯厂提供了可供选择的多种优化操作方案。
基于以上研究所开发的乙烯裂解炉模拟优化系统(EPSOS),已在中国石油兰州石化投入应用。长期的工业试验表明,此软件不仅可以较准确地预测原料油的产物分布,并且可以对全周期操作进行优化计算,得到同时考虑提高乙烯和丙烯产率的优化操作方案。本研究参与的项目已通过了中国石油组织的验收,验收充分肯定了EPSOS对提高乙烯裂解炉的操作水平及经济效益的重要作用。
Ethylene, one of the most important monomers produced worldwide, is the essential of the entire petrochemical industry. The ethylene cracking furnace is the most important equipment in the production of ethylene. The level of operation and technique of the ethylene cracking furnace can directly affect the economic benefit of the whole ethylene company. Recently, the demand of propylene has increased dramatically and its increase ratio has exceeded that of ethylene, which has broken the supply-demand structure whose centre is only ethylene yield. So the yield of propylene has become the second objective to be maximized apart from ethylene yield. Thus, the accurate simulation of ethylene cracking furnaces and the multi-objective optimization for both ethylene and propylene are very necessary.
At present, the lack of the effective method to estimate the selectivities of the first-order reaction (z) of the naphtha pyrolysis process has cumbered the application of simulation and optimization in the industrial production of ethylene. This thesis first proposed a regression method considering the second-order reactions for estimating z. The method can be used for obtaining the z of different kinds of naphtha based on the product yields from plant analytical laboratory, and then a standard database of z containing some standard samples can be established. For estimating z of the new naphtha by using the established database of z, a fuzzy matching method was proposed here. It has been shown that the yields calculated based on the estimated z are very close to the experimental ones, which demonstrates the effectiveness of the proposed method.
On the basis of the accelerating simulation strategy for the ethylene cracking furnace, the optimization model of periodic operation of the ethylene cracking furnace was developed. An advanced simulated annealing method proposed in this thesis(ASA), genetic algorithms(GA) and the sequential quadratic programming (SQP) were used to solve the optimization problem, respectively. A comparison of the above three methods shows that SQP is much more efficient, which can demonstrate that the gradient method SQP is still more effective to settle the complicated constrained optimal nonlinear problem.
After the analysis of the existing multi-objective optimization algorithms, a new parallel hybrid multi-objective optimization algorithm was proposed for the first time. The algorithm which using parallel NSGA-II (the non-dominated sorting genetic algorithm) module and SQP module combines the advantage of multi-objective evolutionary algorithms and the linear weighted method and improves the calculation efficiency evidently. The new algorithm is more efficient to settle the complicated nonlinear constrained problems and can be easily applied to other multi-objective optimization problems in industry.
Based on the demand of the industrial production, the multi-objective optimization model of ethylene cracking furnace was established, in which there are two objectives: the yield of ethylene and the yield of propylene. Then the proposed new hybrid method and NSGA-II were used to settle this problem and obtained the Pareto front respectively. A comparison of these two methods also demonstrates that the new method is more efficient to converge and captures more spectrum of the Pareto front than NSGA-II. The Pareto solutions provided multiple optimized operation alternatives for the ethylene plant.
An Ethylene Pyrolysis Simulation and Optimization System (EPSOS) were developed based on the above-mentioned research. EPSOS has been used in the ethylene plant of PetroChina Lanzhou Petrochemical Company. The long-term industrial experimental data shows that the software not only can forecast the products yields of different naphtha, but also can provide the optimized operation strategy for increasing ethylene and propylene yields simultaneously. The economic benefits obtained by implementing EPSOS in the ethylene plant were affirmed sufficiently.
目前石脑油裂解过程的一次反应选择性系数z缺乏有效的估计方法,阻碍了模拟优化方法在乙烯生产中的应用。本文首先提出了一种考虑二次反应影响的z的推算方法。该方法可以根据实测的裂解产物分布计算出裂解原料油的z,建立具有若干样本的标准数据库。而后提出了一种模糊匹配法来预测新原料油的z。实例证明此模糊匹配法可以较准确地估计石脑油的一次反应选择性系数。
本文建立了全周期生产操作优化模型,并采用了文中提出的改进的模拟退火法、遗传算法和逐次二次规划法(SQP)分别进行了优化计算。对三种方法的效率对比表明,逐次二次规划法的效率最高,在计算速度上具有明显的优势。证明了对于复杂的工业过程,基于梯度的二次规划法仍然是更有效的方法。
本文首次提出了一种并行的混合多目标遗传算法。该算法结合了多目标进化算法和线性加权法的优点,有效地提高了多目标算法的效率,并使得非劣解集的广泛性得到了明显的改善。这种多目标优化方法对解决复杂的非线性约束问题效率较高,所以可以推广到其他领域的多目标优化问题。
根据实际的生产需求,建立了乙烯裂解炉的乙、丙烯多目标优化模型,并用新的混合算法和NSGA-II法分别解得了非劣解集(Pareto解集)。算法效率的比较再次证明了新的混合算法具有较高的效率并且明显地改善了解集的广泛性。所得到的非劣解集为乙烯厂提供了可供选择的多种优化操作方案。
基于以上研究所开发的乙烯裂解炉模拟优化系统(EPSOS),已在中国石油兰州石化投入应用。长期的工业试验表明,此软件不仅可以较准确地预测原料油的产物分布,并且可以对全周期操作进行优化计算,得到同时考虑提高乙烯和丙烯产率的优化操作方案。本研究参与的项目已通过了中国石油组织的验收,验收充分肯定了EPSOS对提高乙烯裂解炉的操作水平及经济效益的重要作用。
Ethylene, one of the most important monomers produced worldwide, is the essential of the entire petrochemical industry. The ethylene cracking furnace is the most important equipment in the production of ethylene. The level of operation and technique of the ethylene cracking furnace can directly affect the economic benefit of the whole ethylene company. Recently, the demand of propylene has increased dramatically and its increase ratio has exceeded that of ethylene, which has broken the supply-demand structure whose centre is only ethylene yield. So the yield of propylene has become the second objective to be maximized apart from ethylene yield. Thus, the accurate simulation of ethylene cracking furnaces and the multi-objective optimization for both ethylene and propylene are very necessary.
At present, the lack of the effective method to estimate the selectivities of the first-order reaction (z) of the naphtha pyrolysis process has cumbered the application of simulation and optimization in the industrial production of ethylene. This thesis first proposed a regression method considering the second-order reactions for estimating z. The method can be used for obtaining the z of different kinds of naphtha based on the product yields from plant analytical laboratory, and then a standard database of z containing some standard samples can be established. For estimating z of the new naphtha by using the established database of z, a fuzzy matching method was proposed here. It has been shown that the yields calculated based on the estimated z are very close to the experimental ones, which demonstrates the effectiveness of the proposed method.
On the basis of the accelerating simulation strategy for the ethylene cracking furnace, the optimization model of periodic operation of the ethylene cracking furnace was developed. An advanced simulated annealing method proposed in this thesis(ASA), genetic algorithms(GA) and the sequential quadratic programming (SQP) were used to solve the optimization problem, respectively. A comparison of the above three methods shows that SQP is much more efficient, which can demonstrate that the gradient method SQP is still more effective to settle the complicated constrained optimal nonlinear problem.
After the analysis of the existing multi-objective optimization algorithms, a new parallel hybrid multi-objective optimization algorithm was proposed for the first time. The algorithm which using parallel NSGA-II (the non-dominated sorting genetic algorithm) module and SQP module combines the advantage of multi-objective evolutionary algorithms and the linear weighted method and improves the calculation efficiency evidently. The new algorithm is more efficient to settle the complicated nonlinear constrained problems and can be easily applied to other multi-objective optimization problems in industry.
Based on the demand of the industrial production, the multi-objective optimization model of ethylene cracking furnace was established, in which there are two objectives: the yield of ethylene and the yield of propylene. Then the proposed new hybrid method and NSGA-II were used to settle this problem and obtained the Pareto front respectively. A comparison of these two methods also demonstrates that the new method is more efficient to converge and captures more spectrum of the Pareto front than NSGA-II. The Pareto solutions provided multiple optimized operation alternatives for the ethylene plant.
An Ethylene Pyrolysis Simulation and Optimization System (EPSOS) were developed based on the above-mentioned research. EPSOS has been used in the ethylene plant of PetroChina Lanzhou Petrochemical Company. The long-term industrial experimental data shows that the software not only can forecast the products yields of different naphtha, but also can provide the optimized operation strategy for increasing ethylene and propylene yields simultaneously. The economic benefits obtained by implementing EPSOS in the ethylene plant were affirmed sufficiently.
引文
[1] Nakamura D N. Global ethylene capacity increases slightly in 2006. Oil & Gas Journal, 2007, 105(27):46-60.
[2]杨友麒.乙烯工厂的模拟、先进控制及实时优化.石油化工, 1999, 28(11):788-793.
[3]崔静怡.乙烯原料发展趋势及优化建议.石油化工技术经济, 2001, 17(6):13-16.
[4]王松汉.乙烯工艺与技术.北京:中国石化出版社, 2000.
[5]蓝兴英.乙烯管式裂解炉的数值模拟.过程工程学报, 2004, 4(3):221-227.
[6]郝红,熊国华,张少锋等.烃类裂解得丙烯产率计算.化学工程, 1999, 27(2):54-56.
[7]关国民,王宗祥.大庆石脑油裂解炉计算机控制模型.石油化工, 1990, 19(8):523-529.
[8]李玉刚,项曙光.SRT-III型柴油裂解炉的动态模型.石油化工, 1999, 28(10):700-703.
[9]张殿华.不同石脑油原料乙烯收率的估算与裂解炉调优方向.石油化工,1987, 16(6):426-429.
[10] Sundaram K M, Froment G F. Modeling of thermal cracking kinetics emdash 3. Radical mechanisms for the pyrolysis of simple paraffins, and their mixtures. Industrial & Engineering Chemistry, Fundamentals, 1978, 17(3):174-182.
[11] Schaerfe M, Ederer H J, Stable U,et al. Modeling of n-Hexane pyrolysis experimental investigations in a flow reactor at normal pressure. German Chemical Engineering, 1985, 8(2):119-129.
[12] Aribike D S, Susu A.A. Kinetics and mechenism of the thermal cracking of n-Heptane. Thermochim Acta, 1988, 127:247-258.
[13] Stabel U, Honig U F, Nowak U, et al. Thermal decomposition of 2,3-dimethyl butane. Experiments and mechanistic modelling. Chemical Engineering & Technology, 1990, 13(4):241-251.
[14] Chen Q, Froment G F. Thermal cracking of substituted aromatic hydrocarbons. I. Kinetic study of the thermal cracking of i-propylbenzene. Journal of Analytical and Applied Pyrolysis, 1991, 21(1-2):27.
[15] Chen Q, Froment GF. Thermal cracking of substituted aromatic hydrocarbons. II. Kinetic study of the thermal cracking of n-propylbenzene and ethylbenzene. Journal of Analytical and Applied Pyrolysis, 1991, 21(1-2):51.
[16] Aguilar E, Ortiz C H, Arzate E. Analysis shows revamp route to naphtha feed. Oil and Gas Journal, 1988, 86(47):48-51.
[17] Joo E, Park S, Lee M. Pyrolysis reaction mechanism for industrial naphtha cracking furnaces. Industrial and Engineering Chemistry Research, 2001, 40(11):2409-2415.
[18]邹仁鋆,邹晋. Study on the simultaneous pyrolysis of ethane-propane mixture in pulsed micro-reactor system.河北省科学院学报, 1986, (1):45-57.
[19]王玮.石脑油裂解动力学模型的研究与开发:[硕士学位论文].上海:华东理工大学化学工程系, 1995.
[20]黄莉莉,何小荣,陈丙珍等.拔头油热裂解制乙烯的数值模拟.计算机与应用化学, 2006, 23(11):23-26.
[21] Wanger E S, Hamid O A. PYROTEC's ethylene plant furnace modeling techology coupled with ASPENTECH's real-time optimization system provides off-line and on-line benefits. ASPEN WORLD 97, Boston, 1997.
[22]石油化工译文集第五集石油烃的裂解(一).北京:石油化学工业出版社, 1976:146.
[23] Sundaram KM, Froment GF. Modeling of thermal cracking kinetics emdash 1. Thermal cracking of ethane, propane and their mixtures. Chemical Engineering Science, 1977, 32 (6):601-608.
[24] Sundaram KM, Froment GF. Modeling of thermal cracking kinetics emdash 2. Cracking of iso-butane, of n-butane and of mixtures ethane-propane-n-butane. Chemical Engineering Science, 1977, 32(6):609-617.
[25] Hirato M, Yoshioka S. Simulation of the pyrolysis of naphtha, kerosene and gas oil with a tubular reactor. Int. Chem. Eng., 1973, 13(2):347-355.
[26] Van Damme PS, Froment GF, Balthasar WB. Scaling up of naphtha cracking coils. Industrial & Engineering Chemistry, Process Design and Development, 1981, 20(2):366-376.
[27] Kumar P, Kunzru D. Modeling of naphtha pyrolysis.Industrial & Engineering Chemistry, Process Design and Development, 1985, 24(3):774-782.
[28] Kumar P, Kunzru D. Kinetics of coke deposition in naphtha.Canadian Journal of Chemical Engineering, 1985, 63(4):598-604.
[29] Cera GD. Parallel dynamic process simulation of a distillation column on the BBN butterfly parallel processor computer. Computers & Chemical Engineering, 1989, 13 (7):737-752.
[30]侯凯湖,王宗祥.大庆馏分油蒸汽热裂解制烯烃反应动力学模型.石油化工,1986, 15(3):145-155.
[31]杨元一.轻质油热裂解制乙烯裂解炉工艺数学模型.大庆石油学院学报, 1981, (9):70-98.
[32]王宗祥,党长涛,郎振飞.油田轻质油热裂解制乙烯反应动力学数学模型的初步探讨.大庆石油学报, 1978, (2):1-13.
[33]王宗祥,罗光熹,吴会清.油田轻质油热裂解制乙烯反应动力学数学模型(1).大庆石油学报, 1978,(2):1-23.
[34]王宗祥,罗光熹,党长涛.油田轻质油热裂解制乙烯反应动力学数学模型(2).大庆石油学报, 1979, (1):1-7.
[35]赵岩,何小荣.乙烯热裂解炉模拟平台的开发及应用.计算机与应用化学,2006, 23(11):1065-1068.
[36]胡益锋,何细藕.石脑油裂解炉动态建模与方针研究. 2004, 2004, 20(6):51-57.
[37]高晓丹,陈丙珍.石脑油裂解过程一次反应选择性系数的估计.计算机与应用化学, 2005, 22(12):1119-1122.
[38]徐强,陈丙珍,何小荣.石脑油在SRT-IV型工业炉清洁辐射管中裂解的数学模拟.计算机与应用化学, 2001, 18(3):223-228.
[39]徐强,陈丙珍,何小荣.SRT-IV型工业裂解炉裂解石脑油的全周期生产数学模拟.计算机与应用化学, 2001, 18(4):303-306.
[40]王松汉.乙烯装置技术.北京:中国石化出版社, 1994.
[41]解冬梅,钱锋,俞金寿.SRT-III型裂解炉中石脑油裂解的工艺数学模型:I-清洁管模型与计算.石油化工, 1993, 22(2):813-819.
[42]解冬梅,钱锋,俞金寿.SRT-III型裂解炉中石脑油裂解的工艺数学模型:II-结焦模型及运转模型分析.石油化工, 1994, 23(2):96-100.
[43]黄文,杨元一,曾清泉.重质油裂解数学模型—裂解炉辐射段炉管工艺计算模型.石油化工, 1986, 10(1):19.
[44] Niaei A, Towfighi J, Sadrameli S M, et al. The combined simulation of heat transfer and pyrolysis reactions in industrial cracking furnaces. Applied Thermal Engineering, 2004, 24(14-15):2251-2265.
[45]胡益锋,徐用懋.石脑油裂解炉建模技术研究[博士学位论文]北京:清华大学自动化系,2005.
[46]韩云龙,章名耀,肖睿.乙烯裂解炉石脑油裂解反应的数值模拟.化学反应工程与工艺, 2006, 22(3):265-270.
[47]蓝兴英,高金森,徐春明.乙烯裂解炉内传递和反应过程综合数值模拟Ⅱ.反应管内传递和反应过程的数值模拟.石油学报, 2003, 19(6):64-69.
[48]蓝兴英,高金森,徐春明.乙烯裂解炉内传递及反应过程综合数值模拟I.数学模型的建立.石油学报, 2004, 19(5):80-85.
[49]蓝兴英,高金森,徐春明.乙烯管式裂解炉的数值模拟.过程工程学报, 2004, 4(3):221-227.
[50]蓝兴英,高金森,徐春明.乙烯裂解炉内传递和反应过程综合数值模拟研究Ⅲ炉膛内燃烧和传热过程的数值模拟.石油学报, 2004, 20 (1):46-50.
[51]张红梅,黄殿利,麻立志.轻质油裂解炉管二维工艺模型的应用.石油化工,2004, 33(1):57-60.
[52] Towfighi J, Karimzadeh R, Niaei A, Saedi G. Application of dynamic programming methodin optimal control and simulation of an industrial naphtha cracker. 2004:10029-10037.
[53] Abel,O., Birk J. Real time optimization of chemical processes with application to olefins production. Automatisierungstechnik, 2002, 50(12):586-596.
[54] Tarafder A, Lee B, Ray AK, et al. Multiobjective optimization of an industrial ethylene reactor using a nondominated sorting genetic algorithm. Industrial and Engineering Chemistry Research, 2005, 44(1):124-141.
[55] Li C, Zhu Q, Geng Z. Multi-objective particle swarm optimization hybrid algorithm: An application on industrial cracking furnace. Industrial and Engineering Chemistry Research, 2007, 46(11):3602-3609.
[56]吴启龙.蒸汽裂解原料优化.石油炼制与化工, 2000, 31(9):45-49.
[57]王基铭.乙烯原料优化问题的探讨.石油化工, 1999, 28(5):333-337.
[58]谢国学.从乙烯原料油的平衡论裂解炉的优化操作与配置.石化技术, 1999, 6(1):1-6.
[59]陈赤榕. 30万吨乙烯/年裂解炉-废热锅炉的模拟与调优.石油化工, 1994,23(7):456-461.
[60]胡大俊.乙烯生产装置的优化操作.石油化工, 1990, 19(2):103-107.
[61]郭宝林.乙烯原料变化对生产的影响及改进措施.石油化工, 1988, 17(2):98-101.
[62]黄一俞.乙烯裂解炉过程建模与操作优化[硕士学位论文]北京:北京化工大学, 2005.
[63]颜飞雪.石脑油热裂解过程参数的优化[硕士学位论文]上海:华东理工大学, 1996.
[64]钱锋,俞金寿.裂解炉最优裂解反应温度分布的研究.浙江大学学报(98增刊),1998, 453-458.
[65] Wysiekierski A G, Fisher G, Schillmoller C M. Control coking for olefins plants. Hydrocarbon Processing, 1999, 78(1):97-100.
[66] Schools E M, Froment G F. Simulation of decoking of thermal cracking coils by steam/air-mixtures. AIChE Journal, 1997, 43(1):118-126.
[67] Reyniers G C, Froment G F, Kopinke F-D, et al. Coke formation in the thermal cracking of hydrocarbons. 4. Modeling of coke formation in naphtha cracking. Industrial & Engineering Chemistry Research, 1994, 33(11):2584-2590.
[68]万文举,赖向军.轻柴油蒸气热裂解结焦动力学.石油化工, 1990, 19(3):143-147.
[69]万文举,赖向军.轻柴油蒸气热裂解结焦动力学.石油化工, 1993, 22(5):313-316.
[70]王延吉,王宗祥.轻柴油蒸气裂解解制烯烃反应管结焦结模型的研究.石油学报, 1987, 3(4):9-21.
[71] Heynderickx GJ, Froment GF. Simulation and comparison of the run length of an ethane cracking furnace with reactor tubes of circular and elliptical cross sections. Industrial & Engineering Chemistry Research, 1998, 37(3):914-922.
[72] Oprins AJM, Heynderickx GJ, Marin GB. Three-dimensional asymmetric flow and temperature fields in cracking furnaces. Industrial & Engineering Chemistry Research,2001, 40(23):5087-5094.
[73]李庆扬.数值计算原理.北京:清华大学出版社, 2000.
[74] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by Simulated Anealing. Science, 1983, 220(4598):671-680.
[75]胡山鹰.连续变量问题全局优化的模拟退火算法.系统工程理论与实践, 1995, (9):73-80.
[76] Holland JH. Adaption in Natural and Artificial Systems. MIT Press, 1975.
[77] Deb K, Agrawal RB. Simulated binary crossover for continuous search space. Complex Systems, 1995, 9: 115-148.
[78] Albuquerque J, Gopal V, Staus G, et al. Interior point SQP strategies for large-scale, structured process optimization problems. Computers and Chemical Engineering, 1999, 23(4-5):543-554.
[79]沈静珠,陈丙珍.过程系统优化.北京:清华大学出版社, 1994.
[80] Schmid C, Biegler L T. Acceleration of reduced Hessian methods for large-scale nonlinear programming. Computers & Chemical Engineering, 1993, 17(5-6):451-463.
[81] Ringuest J L. Multi-objective optimization: behavior and computational considerations. Boston: Kluwer, 1992.
[82] David A, Veldhuizen V, Lamont GB. Multiobjective evolutionary algorithms: analyzing the state-of-art. Evolutionary Computation, 2000, 8(2):125-147.
[83] Zitzler E, Deb K, Thiele L. Comparison of multi-objective evolutionary algorithms: empirical results. Evolutionary Computation, 2000, 8(2):173-195.
[84] Cohon J L. Multiobjective programming and planning. New York: Academic Press, 1978.
[85] Das I, Dennis J E. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Structural Optimization, 1997, 14(63-69).
[86] Rosenberg R S. Simulation of genetic populations with bio-chemical properties.Michigan: University of Michigan, 1967.
[87] Schaffer J D. Mutiple objective optimization with vector evaluated genetic algorithms. The 1st International Conference on Genetic algorithms, 1985:93-100.
[88] Fonseca C M, Fleming P J. Genetic algorithms for multi-objective optimizaiton: formulation, discussion and generation. The 5th International Conference on Genetic Algorithms, 1993:416-423.
[89] Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 1994, 2(3):221-248.
[90] Horn J, Nafpliotis N. Multiobjective optimization using the niched Pareto genetic algorithm.Univeristy of Illinois at Urbana-Champaign, Urbana, Illinois, USA: Technical Report, IlliGAL Report 93005, 1993.
[91] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[92] Knowles J, Corne D. The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization. Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press, 98., 1999.
[93] Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4):9257-9271.
[94] Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Research Report, May 2001.
[95] Corne D, Knowles J, Oates M. The pareto envelope-based selection algorithm for multi-objective optimization. In proceedings of the 6th international conference on parallel problem solving from nature VI(PPSN-VI), 2000, 839-848.
[96] Hart W, Belew R. Optimization with genetic algorithm hybrids that use local search. Adaptive Individuals in Evolving Populations: Models and Algorithms, Santa Fe Institute studies in the science of complexity, volume 26. Addison-Wesley. 1996.
[97] Knowles J, Corne D. M-PAES: A memetic algorithm for multiobjective optimization. Proceedings of 2000 Congress on Evolutionary Computation, San Diego, CA, July 16-19, 2000.
[98] Ishibuchi H, Murata T. Multi-objective genetic local search algorithm. Proceedings of 3rd IEEE Int. Conf. Evolutionary Computation, Nagoya, Japan,1996, 119.
[99] Ishibuchi H, Yoshida T, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 2003, 7(2):204-223.
[100] Ishibuchi H, Narukawa K. Performance evaluation of simple multiobjective genetic local search algorithms on multiobjective 0/1 knapsack problems. 2004 Congress on Evolutionary Computation, CEC2004, 2004:441.
[101] Hu X, Huang z, Wang Z. Hybridization of the Multi-Objective Evolutionary Algorithms and the Gradient-based Algorithms. 2003 Congress on Evolutionary Computation (CEC'2003), 2003:870.
[2]杨友麒.乙烯工厂的模拟、先进控制及实时优化.石油化工, 1999, 28(11):788-793.
[3]崔静怡.乙烯原料发展趋势及优化建议.石油化工技术经济, 2001, 17(6):13-16.
[4]王松汉.乙烯工艺与技术.北京:中国石化出版社, 2000.
[5]蓝兴英.乙烯管式裂解炉的数值模拟.过程工程学报, 2004, 4(3):221-227.
[6]郝红,熊国华,张少锋等.烃类裂解得丙烯产率计算.化学工程, 1999, 27(2):54-56.
[7]关国民,王宗祥.大庆石脑油裂解炉计算机控制模型.石油化工, 1990, 19(8):523-529.
[8]李玉刚,项曙光.SRT-III型柴油裂解炉的动态模型.石油化工, 1999, 28(10):700-703.
[9]张殿华.不同石脑油原料乙烯收率的估算与裂解炉调优方向.石油化工,1987, 16(6):426-429.
[10] Sundaram K M, Froment G F. Modeling of thermal cracking kinetics emdash 3. Radical mechanisms for the pyrolysis of simple paraffins, and their mixtures. Industrial & Engineering Chemistry, Fundamentals, 1978, 17(3):174-182.
[11] Schaerfe M, Ederer H J, Stable U,et al. Modeling of n-Hexane pyrolysis experimental investigations in a flow reactor at normal pressure. German Chemical Engineering, 1985, 8(2):119-129.
[12] Aribike D S, Susu A.A. Kinetics and mechenism of the thermal cracking of n-Heptane. Thermochim Acta, 1988, 127:247-258.
[13] Stabel U, Honig U F, Nowak U, et al. Thermal decomposition of 2,3-dimethyl butane. Experiments and mechanistic modelling. Chemical Engineering & Technology, 1990, 13(4):241-251.
[14] Chen Q, Froment G F. Thermal cracking of substituted aromatic hydrocarbons. I. Kinetic study of the thermal cracking of i-propylbenzene. Journal of Analytical and Applied Pyrolysis, 1991, 21(1-2):27.
[15] Chen Q, Froment GF. Thermal cracking of substituted aromatic hydrocarbons. II. Kinetic study of the thermal cracking of n-propylbenzene and ethylbenzene. Journal of Analytical and Applied Pyrolysis, 1991, 21(1-2):51.
[16] Aguilar E, Ortiz C H, Arzate E. Analysis shows revamp route to naphtha feed. Oil and Gas Journal, 1988, 86(47):48-51.
[17] Joo E, Park S, Lee M. Pyrolysis reaction mechanism for industrial naphtha cracking furnaces. Industrial and Engineering Chemistry Research, 2001, 40(11):2409-2415.
[18]邹仁鋆,邹晋. Study on the simultaneous pyrolysis of ethane-propane mixture in pulsed micro-reactor system.河北省科学院学报, 1986, (1):45-57.
[19]王玮.石脑油裂解动力学模型的研究与开发:[硕士学位论文].上海:华东理工大学化学工程系, 1995.
[20]黄莉莉,何小荣,陈丙珍等.拔头油热裂解制乙烯的数值模拟.计算机与应用化学, 2006, 23(11):23-26.
[21] Wanger E S, Hamid O A. PYROTEC's ethylene plant furnace modeling techology coupled with ASPENTECH's real-time optimization system provides off-line and on-line benefits. ASPEN WORLD 97, Boston, 1997.
[22]石油化工译文集第五集石油烃的裂解(一).北京:石油化学工业出版社, 1976:146.
[23] Sundaram KM, Froment GF. Modeling of thermal cracking kinetics emdash 1. Thermal cracking of ethane, propane and their mixtures. Chemical Engineering Science, 1977, 32 (6):601-608.
[24] Sundaram KM, Froment GF. Modeling of thermal cracking kinetics emdash 2. Cracking of iso-butane, of n-butane and of mixtures ethane-propane-n-butane. Chemical Engineering Science, 1977, 32(6):609-617.
[25] Hirato M, Yoshioka S. Simulation of the pyrolysis of naphtha, kerosene and gas oil with a tubular reactor. Int. Chem. Eng., 1973, 13(2):347-355.
[26] Van Damme PS, Froment GF, Balthasar WB. Scaling up of naphtha cracking coils. Industrial & Engineering Chemistry, Process Design and Development, 1981, 20(2):366-376.
[27] Kumar P, Kunzru D. Modeling of naphtha pyrolysis.Industrial & Engineering Chemistry, Process Design and Development, 1985, 24(3):774-782.
[28] Kumar P, Kunzru D. Kinetics of coke deposition in naphtha.Canadian Journal of Chemical Engineering, 1985, 63(4):598-604.
[29] Cera GD. Parallel dynamic process simulation of a distillation column on the BBN butterfly parallel processor computer. Computers & Chemical Engineering, 1989, 13 (7):737-752.
[30]侯凯湖,王宗祥.大庆馏分油蒸汽热裂解制烯烃反应动力学模型.石油化工,1986, 15(3):145-155.
[31]杨元一.轻质油热裂解制乙烯裂解炉工艺数学模型.大庆石油学院学报, 1981, (9):70-98.
[32]王宗祥,党长涛,郎振飞.油田轻质油热裂解制乙烯反应动力学数学模型的初步探讨.大庆石油学报, 1978, (2):1-13.
[33]王宗祥,罗光熹,吴会清.油田轻质油热裂解制乙烯反应动力学数学模型(1).大庆石油学报, 1978,(2):1-23.
[34]王宗祥,罗光熹,党长涛.油田轻质油热裂解制乙烯反应动力学数学模型(2).大庆石油学报, 1979, (1):1-7.
[35]赵岩,何小荣.乙烯热裂解炉模拟平台的开发及应用.计算机与应用化学,2006, 23(11):1065-1068.
[36]胡益锋,何细藕.石脑油裂解炉动态建模与方针研究. 2004, 2004, 20(6):51-57.
[37]高晓丹,陈丙珍.石脑油裂解过程一次反应选择性系数的估计.计算机与应用化学, 2005, 22(12):1119-1122.
[38]徐强,陈丙珍,何小荣.石脑油在SRT-IV型工业炉清洁辐射管中裂解的数学模拟.计算机与应用化学, 2001, 18(3):223-228.
[39]徐强,陈丙珍,何小荣.SRT-IV型工业裂解炉裂解石脑油的全周期生产数学模拟.计算机与应用化学, 2001, 18(4):303-306.
[40]王松汉.乙烯装置技术.北京:中国石化出版社, 1994.
[41]解冬梅,钱锋,俞金寿.SRT-III型裂解炉中石脑油裂解的工艺数学模型:I-清洁管模型与计算.石油化工, 1993, 22(2):813-819.
[42]解冬梅,钱锋,俞金寿.SRT-III型裂解炉中石脑油裂解的工艺数学模型:II-结焦模型及运转模型分析.石油化工, 1994, 23(2):96-100.
[43]黄文,杨元一,曾清泉.重质油裂解数学模型—裂解炉辐射段炉管工艺计算模型.石油化工, 1986, 10(1):19.
[44] Niaei A, Towfighi J, Sadrameli S M, et al. The combined simulation of heat transfer and pyrolysis reactions in industrial cracking furnaces. Applied Thermal Engineering, 2004, 24(14-15):2251-2265.
[45]胡益锋,徐用懋.石脑油裂解炉建模技术研究[博士学位论文]北京:清华大学自动化系,2005.
[46]韩云龙,章名耀,肖睿.乙烯裂解炉石脑油裂解反应的数值模拟.化学反应工程与工艺, 2006, 22(3):265-270.
[47]蓝兴英,高金森,徐春明.乙烯裂解炉内传递和反应过程综合数值模拟Ⅱ.反应管内传递和反应过程的数值模拟.石油学报, 2003, 19(6):64-69.
[48]蓝兴英,高金森,徐春明.乙烯裂解炉内传递及反应过程综合数值模拟I.数学模型的建立.石油学报, 2004, 19(5):80-85.
[49]蓝兴英,高金森,徐春明.乙烯管式裂解炉的数值模拟.过程工程学报, 2004, 4(3):221-227.
[50]蓝兴英,高金森,徐春明.乙烯裂解炉内传递和反应过程综合数值模拟研究Ⅲ炉膛内燃烧和传热过程的数值模拟.石油学报, 2004, 20 (1):46-50.
[51]张红梅,黄殿利,麻立志.轻质油裂解炉管二维工艺模型的应用.石油化工,2004, 33(1):57-60.
[52] Towfighi J, Karimzadeh R, Niaei A, Saedi G. Application of dynamic programming methodin optimal control and simulation of an industrial naphtha cracker. 2004:10029-10037.
[53] Abel,O., Birk J. Real time optimization of chemical processes with application to olefins production. Automatisierungstechnik, 2002, 50(12):586-596.
[54] Tarafder A, Lee B, Ray AK, et al. Multiobjective optimization of an industrial ethylene reactor using a nondominated sorting genetic algorithm. Industrial and Engineering Chemistry Research, 2005, 44(1):124-141.
[55] Li C, Zhu Q, Geng Z. Multi-objective particle swarm optimization hybrid algorithm: An application on industrial cracking furnace. Industrial and Engineering Chemistry Research, 2007, 46(11):3602-3609.
[56]吴启龙.蒸汽裂解原料优化.石油炼制与化工, 2000, 31(9):45-49.
[57]王基铭.乙烯原料优化问题的探讨.石油化工, 1999, 28(5):333-337.
[58]谢国学.从乙烯原料油的平衡论裂解炉的优化操作与配置.石化技术, 1999, 6(1):1-6.
[59]陈赤榕. 30万吨乙烯/年裂解炉-废热锅炉的模拟与调优.石油化工, 1994,23(7):456-461.
[60]胡大俊.乙烯生产装置的优化操作.石油化工, 1990, 19(2):103-107.
[61]郭宝林.乙烯原料变化对生产的影响及改进措施.石油化工, 1988, 17(2):98-101.
[62]黄一俞.乙烯裂解炉过程建模与操作优化[硕士学位论文]北京:北京化工大学, 2005.
[63]颜飞雪.石脑油热裂解过程参数的优化[硕士学位论文]上海:华东理工大学, 1996.
[64]钱锋,俞金寿.裂解炉最优裂解反应温度分布的研究.浙江大学学报(98增刊),1998, 453-458.
[65] Wysiekierski A G, Fisher G, Schillmoller C M. Control coking for olefins plants. Hydrocarbon Processing, 1999, 78(1):97-100.
[66] Schools E M, Froment G F. Simulation of decoking of thermal cracking coils by steam/air-mixtures. AIChE Journal, 1997, 43(1):118-126.
[67] Reyniers G C, Froment G F, Kopinke F-D, et al. Coke formation in the thermal cracking of hydrocarbons. 4. Modeling of coke formation in naphtha cracking. Industrial & Engineering Chemistry Research, 1994, 33(11):2584-2590.
[68]万文举,赖向军.轻柴油蒸气热裂解结焦动力学.石油化工, 1990, 19(3):143-147.
[69]万文举,赖向军.轻柴油蒸气热裂解结焦动力学.石油化工, 1993, 22(5):313-316.
[70]王延吉,王宗祥.轻柴油蒸气裂解解制烯烃反应管结焦结模型的研究.石油学报, 1987, 3(4):9-21.
[71] Heynderickx GJ, Froment GF. Simulation and comparison of the run length of an ethane cracking furnace with reactor tubes of circular and elliptical cross sections. Industrial & Engineering Chemistry Research, 1998, 37(3):914-922.
[72] Oprins AJM, Heynderickx GJ, Marin GB. Three-dimensional asymmetric flow and temperature fields in cracking furnaces. Industrial & Engineering Chemistry Research,2001, 40(23):5087-5094.
[73]李庆扬.数值计算原理.北京:清华大学出版社, 2000.
[74] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by Simulated Anealing. Science, 1983, 220(4598):671-680.
[75]胡山鹰.连续变量问题全局优化的模拟退火算法.系统工程理论与实践, 1995, (9):73-80.
[76] Holland JH. Adaption in Natural and Artificial Systems. MIT Press, 1975.
[77] Deb K, Agrawal RB. Simulated binary crossover for continuous search space. Complex Systems, 1995, 9: 115-148.
[78] Albuquerque J, Gopal V, Staus G, et al. Interior point SQP strategies for large-scale, structured process optimization problems. Computers and Chemical Engineering, 1999, 23(4-5):543-554.
[79]沈静珠,陈丙珍.过程系统优化.北京:清华大学出版社, 1994.
[80] Schmid C, Biegler L T. Acceleration of reduced Hessian methods for large-scale nonlinear programming. Computers & Chemical Engineering, 1993, 17(5-6):451-463.
[81] Ringuest J L. Multi-objective optimization: behavior and computational considerations. Boston: Kluwer, 1992.
[82] David A, Veldhuizen V, Lamont GB. Multiobjective evolutionary algorithms: analyzing the state-of-art. Evolutionary Computation, 2000, 8(2):125-147.
[83] Zitzler E, Deb K, Thiele L. Comparison of multi-objective evolutionary algorithms: empirical results. Evolutionary Computation, 2000, 8(2):173-195.
[84] Cohon J L. Multiobjective programming and planning. New York: Academic Press, 1978.
[85] Das I, Dennis J E. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Structural Optimization, 1997, 14(63-69).
[86] Rosenberg R S. Simulation of genetic populations with bio-chemical properties.Michigan: University of Michigan, 1967.
[87] Schaffer J D. Mutiple objective optimization with vector evaluated genetic algorithms. The 1st International Conference on Genetic algorithms, 1985:93-100.
[88] Fonseca C M, Fleming P J. Genetic algorithms for multi-objective optimizaiton: formulation, discussion and generation. The 5th International Conference on Genetic Algorithms, 1993:416-423.
[89] Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 1994, 2(3):221-248.
[90] Horn J, Nafpliotis N. Multiobjective optimization using the niched Pareto genetic algorithm.Univeristy of Illinois at Urbana-Champaign, Urbana, Illinois, USA: Technical Report, IlliGAL Report 93005, 1993.
[91] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[92] Knowles J, Corne D. The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization. Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press, 98., 1999.
[93] Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4):9257-9271.
[94] Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Research Report, May 2001.
[95] Corne D, Knowles J, Oates M. The pareto envelope-based selection algorithm for multi-objective optimization. In proceedings of the 6th international conference on parallel problem solving from nature VI(PPSN-VI), 2000, 839-848.
[96] Hart W, Belew R. Optimization with genetic algorithm hybrids that use local search. Adaptive Individuals in Evolving Populations: Models and Algorithms, Santa Fe Institute studies in the science of complexity, volume 26. Addison-Wesley. 1996.
[97] Knowles J, Corne D. M-PAES: A memetic algorithm for multiobjective optimization. Proceedings of 2000 Congress on Evolutionary Computation, San Diego, CA, July 16-19, 2000.
[98] Ishibuchi H, Murata T. Multi-objective genetic local search algorithm. Proceedings of 3rd IEEE Int. Conf. Evolutionary Computation, Nagoya, Japan,1996, 119.
[99] Ishibuchi H, Yoshida T, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 2003, 7(2):204-223.
[100] Ishibuchi H, Narukawa K. Performance evaluation of simple multiobjective genetic local search algorithms on multiobjective 0/1 knapsack problems. 2004 Congress on Evolutionary Computation, CEC2004, 2004:441.
[101] Hu X, Huang z, Wang Z. Hybridization of the Multi-Objective Evolutionary Algorithms and the Gradient-based Algorithms. 2003 Congress on Evolutionary Computation (CEC'2003), 2003:870.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |