热集成精馏系统建模、优化与控制的若干问题研究
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摘要
在石油加工工业和化学工业中,精馏是分离液相混合物十分重要的操作单元,同时它又是一个高耗能设备。随着全球范围内能源危机的日益严重,以及市场竞争的日益激烈,使得人们存在着无数的动机,采用新的或改进的方法和设备来降低它的操作能耗。一方面,通过利用优化技术、先进控制技术来降低能耗,另一方面,通过工艺上改进,如热泵循环精馏、热集成精馏等方法来降低精馏过程的能耗。
本论文的研究对象是双塔热集成精馏系统,它是精馏过程中有效的节能操作方式之一,节能效果可达50%。它的基本原理是由一个高压塔和一个低压塔来构成一个精馏系统,高压塔的塔顶蒸汽作为低压塔的塔釜供热,以节省能耗。为了能够达到理想的节能效果,必须对这两个塔进行合理的操作和控制,否则,反而会影响正常的生产。但由于采用了这样的热集成操作方式,使得两个塔之间的关联变得十分严重,给系统的操作和控制带来了很大的难度。
本论文将以分离甲醇和水二元混合液的热集成精馏系统为研究对象,展开了机理模型的建立、模型参数估计、状态变量估计、以最小能耗为目标的动态优化和控制的研究工作。论文主要内容包括:
(1) 热集成精馏系统的动态机理模型建立方法介绍。在建立机理模型过程中,充分考虑了每个塔板的9个状态变量:塔板温度,塔板压力,汽、液相流量,汽、液相浓度和持液量(Holdup)的动态特性。利用Wilson模型计算液相的非理想特性。根据汽液平衡、物料平衡和能量平衡所建的机理模型是一组非线性的微分代数方程组。为了能够使模型适合优化和控制策略研究的需要,利用三点正交配置离散化的方法,对模型方程进行离散化,将其转化成代数方程,然后,利用牛顿—拉夫逊方法来求解。
(2) 为了提高模型的精度和实现热集成精馏系统先进控制策略,必须解决上述模型中的塔板效率和进料浓度等参数的在线估计问题。热集成精馏系统模型的参数估计是一个大系统、非线性、时变的多参数估计问题,本论文提出了可调加权系数动态优化的参数估计方法,在该方法中利用二级动态优化进行参数估计的方法。通过对优化过程的分析,揭示了参数估计的内在特性,进一步提出了反馈
Distillation is an important and energy-intensive separation technique for fluid mixtures in chemical and petrochemical industry. With the increasing competition in the chemical industry and an energy crisis in the world, lots of improved approaches for decreasing the operation cost in distillation process have been developed, e.g. optimal and advanced control technology, new techniques including the heat pump distillation, and heat-integrated distillation column system.A two-column heat-integrated distillation system is studied in detail in this dissertation. 50% energy savings can be achieved in this distillation system by running one of the columns at a higher pressure and integrating the condenser of the high-pressure (HP) column with the reboiler of the low-pressure (LP) column. Because the heat integration leads to significant difficulties in operation and control due to the strong coupling in material and energy of the two columns, it is a challenge to operate this system correctly so that the plant is operational and the energy savings are achieved.The main research wok and contributions of this dissertation are as follows:(1) The dynamic rigorous model of the heat-integrated distillation column system is introduced which is based on vapor-liquid equilibrium, component material balances and energy balance for each tray. The non-ideal liquid phase is computed by Wilson model. The compositions of vapor and liquid phases, vapor and liquid flow rates, temperature, pressure, and liquid holdups are the state variables in the model. The rigorous system model is a set of nonlinear differential and algebraic equations (DAEs) which are discretized by the orthogonal collocation on finite elements with three points. The model equations are solved by conventional Newton-Raphson algorithm.(2) It is crucial to estimate the tray efficiencies and feed composition for improving the accuracy of model and implementing of advanced control strategy for the heat-integrated distillation column system. A new parameter estimation approach is proposed which is addressed by turning weight factors in the least-squares dynamic
optimization, hi this work, through analysis of the solution procedure of the resulting optimization problem, it is found that parameter estimation with a weighted least-squares method is equivalent to a multi-variable closed-loop control system. The weighting factors in the objective function are equivalent to the controller parameters. Like any control system synthesis, the tuning can be made based on the output-input sensitivity analysis. The effectiveness of the proposed approach is illustrated by comparing the results of this method with the results of the general least-squares method.(3) To implement the dynamic optimization and control strategy, it is necessary to estimate the unmeasurable state variables in every time interval. A new approach, which is called optimization-predictor method, is proposed to estimate the unmeasurable state variables in such a large-scale system. The results show that the proposed approach is effective for real-time state estimation of the nonlinear large-scale heat-integrated distillation column system.(4) Two-level control strategy integrated dynamic optimization and control of a heat-integrated distillation column system is proposed to minimize the heat supply to HP column when the feed rate and composition are changed, and the product qualities are satisfied. The optimization variables are heat supply to HP column reboiler, HP column reflux rate, LP column reflux rate, and feed rate of LP column.Finally some conclusions and future researches are drawn in this dissertation.
本论文的研究对象是双塔热集成精馏系统,它是精馏过程中有效的节能操作方式之一,节能效果可达50%。它的基本原理是由一个高压塔和一个低压塔来构成一个精馏系统,高压塔的塔顶蒸汽作为低压塔的塔釜供热,以节省能耗。为了能够达到理想的节能效果,必须对这两个塔进行合理的操作和控制,否则,反而会影响正常的生产。但由于采用了这样的热集成操作方式,使得两个塔之间的关联变得十分严重,给系统的操作和控制带来了很大的难度。
本论文将以分离甲醇和水二元混合液的热集成精馏系统为研究对象,展开了机理模型的建立、模型参数估计、状态变量估计、以最小能耗为目标的动态优化和控制的研究工作。论文主要内容包括:
(1) 热集成精馏系统的动态机理模型建立方法介绍。在建立机理模型过程中,充分考虑了每个塔板的9个状态变量:塔板温度,塔板压力,汽、液相流量,汽、液相浓度和持液量(Holdup)的动态特性。利用Wilson模型计算液相的非理想特性。根据汽液平衡、物料平衡和能量平衡所建的机理模型是一组非线性的微分代数方程组。为了能够使模型适合优化和控制策略研究的需要,利用三点正交配置离散化的方法,对模型方程进行离散化,将其转化成代数方程,然后,利用牛顿—拉夫逊方法来求解。
(2) 为了提高模型的精度和实现热集成精馏系统先进控制策略,必须解决上述模型中的塔板效率和进料浓度等参数的在线估计问题。热集成精馏系统模型的参数估计是一个大系统、非线性、时变的多参数估计问题,本论文提出了可调加权系数动态优化的参数估计方法,在该方法中利用二级动态优化进行参数估计的方法。通过对优化过程的分析,揭示了参数估计的内在特性,进一步提出了反馈
Distillation is an important and energy-intensive separation technique for fluid mixtures in chemical and petrochemical industry. With the increasing competition in the chemical industry and an energy crisis in the world, lots of improved approaches for decreasing the operation cost in distillation process have been developed, e.g. optimal and advanced control technology, new techniques including the heat pump distillation, and heat-integrated distillation column system.A two-column heat-integrated distillation system is studied in detail in this dissertation. 50% energy savings can be achieved in this distillation system by running one of the columns at a higher pressure and integrating the condenser of the high-pressure (HP) column with the reboiler of the low-pressure (LP) column. Because the heat integration leads to significant difficulties in operation and control due to the strong coupling in material and energy of the two columns, it is a challenge to operate this system correctly so that the plant is operational and the energy savings are achieved.The main research wok and contributions of this dissertation are as follows:(1) The dynamic rigorous model of the heat-integrated distillation column system is introduced which is based on vapor-liquid equilibrium, component material balances and energy balance for each tray. The non-ideal liquid phase is computed by Wilson model. The compositions of vapor and liquid phases, vapor and liquid flow rates, temperature, pressure, and liquid holdups are the state variables in the model. The rigorous system model is a set of nonlinear differential and algebraic equations (DAEs) which are discretized by the orthogonal collocation on finite elements with three points. The model equations are solved by conventional Newton-Raphson algorithm.(2) It is crucial to estimate the tray efficiencies and feed composition for improving the accuracy of model and implementing of advanced control strategy for the heat-integrated distillation column system. A new parameter estimation approach is proposed which is addressed by turning weight factors in the least-squares dynamic
optimization, hi this work, through analysis of the solution procedure of the resulting optimization problem, it is found that parameter estimation with a weighted least-squares method is equivalent to a multi-variable closed-loop control system. The weighting factors in the objective function are equivalent to the controller parameters. Like any control system synthesis, the tuning can be made based on the output-input sensitivity analysis. The effectiveness of the proposed approach is illustrated by comparing the results of this method with the results of the general least-squares method.(3) To implement the dynamic optimization and control strategy, it is necessary to estimate the unmeasurable state variables in every time interval. A new approach, which is called optimization-predictor method, is proposed to estimate the unmeasurable state variables in such a large-scale system. The results show that the proposed approach is effective for real-time state estimation of the nonlinear large-scale heat-integrated distillation column system.(4) Two-level control strategy integrated dynamic optimization and control of a heat-integrated distillation column system is proposed to minimize the heat supply to HP column when the feed rate and composition are changed, and the product qualities are satisfied. The optimization variables are heat supply to HP column reboiler, HP column reflux rate, LP column reflux rate, and feed rate of LP column.Finally some conclusions and future researches are drawn in this dissertation.
引文
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[2] Stichlmair J, Thiel J. Design of distillation columns- state of the art and future developments from perspective of universities and industry [J], Chem. Ing. Tech., 2003, 75(8): 1163-1164.
[3] Humphrey J L, Seibert A E Separation technologies: an opportunity for energy saving [J], Chem. Eng. Progr., 1992, 88(3): 32-41.
[4] 金晓明,张泉灵,王树青.预测函数控制在精馏塔重沸炉中的应用[J],自动化仪表,2001,22(12):19-22.
[5] Diehl M, Bock H G, Schloder J P, Findeisen R, Nagy Z, Allgower F. Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations [J], J. of Process Control, 2002,12(4): 577-585.
[6] Karacan S. Application of a non-linear long range predictive control to a packed distillation column [J], Chem. Eng. Proc., 2003, 42(12): 943-953.
[7] Qin S J, Badgwell T A. A survey of industrial model predictive control technology [J], Control Engineering Practice, 2003, 11(7): 733-764.
[8] 郭敏强.三相精馏过程的非平衡模型与优化研究[D].浙江大学博士学位论文,2004.
[9] 王树青等编著.先进控制技术及应用[M].北京:化学工业出版社,2001.
[10] 王树青,金晓明等编著.先进控制技术应用实例M].北京:化学工业出版社,2005.
[11] Wozny G, Li P, Wendt M. Optimization in process engineering: state of the art and future trends [J], Inzynieria Chemiczna I Procesowa, 2001, 22(3A): 137-163.
[12] Wozny G, Li P. Planning and optimization of dynamic plant operation[J], Applied Thermal Engineering, 2000, 20:1393-1407.
[13]Nacedal J, Wright S J. Numerical Optimization [M], Springer, New York, 1999.
[14] Cervantes A, Biegler L T. Large-scale DAE optimization using a simultaneous NLP formulation [J],AICHEJ., 1998,44(5): 1038-1050.
[15]Biegler L T, Grossnann I E, Westerberg A W. Systematic Methods of Chemical Process Design [M], Prentice Hall, New Jersey, 1997.
[16]Floudas C A. Nonlinear and Mixed-integer Optimization [M], Oxford University Press, 1995.
[17] Brooke A, Kendrick D, Meeraus A. GAMS release 2.25: A User's Guide, GAMS Development Corporation, Washington DC.
[18]ROMeo Training Handbook, Getting started with ROMeo, Simulation Science Inc., California, 2000.
[19]gPROMS vl. 7 Release Notes, Process Systems Enterprise Ltd., London, 1999.
[20]Garrat T J, Vassiliadis V S. Application of control vector parameterization in larege-scale optimal control problems, Aspen world 2000, Florida, Feb. 6-11,2000.
[21]Mudt D R, Petersen C C. Use of rigorous models in refinery-wide optimization, Petroleum Technology Quarterly, winter 2000.
[22]Onstott B, Linn R. ROMeo provides state of the art optimization, World Refining, 2000, 26.
[23]Floudas C A, Pardalos P M (ED). State of the art in global optimization:computational methods and applications [M], Kluwer Academic Publishers, Dorecht, 1996.
[24]Adjiman C S, Dallwig S, Floudas C A, Nuemaeir A. A global optimization method, aBB, for general twice-differentiable constrained NLPs-I. Theoretical advances [J], Comput. Chem. Eng, 1998,22(9): 1137-1158.
[25] Adjiman C S, Androulakis I P, Floudas C A. A global optimization method, aBB, for general twice-differentiable constrained NLPs-II. Implementation and computational results [J], Comput. Chem. Eng, 1998,22(9): 1159-1179.
[26] Goffe W L, Ferrier G D, Rogers J. Global optimization of statistical functions with simulated annealing [J], J. Econometrics, 1994,60(1-2):65-99.
[27]Ingeber L. Simulated annealing: practice versus theory [J], J. Math. Comput. Model, 1993,11:29.
[28]Floquet P, Piboloueau L, Domennech S. Separation sequence synthesis: How to use the simulated annealing procedure [J], Comput. Chem. Eng, 1994, 18(11-12): 1141-1148.
[29] Hanke M, Li P. Simulated annealing for the optimization of batch distillation production processes [J], Comput. Chem. Eng., 2000, 24(1): 1-8.
[30] Finlayson B A. Nonlinear Analysis in Chemical Engineering [M], McGraw-Hill, New York, 1980.
[31] Bock H G, Plittk K J. A multiple shooting algorithm for direct solution of optimal control problems, IFAC 9th World Congress, Budapest, Hungry, July 2-6, 1984.
[32] Biegler L T, Cervantes A, Wachter A. Advances in simultaneous strategies for dynamic process optimization [J], Chem. Eng. Sci., 2002, 57(4): 575-593.
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