油气管道优化技术

1、 中国石油大学（北京） 吴长春 最优化方法主要分支l Linear programming Linear programming l Transportation problem Transportation probleml Nonlinear programming Nonlinear programmingl Integer programming Integer programmingl Dynamic programming Dynamic programmingl Geometry programming Geometry programmingl Multi-objective

2、programming Multi-objective programmingl Network programming Network programmingl Stochastic programmingStochastic programmingl Fuzzy programmingFuzzy programmingl Combinatory optimization Combinatory optimizationl Optimal control Optimal controll Large scale system optimization Large scale system o

3、ptimization最优化方法在油气储运系统的应用l 油气集输系统优化设计l 油气集输系统优化运行l 石油产品调合l 油库进货计划l 原油和成品油调运方案l 原油调运、炼厂配产、油品调运一体化l 油气管道（网）优化运行l 油气管道（网）优化设计l 油气管道（网）最优规划l 石油场站优化设计 油气储运工程主要优化方法 动态规划动态规划法法 (DP): Dynamic Programming(DP): Dynamic Programming 线性规划：LP 非线性规划：NLP 整数规划：IP 非线性混合整数规划：MNLP 遗传算法：GA 蚁群算法，微粒群算法：ANT，PSO 模拟退火算法：SA

4、PSIG 9803 PSIG：Pipeline Simulation Interest Group 管道仿真合作组织管道仿真合作组织Pipeline Optimization: Dynamic Programming after 30 YearsDr. Richard G. CarterStoner Associates Inc.5177 Richmond Avenue, Suite 900Houston TX 77056-.October 29, 1998AbstractOn the 30th anniversary of PSIG it seems appropriate to revie

5、w the status of Dynamic Programming after 30 years of use in the pipeline industry. Dynamic programming (DP) has been one of the workhorse techniques of pipeline optimization since the late 1960s. Originally applied to gunbarrel systems, it gained popularity due to its fast computational speed on su

6、ch sequential systems and its insensitivity to simulation nonlinearities, noise, and modeling discontinuities.In the late 1980s hybrid DP/Enumeration/Annealing methods were produced which could optimize more general branched and looped networks. Although these were very successful at optimizing pipe

7、lines - sometimes by a large percentage - the hybrid nature of the methods sometimes cause long runtimes or reduced accuracy in solving the discretized problem.Recent advances have allowed us to perform pure DP directly to general branched and looped systems. Not only is full solution accuracy there

8、by assured, but the pure DP method have been up to 10 times faster than hybrid methods in our tests on complex networks. This allows rapid turn around of optimization runs during design and feasibility studies. We illustrate these results with real-world examples.We cap off our historical perspectiv

9、e by comparing DP solutions to those generated by another class of methods that have gained support in recent years: Genetic Algorithms.J. T. Jefferson. Shell Pipe Line Calls It Dynamic programming. Oil and Gas Journal, pages 102-107, May 8，1961.References:R. Bellman. Dynamic Programming. Princeton University Press, Princeton NJ, 1957.P.J. Wong and R.E. Larson. Optimization of natural gas pipeline systems viadynamic programming. IEEE Trans. Auto. Control, AC-1