基于CAPE-OPEN标准的简捷炼油过程建模及常减压蒸馏流程在线模拟

摘要/Abstract

摘要： 简捷炼油过程模型广泛应用于运行监测、在线计算、实时优化等领域，但大多数模型只是将输入、输出变量进行关联，无法计算出各组分流量组成，难以集成在模拟软件中进行全流程模拟。本研究简化了炼油分离过程，开发了基于CAPE-OPEN标准的简捷炼油塔模型。首先，将炼油过程简化为连续的分离单元，根据实际产品数据校正模型参数，实现某一实际工况流程模拟；其次，将模型参数与实际工况操作变量数据相结合，采用径向基函数插值确定操作变量对模型参数的影响关系，实现多种工况流程模拟；最后，通过常减压蒸馏工业装置实例的3种工况进行验证。结果表明：各工况模拟结果与Aspen计算结果相比，初馏塔塔顶油馏出温度点偏差大多在2%以内，常减压蒸馏塔各侧线油的馏出温度点平均偏差在3%以内；与实际结果相比，平均误差都在5%以内，符合工程允许误差范围要求。所建模型的计算速度快，各塔均在0.2 s 左右完成计算，适用于在线生产、调度等。

关键词: 简捷炼油模型, 过程建模, CAPE-OPEN, 在线模拟计算, 常减压蒸馏装置

Abstract: Simple refining process model is widely used in operation monitoring, on-line calculation, real-time optimization and other fields. However, most of the models only correlate the input and output variables, so they can not calculate the composition of each group of diverting quantities and can not be integrated into the simulation software to simulate the whole process. In this study,the separation process was simplified,and a simple refinery column model based on CAPE-OPEN standardwas developed. Firstly, the refining process was simplified as a continuous separation unit, and the model parameters could be corrected according to the actual product datato simulate a real process. Secondly, the model parameters were combined with the actual operating variable data, and radial basis interpolation was used to establish the influence of operating variables on the model parameters, so as to realize the simulation of various operating conditions. Finally, three working conditions of commercial atmospheric and vacuum distillation unit were verified. The results showed that compared with Aspen's calculation results, the deviation of the preflash column was mostly within 2%, and the average deviation of the side lines of the atmospheric and vacuum column was less than 3%;compared with the actual results, the average error is less than 5%, in line with the allowable engineering error range. The calculation speed of the model is fast, andthe calculation of each columnis completed in about 0.2 s, which is suitable for on-line production and scheduling.

Key words: simple refining model, process modeling, CAPE-OPEN, online simulation calculation, atmospheric and vacuum distillation unit

焦云强陈玉石王建平. 基于CAPE-OPEN标准的简捷炼油过程建模及常减压蒸馏流程在线模拟[J]. 石油炼制与化工, 2024, 55(8): 164-177.

参考文献

[1]Chu F, Dai B, Ma X, et al. A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method [J]. IEEE Transactions on Automation Science and Engineering, 2019, 17(2): 947-956. [2] 黄德先, 江永亨, 金以慧. 炼油工业过程控制的研究现状、问题与展望[J]. 自动化学报, 2017, 43(6): 902-916. HUANG Dexian, JIANG Yongheng, JIN Yihui. Present research situation, major bottlenecks, and prospect of refinery industry process control[J]. Acta Automatica Sinica, 2017, 43(6): 902-916. [3] 刘广杰, 孙晓岩, 毕荣山, 等. 复杂炼油塔模拟的改进联立方程内外层算法 [J]. 化工学报, 2020, 71(05): 2118-2127. [4] Smith R, Ochoa-Estopier L M, Jobson M. The use of reduced models in the optimisation of energy integrated processes [J]. Chemical Engineering Transactions, 2013, 35: 139-144. [5] Zhao H, Rong G, Feng Y. Effective solution approach for integrated optimization models of refinery production and utility system [J]. Industrial & Engineering Chemistry Research, 2015, 54(37): 9238-9250. [6] Li J, Xiao X, Boukouvala F, et al. Data‐driven mathematical modeling and global optimization framework for entire petrochemical planning operations [J]. AIChE Journal, 2016, 62(9): 3020-3040. [7] Ketabchi E, Mechleri E, Arellano-Garcia H. Increasing operational efficiency through the integration of an oil refinery and an ethylene production plant [J]. Chemical Engineering Research and Design, 2019, 152: 85-94. [8] Mejdell T, Skogestad S. Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression[J]. Industrial & Engineering Chemistry Research, 1991, 30(12): 2543-2555. [9] Mahalec V, Hashim A, Sanchez Y. Hybrid Models for Monitoring & Optimization of Hydrocarbon Separation Equipment[C]//Proceedings of the 2nd Annual Gas Processing Symposium. Elsevier, 2010: 427-435. [10] BROOKS R W, WALSEM F D V, DRURY J. Choosing cutpoints to optimize product yields[J]. Hydrocarbon Processing, 1999, 78(11): 53-60. [11] ZHANG J, ZHU X X, TOWLER G P. A simultaneous optimization strategy for overall integration in refinery planning[J]. Industrial & Engineering Chemistry Research, 2001, 40(12): 2640-2653. [12] LI Wenkai, HUI Chiwai, LI Anxue. Integrating CDU, FCC and product blending models into refinery planning[J]. Computers & Chemical Engineering, 2005, 29(9): 2010-2028. [13] MENEZES Brenno C, KELLY Jeffrey D, GROSSMANN Ignacio E. Improved swing-cut modeling for planning and scheduling of oil-refinery distillation units[J]. Industrial & Engineering Chemistry Research, 2013, 52(51): 18324-18333. [14] ALATTAS Abdulrahman M, GROSSMANN Ignacio E, PALOU-RIVERA Ignasi. Integration of nonlinear crude distillation unit models in refinery planning optimization[J]. Industrial & Engineering Chemistry Research, 2011, 50(11): 6860-6870. [15] Lee D H, Lee E S, Park S J. Characterization of crude feed and products from operating conditions by using continuous probability functions and inferential models[J]. Korean Journal of Chemical Engineering, 2006, 23(4): 517-521. [16] Yao H, Chu J. Operational optimization of a simulated atmospheric distillation column using support vector regression models and information analysis [J]. Chemical Engineering Research and Design, 2012, 90(12): 2247-2261. [17] Ochoa-Estopier L M, Jobson M, Smith R. Operational optimization of crude oil distillation systems using artificial neural networks [J]. Computers & Chemical Engineering, 2013, 59: 178-185. [18] Mahalec V, Sanchez Y. Inferential monitoring and optimization of crude separation units via hybrid models [J]. Computers & Chemical Engineering, 2012, 45: 15-26. [19] Fu G, Sanchez Y, Mahalec V. Hybrid model for optimization of crude oil distillation units [J]. AIChE Journal, 2016, 62(4): 1065-1078. [20] Li F, Qian F, Yang M, et al. Product tri‐section based crude distillation unit model for refinery production planning and refinery optimization [J]. AIChE Journal, 2021, 67(2): e17115. [21] 周磊, 孙晓岩, 陶少辉, 等. 基于分离因数法的简捷炼油塔模型开发及应用 [J/OL]. 化工进展: 1-12. [22] Gutmann H-M. A radial basis function method for global optimization [J]. Journal of Global Optimization, 2001, 19(3): 201-227. [23] Gutmann H-M. On the semi-norm of radial basis function interpolants [J]. Journal of Approximation Theory, 2001, 111(2): 315-328. [24] Soares R d P, Secchi A R. EMSO: A new environment for modelling, simulation and optimisation [M]. Computer Aided Chemical Engineering. Elsevier. 2003: 947-952. [25] Van Baten J, Szczepanski R. A thermodynamic equilibrium reactor model as a CAPE-OPEN unit operation [J]. Computers & Chemical Engineering, 2011, 35(7): 1251-1256. [26] 李旭, 马力. VB6在CSV文件格式处理中的应用研究 [J]. 信息技术, 2009, 33(07): 26-28.