期刊首页 优先出版 当期阅读 过刊浏览 作者中心 关于期刊 English

《工程(英文)》 >> 2017年 第3卷 第2期 doi: 10.1016/J.ENG.2017.02.012

炼油和石化行业的智能制造

a Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
b China Petroleum and Chemical Corporation Jiujiang Company, Jiujiang 332004, China

收稿日期: 2016-12-20 修回日期: 2017-03-09 录用日期: 2017-03-10 发布日期: 2017-03-22

下一篇 上一篇

摘要

智能制造将把炼油及石化行业转化成为一个相互关联的、信息驱动的行业链。通过应用实时和高位值支持系统,智能制造能够实现协调运作、绩效导向的制造企业,该企业可迅速回应客户需求,并且将能源和材料的消耗最小化。同时,智能制造还能从根本上改善企业的可持续发展能力、生产力、创新力和经济竞争力。本文展示了石化行业内称为“智能制造”的几个应用案例,如由大数据驱动的催化裂化装置故障检测、对炼油厂现场的规划和调度进行优化等。智能制造在化工和石化领域进一步发展中的关键科技因素和挑战已经明确。

图片

图1

图2

图3

参考文献

[ 1 ] Ding J, Chai T, Wang H. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization. IEEE Trans Neural Netw 2011;22(3):408–19 链接1 链接2

[ 2 ] Jäschke J, Skogestad S. NCO tracking and self-optimizing control in the context of real-time optimization. J Process Contr 2011;21(10):1407–16 链接1 链接2

[ 3 ] Würth L, Hannemann R, Marquardt W. A two-layer architecture for economically optimal process control and operation. J Process Contr 2011;21(3):311–21 链接1 链接2

[ 4 ] Engell S. Feedback control for optimal process operation. IFAC Proc Vol 2006; 39(2):13–26 链接1 链接2

[ 5 ] Mercangöz M, Doyle FJ III. Real-time optimization of the pulp mill benchmark problem. Comput Chem Eng 2008;32(4–5):789–804 链接1 链接2

[ 6 ] Adetola V, Guay M. Integration of real-time optimization and model predictive control. J Process Contr 2010;20(2):125–33 链接1 链接2

[ 7 ] []Qin SJ, Cherry G, Good R, Wang J, Harrison CA. Semiconductor manufacturing process control and monitoring: A fab-wide framework. J Process Contr 2006;16(3):179–91 链接1 链接2

[ 8 ] Bartusiak RD. NLMPC: A platform for optimal control of feed- or product-flexible manufacturing. In: Findeisen R, Allg?wer F, Biegler LT, editors Assessment and future directions of nonlinear model predictive control. Berlin: Springer; 2007. p. 367–81 链接1 链接2

[ 9 ] Yu G, Chai T, Luo X. Multiobjective production planning optimization using hybrid evolutionary algorithms for mineral processing. IEEE Trans Evol Comput 2011;15(4):487–514 链接1 链接2

[10] Kong W, Ding J, Chai T, Zheng X, Yang S. A multiobjective particle swarm optimization algorithm for load scheduling in electric smelting furnaces. In: Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES); 2013Apr 16–19; Piscataway: IEEE; 2013. p. 188–95 链接1 链接2

[11] Yu G, Chai T, Luo X. Two-level production plan decomposition based on a hybrid MOEA for mineral processing. IEEE Trans Autom Sci Eng 2013;10(4):1050–71 链接1 链接2

[12] Chai T, Ding J, Yu G, Wang H. Integrated optimization for the automation systems of mineral processing. IEEE Trans Autom Sci Eng 2014;11(4):965–82 链接1 链接2

[13] Marchetti AG, Ferramosca A, González AH. Steady-state target optimization designs for integrating real-time optimization and model predictive control. J Process Contr 2014;24(1):129–45 链接1 链接2

[14] Chachuat B, Srinivasan B, Bonvin D. Adaptation strategies for real-time optimization. Comput Chem Eng 2009;33(10):1557–67 链接1 链接2

[15] Chen VYX. A 0–1 goal programming model for scheduling multiple maintenance projects at a copper mine. Eur J Oper Res 1994;76(1):176–91 链接1 链接2

[16] Bevilacqua M, Ciarapica FE, Giacchetta G. Critical chain and risk analysis applied to high-risk industry maintenance: A case study. Int J Proj Manag 2009;27(4):419–32 链接1 链接2

[17] Kumral M. Genetic algorithms for optimization of a mine system under uncertainty. Prod Plann Contr 2004;15(1):34–41 链接1 链接2

[18] Cisternas LA, Gálvez ED, Zavala MF, Magna J. A MILP model for the design of mineral flotation circuits. Int J Miner Process 2004;74(1–4):121–31 链接1 链接2

[19] Li Z, Ierapetritou M. Process scheduling under uncertainty: Review and challenges. Comput Chem Eng 2008;32(4–5):715–27 链接1 链接2

[20] Pinto JM, Grossmann IE. Assignment and sequencing models for the scheduling of process systems. Ann Oper Res 1998;81:433–66 链接1 链接2

[21] Chai T, Ding J, Wang H. Multi-objective hybrid intelligent optimization of operational indices for industrial processes and application. IFAC Proc Vol 2011; 44(1):10517–22 链接1 链接2

[22] Ding J, Chai T, Wang H, Wang J, Zheng X. An intelligent factory-wide optimal operation system for continuous production process. Enterprise Inf Syst 2016;10(3):286–302 链接1 链接2

[23] Ding J, Wang H, Liu C, Chai T. A multiobjective operational optimization approach for iron ore beneficiation process. In: Proceedings of the 2013 International Conference on Advanced Mechatronic Systems; 2013 Sep 25–27; Luoyang, China.Piscataway: IEEE; 2013. p. 582–7 链接1 链接2

[24] Ding J, Modares H, Chai T, Lewis FL. Data-based multiobjective plant-wide performance optimization of industrial processes under dynamic environments. IEEE Trans Industr Inform 2016;12(2):454–65 链接1 链接2

[25] Yang C, Ding J. Constraint dynamic multi-objective evolutionary optimization for operational indices of beneficiation process. J Intell Manuf. In press.

[26] Ma Y, Sun Z, Gao H. Incremental associate data mining in real time database. J Comput Res Develop 2000;37(12):1446–51. Chinese.

[27] Ding J, Chai T, Cheng W, Zheng X. Data-based multiple-model prediction of the production rate for hematite ore beneficiation process. Control Eng Pract 2015;45:219–29 链接1 链接2

[28] Liu C, Ding J, Toprac AJ, Chai T. Data-based adaptive online prediction model for plant-wide production indices. Knowl Inf Syst 2014;41(2):401–21 链接1 链接2

[29] Liu C, Ding J, Chai T. Robust prediction for quality of industrial processes. In: Proceedings of the 2014 IEEE International Conference on Information and Automation (ICIA); 2014 Jul 28–30; Hailar, China.Piscataway: IEEE; 2014. p. 1172–5 链接1 链接2

[30] Ding J, Chai T, Wang H, Chen X. Knowledge-based global operation of mineral processing under uncertainty. IEEE Trans Industr Inform 2012;8(4):849–59 链接1 链接2

[31] Chai T, Qin SJ, Wang H. Optimal operational control for complex industrial processes. Annu Rev Contr 2014;38(1):81–92 链接1 链接2

[32] Chai T, Ding J, Wu F. Hybrid intelligent control for optimal operation of shaft furnace process. Cont Eng Pract 2011;19(3):264–75 链接1 链接2

[33] Zhou P, Chai T, Sun J. Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system. IEEE Trans Contr Syst Technol 2013;21(1):162–75 链接1 链接2

[34] Zhou P, Lu S, Yuan M, Chai T. Survey on higher-level advanced control for grinding circuits operation. Powder Technol 2016;288:324–38 链接1 链接2

相关研究