复杂工业过程智能优化决策系统的现状与展望

丁进良; 杨翠娥; 陈远东; 柴天佑; 丁进良; 杨翠娥; 陈远东; 柴天佑

doi:10.16383/j.aas.2018.c180550

[1]

柴天佑, 丁进良.流程工业智能优化制造.中国工程科学, 2018, DOI: 10.15302/J-SSCAE-2018.04.007

Chai Tian-You, Ding Jin-Liang. Intelligent optimized manufacturing in process industry. Chinese Engineering Science. 2018, DOI: 10.15302/J-SSCAE-2018.04.007

[2]

柴天佑, 丁进良, 桂卫华, 钱锋.大数据与制造流程知识自动化发展战略研究.北京:科学出版社, 2018.

Chai Tian-You, Ding Jin-Liang, Gui Wei-Hua, Qian Feng. Research on the development strategy of knowledge automation of big data and manufacturing process. Beijing:Science Press, 2018.

[3]

桂卫华, 王成红, 谢永芳, 宋苏, 孟庆峰, 丁进良.流程工业实现跨越式发展的必由之路.中国科学基金, 2015, 29(5):337-342 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZKJJ201505005&dbname=CJFD&dbcode=CJFQ

Gui Wei-Hua, Wang Cheng-Hong, Xie Yong-Fang, Song Su, Meng Qing-Feng, Ding Jin-Liang. The necessary way to realize great-leap-forward development of process industries. Bulletin of National Natural Science Foundation of China. 2015, 29(5):337-342 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZKJJ201505005&dbname=CJFD&dbcode=CJFQ

[4]

Qian F, Zhong W, Du W. Fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Engineering, 2017, 3(2):154-160 doi: 10.1016/J.ENG.2017.02.011

[5]

柴天佑.生产制造全流程优化控制对控制与优化理论方法的挑战.自动化学报, 2009, 35(6):641-649 http://www.aas.net.cn/CN/abstract/abstract18090.shtml

Chai Tian-You. The challenge of control and optimization theory method for production and manufacturing process optimization control. Acta Automatica Sinica, 2009, 35(6):641-649 http://www.aas.net.cn/CN/abstract/abstract18090.shtml

[6]

柴天佑, 丁进良, 王宏, 苏春翌.复杂工业过程运行的混合智能优化控制方法.自动化学报, 2008, 34(5):505-515 http://www.aas.net.cn/CN/abstract/abstract13476.shtml

Chai Tian-You, Ding Jin-Liang, Wang Hong, Su Chun-Yi. Hybrid intelligent optimization control for complex industrial processes. Acta Automatica Sinica, 2008, 34(5):505-515 http://www.aas.net.cn/CN/abstract/abstract13476.shtml

[7]

Young R E. Petroleum refining process control and realtime optimization. Control Systems IEEE, 1999, 26(6):73-83 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4019324

[8]

丁进良.动态环境下选矿生产全流程运行指标优化决策方法研究[博士学位论文], 东北大学, 中国, 2012.

Ding Jin-Liang. Study on optimization decision method of whole process operation index under dynamic environment[Ph. D. dissertation], Northeastern University, China, 2012.

[9]

柴天佑, 丁进良, 徐泉, 岳恒.基于物联网的选矿制造执行系统技术.物联网学报, 2018, (01):1-16 http://www.cnki.com.cn/Article/CJFDTOTAL-WLWX201801001.htm

Chai Tian-You, Ding Jin-Liang, Xu Quan, Yue Heng. Implementation system technology of mineral processing manufacturing based on Internet of things. Journal of Internet of Things, 2018, (01):1-16 http://www.cnki.com.cn/Article/CJFDTOTAL-WLWX201801001.htm

[10]

Chai T, Ding J, Yu G, Wang H. Integrated optimization for the automation systems of mineral processing. IEEE Transactions on Automation Science and Engineering, 2014, 11(4):965-982 doi: 10.1109/TASE.2014.2308576

[11]

柴天佑.复杂工业过程运行优化与反馈控制.自动化学报, 2013, 39(11):1744-1757 http://www.aas.net.cn/CN/abstract/abstract18214.shtml

Chai Tian-You. Operation optimization and feedback control of complex industrial processes. Acta Automatica Sinica, 2013, 39(11):1744-1757 http://www.aas.net.cn/CN/abstract/abstract18214.shtml

[12]

Mehmet M, Francis J. Doyle III. Real-time optimization of the pulp mill benchmark problem. Computers & Chemical Engineering, 2008, 32(4-5):789-804 http://www.sciencedirect.com/science/article/pii/S0098135407000646

[13]

王越.不确定环境下生产计划和调度的研究[博士学位论文], 浙江大学, 中国, 2016.

Wang Yue. Research on production planning and scheduling under uncertain environment[Ph. D. dissertation], Zhejiang University, China, 2016.

[14]

侯艳.炼油厂原油处理短期生产计划调度优化[博士学位论文], 广东工业大学, 中国, 2016.

Hou Yan. Optimization of short-term production scheduling for oil refinery[Ph. D. dissertation], Guangdong University of Technology, China, 2016.

[15]

赵小强.炼厂生产调度问题研究[博士学位论文], 浙江大学, 中国, 2005.

Zhao Xiao-Qiang. Research on refinery production scheduling problem[Ph. D. dissertation], Zhejiang University, China, 2005.

[16]

Velez S, Maravelias C T. Multiple and nonuniform time grids in discrete-time MIP models for chemical production scheduling. Computers & Chemical Engineering, 2013, 53(11):70-85. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=260c39cde3abd4fb8d102be40a1f4917

[17]

Bowman E H. The schedule-sequencing problem. Operations Research, 1959, 7(5):621-624 doi: 10.1287/opre.7.5.621

[18]

Manne A S. On the job-shop scheduling problem. Operations Research, 1960, 8(2):219-223 http://d.old.wanfangdata.com.cn/Periodical/jsjjczzxt201804007

[19]

Hussin S M, Hassan M Y. Coordination of short-term maintenance scheduling with hourly security-constrained unit commitment. In Proceedings of Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International. Langkawi, Malaysia. USA: IEEE, 2014. 73-78

[20]

Jia Z, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation. Computers & Chemical Engineering, 2004, 28(6):1001-1019. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=02576a3e73f9cf64c1e642899a8c7c70

[21]

Joly M, Moro L F L, Pinto J M. Planning and scheduling for petroleum refineries using mathematical programming. Brazilian Journal of Chemical Engineering, 2002, 19(2):207-228. doi: 10.1590/S0104-66322002000200008

[22]

Ezpeleta J, Colom J M, Martinez J. A Petri net based deadlock prevention policy for flexible manufacturing systems. IEEE Transactions on Robotics and Automation, 1995, Ⅱ(2):173-184. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=370500

[23]

Lin F J, Fung R F, Wang Y C. Sliding mode and fuzzy control of toggle mechanism using PM synchronous servomotor drive. IEE Proceedings-Control Theory and Applications, 1997, 144(5):393-402. doi: 10.1049/ip-cta:19971357

[24]

Balduzzi F, Giua A, Seatzu C. Modelling and simulation of manufacturing systems with first-order hybrid Petri nets. International Journal of Production Research, 2001, 39(2):255-282. doi: 10.1080/00207540010004278

[25]

Champagnat R, Esteban P, Pingaud H, Valette, R. Petri net based modeling of hybrid systems. Computers in industry, 1998, 36(1-2):139-146. doi: 10.1016/S0166-3615(97)00109-7

[26]

Allam M, Alla H. Modeling and simulation of an electronic component manufacturing system using hybrid Petri nets. IEEE Transactions on Semiconductor Manufacturing, 1998, 11(3):374-383. doi: 10.1109/66.705372

[27]

Chien C F, Chen C H. Using genetic algorithms (GA) and a coloured timed Petri net (CTPN) for modelling the optimization-based schedule generator of a generic production scheduling system. International Journal of Production Research, 2007, 45(8):1763-1789 doi: 10.1080/00207540500380981

[28]

Sadrieh S A, Ghaeli M, Bahri P A, Lee P L. An integrated Petri net and GA based approach for scheduling of hybrid plants. Computers in Industry, 2007, 58(6):519-530. doi: 10.1016/j.compind.2006.12.001

[29]

张劲松.基于受控混杂Petri网的连续过程生产调度建模及优化方法[博士学位论文], 山东大学, 中国, 2008.

Zhang Jin-Song. Modeling and optimization of continuous process production scheduling based on controlled hybrid Petri net[Ph. D. dissertation], Shandong University, China, 2008.

[30]

邬仲臻, 冯毅萍, 王继帅, 吴玉成, 荣冈.一种基于仿真的流程工业生产调度闭环优化方法.化工自动化及仪表, 2011, 38(04):369-374 doi: 10.3969/j.issn.1000-3932.2011.04.003

Wu Zhong-Zhen, Feng Yi-Ping, Wang Ji-Shuai, Wu Yu-Cheng, Rong Gang. A simulation based process industry production scheduling closed-loop optimization method. Control and Instruments in Chemical Industry, 2011, 38(04):369-374 doi: 10.3969/j.issn.1000-3932.2011.04.003

[31]

王伟达, 王伟, 刘文剑.基于仿真的生产计划与调度系统集成.计算机工程与设计, 2007(07):1626-1629 doi: 10.3969/j.issn.1000-7024.2007.07.044

Wang Wei-Da, Wang Wei, Liu Wen-Jian. Integration of simulation based production planning and scheduling system. Computer Engineering and Design, 2007(07):1626-1629 doi: 10.3969/j.issn.1000-7024.2007.07.044

[32]

Chryssolouris G, Papakostas N, Mourtzis D. refinery short-term scheduling with tank farm, inventory and distillation management:an integrated simulation-based approach. European Journal of Operational Research, 2005, 166(3):812-827. doi: 10.1016/j.ejor.2004.03.046

[33]

马文强, 杜子平, 李东坡.仿真优化在制造系统生产调度中的研究进展.现代制造工程, 2012, 15(03):10-14 doi: 10.3969/j.issn.1671-3133.2012.03.004

Ma Wen-Qiang, Du Zi-Ping, Li Dong-Po. Research progress of simulation optimization in manufacturing system production scheduling. Modern Manufacturing Engineering, 2012, 15(03):10-14 doi: 10.3969/j.issn.1671-3133.2012.03.004

[34]

Baker C T, Dzielinski B P. Simulation of a simplified job shop. Management Science, 1960, 6(3):311-323. doi: 10.1287/mnsc.6.3.311

[35]

黄辉, 柴天佑, 郑秉霖, 罗小川, 张红.面向铁钢对应的两级案例推理铁水动态调度系统.化工学报, 2010, 61(08):2021-2029 http://d.old.wanfangdata.com.cn/Periodical/hgxb201008024

Huang Hui, Chai Tian-You, Zheng Bing-Lin, Luo Xiao-Chuan, Zhang Hong. The two-level case reasoning iron-water dynamic scheduling system for iron-oriented steel. Journal of Chemical Engineering, 2010, 61(08):2021-2029 http://d.old.wanfangdata.com.cn/Periodical/hgxb201008024

[36]

Al-Khayyal F, Gri-n P M, Smith N R. Solution of a largescale two-stage decision and scheduling problem using decomposition. European Journal of Operational Research, 2001, 132(2):453-465 doi: 10.1016/S0377-2217(00)00138-7

[37]

Chen C L, Chen C L. Bottleneck-based heuristics to minimize total tardiness for the flexible flow line with unrelated parallel machines. Computers & Industrial Engineering, 2009, 56(4):1393-1401 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=504737ce05903e87e3dca3a997979696

[38]

战德臣, 陈伟, 王忠杰.基于启发式规则的混合遗传算法及其在生产计划优化中的应用.计算机工程与应用, 2003, (8):215-218 doi: 10.3321/j.issn:1002-8331.2003.08.071

Zhan De-Chen, Chen Wei, Wang Zhong-Jie. The hybrid genetic algorithm based on heuristic rules and its application in production planning optimization. Computer Engineering and Applications, 2003, (8):215-218 doi: 10.3321/j.issn:1002-8331.2003.08.071

[39]

肖志娇, 常会友, 衣杨.启发式规则与GA结合的优化方法求解工作流动态调度优化问题.计算机科学, 2007, 34(2):157-160 doi: 10.3969/j.issn.1002-137X.2007.02.039

Xiao Zhi-Jiao, Chang Hui-You, Yi Yang. Optimization of workflow dynamic scheduling by heuristic rules and GA combined optimization method. Computer Science, 2007, 34(2):157-160 doi: 10.3969/j.issn.1002-137X.2007.02.039

[40]

He Y, Hui C W. A rule-based genetic algorithm for the scheduling of single-stage multi-product batch plants with parallel units. Computers & Chemical Engineering, 2008, 32(12):3067-3083 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6588b9ed6b5af5cd7f66fb711ffb3321

[41]

Pistikopoulos E N. Uncertainty in process design and operations. Computers & Chemical Engineering, 1995, 19:553-563 http://www.sciencedirect.com/science/article/pii/0098135495870946

[42]

Ho J W, Fang C C. Production capacity planning for multiple products under uncertain demand conditions. International Journal of Production Economics, 2013, 141(2):593-604 doi: 10.1016/j.ijpe.2012.09.016

[43]

Ning Y, Liu J, Yan L. Uncertain aggregate production planning. Soft Computing, 2013, 17(4):617-624 doi: 10.1007/s00500-012-0931-4

[44]

Kórpeoĝlu E, Yaman H, Aktürk M S. A multi-stage stochastic programming approach in master production scheduling. European Journal of Operational Research, 2011, 213(1):166-179 doi: 10.1016/j.ejor.2011.02.032

[45]

Figueroa-García J C, Kalenatic D, Lopez-Bello C A. Multiperiod mixed production planning with uncertain demands:fuzzy and interval fuzzy sets approach. Fuzzy Sets and Systems, 2012, 206:21-38 doi: 10.1016/j.fss.2012.03.005

[46]

Torabi S A, Ebadian M, Tanha R. Fuzzy hierarchical production planning (with a case study). Fuzzy Sets and Systems, 2010, 161(11):1511-1529 doi: 10.1016/j.fss.2009.11.006

[47]

Guillaume R, Kobylański P, Zieliski P. Production planning with uncertain demands. In: Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ). Taipei, China: IEEE, 2011, 2644-2649

[48]

Iris C, Cevikcan E. A fuzzy linear programming approach for aggregate production planning. In: Proceedings of Supply Chain Management Under Fuzziness. Springer, Berlin, Heidelberg, 2014: 355-374

[49]

唐加福, 汪定伟, 许宝栋.多品种集约生产计划问题的模糊方法.管理科学学报, 2003, 6(01):44-50 doi: 10.3321/j.issn:1007-9807.2003.01.007

Tang Jia-Fu, Wang Ding-Wei, Xu Bao-Dong. The fuzzy method of multi-variety intensive production planning. Journal of Management, 2003, 6(01):44-50 doi: 10.3321/j.issn:1007-9807.2003.01.007

[50]

Aghezzaf E H, Sitompul C, Najid N M. Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & Operations Research, 2010, 37(5):880-889 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b102182eaf5704f56e72706b69b122a2

[51]

Leung S C H, Tsang S O S, Ng W L, Wu Y. A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 2007, 181(1):224-238 doi: 10.1016/j.ejor.2006.06.011

[52]

Rahmani D, Ramezanian R, Fattahi P, Heydari M. A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 2013, 37(20-21):8957-8971 doi: 10.1016/j.apm.2013.04.016

[53]

罗春鹏, 荣冈.不确定条件下汽油调和调度的鲁棒优化模型(英文).石油学报(石油加工), 2009, 25(03):391-400 doi: 10.3969/j.issn.1001-8719.2009.03.015

Luo Chun-Peng, Rong Gang. Robust optimization model for gasoline harmonic scheduling under uncertain conditions. Journal of Petroleum (Petroleum Processing), 2009, 25(03):391-400 doi: 10.3969/j.issn.1001-8719.2009.03.015

[54]

Al-e S M J M, Aryanezhad M B, Sadjadi S J. An Efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. The International Journal of Advanced Manufacturing Technology, 2012, 58(5-8):765-782 doi: 10.1007/s00170-011-3396-1

[55]

Lan Y, Zhao R, Tang W. Minimum risk criterion for uncertain production planning problems. Computers & Industrial Engineering, 2011, 61(3):591-599 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c0e81920bea65c0208c3262ff9dfe5cb

[56]

李歧强, 李明, 张平.基于事件逻辑的炼油企业动态调度系统.同济大学学报(自然科学版), 2010, 38(12):1836-1840 doi: 10.3969/j.issn.0253-374x.2010.12.024

Li Qi-Qiang, Li Ming, Zhang Ping. Dynamic scheduling system based on event logic. Journal of Tongji University (Natural Science Edition), 2010, 38(12):1836-1840 doi: 10.3969/j.issn.0253-374x.2010.12.024

[57]

徐佳东.基于Petri网的原油调度问题研究[博士学位论文], 浙江大学, 中国, 2014.

Xu Jia-Dong. Research on crude oil scheduling based on Petri net[Ph. D. dissertation], Zhejiang University, China, 2014.

[58]

Ding J L, Yang C E, Xiao Q, Chai T Y, Jin Y C. Dynamic evolutionary multiobjective optimization for raw ore allocation in mineral processing. IEEE Transactions on Emerging Topics in Computational Intelligence, to be published.

[59]

Khoshnevis B, Chen Q M. Integration of process planning and scheduling functions. Journal of Intelligent Manufacturing, 1991, 2(3):165-175 doi: 10.1007/BF01471363

[60]

Shao X, Li X, Gao L, Zhang C. Integration of process planning and scheduling-a modified genetic algorithm-based approach. Computers & Operations Research, 2009, 36(6):2082-2096 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2d1fd30fce56f1cadca2699ba6c75824

[61]

Chunpeng L, Gang R. A strategy for the integration of production planning and scheduling in refineries under uncertainty. Chinese Journal of Chemical Engineering, 2009, 17(1):113-127 doi: 10.1016/S1004-9541(09)60042-2

[62]

Yu G, Chai T, Luo X. Two-level production plan decomposition based on a hybrid MOEA for mineral processing. IEEE Transactions on Automation Science and Engineering, 2013, 10(4):1050-1071 doi: 10.1109/TASE.2012.2221458

[63]

Chu Y, You F, Wassick J M, Agarwal A. Integrated planning and scheduling under production uncertainties:bilevel model formulation and hybrid solution method. Computers & Chemical Engineering, 2015, 72:255-272 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0234591599/

[64]

Yu M, Zhang Y, Chen K, Zhang D. Integration of process planning and scheduling using a hybrid GA/PSO algorithm. The International Journal of Advanced Manufacturing Technology, 2015, 78(1-4):583-592 doi: 10.1007/s00170-014-6669-7

[65]

Baldea M, Harjunkoski I. Integrated production scheduling and process control:a systematic review. Computers & Chemical Engineering, 2014, 71:377-390 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0234501781/

[66]

Dias L S, Ierapetritou M G. Integration of scheduling and control under uncertainties:review and challenges. Chemical Engineering Research and Design, 2016, 116:98-113 doi: 10.1016/j.cherd.2016.10.047

[67]

高小永, 江永亨, 黄德先.基于装置级优化控制与厂级调度优化集成的过程模型方法.化工学报, 2016, 67(12):5105-5111 http://d.old.wanfangdata.com.cn/Periodical/hgxb201612025

Gao Xiao-Yong, Jiang Yong-Heng, Huang De-Xian. Process modelling based on integration of unitwide optimal process control and plantwide scheduling. CIESC Journal, 2016, 67(12):5105-5111 http://d.old.wanfangdata.com.cn/Periodical/hgxb201612025

[68]

Flores-Tlacuahuac A, Grossmann I E. Simultaneous cyclic scheduling and control of a multiproduct CSTR. Industrial & Engineering Chemistry Research, 2006, 45(20):6698-6712 doi: 10.1021/ie051293d

[69]

Zhuge J, Ierapetritou M G. Integration of scheduling and control with closed loop implementation. Industrial & Engineering Chemistry Research, 2012, 51(25):8550-8565 doi: 10.1021/ie3002364

[70]

Skogestad S. Control structure design for complete chemical plants. Computers & Chemical Engineering, 2004, 28(1-2):219-234 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025121846/

[71]

Ellis M, Christofides P D. Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems. Control Engineering Practice, 2014, 22(22):242-251 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d5cab633219a01a66ce7e1ded2d1b270

[72]

Baldea M, Du J, Park J, Harjunkoski I. Integrated production scheduling and model predictive control of continuous processes. AIChE Journal, 2015, 61(12):4179-4190 doi: 10.1002/aic.v61.12

[73]

Ellis M, Durand H, Christofides P D. A tutorial review of economic model predictive control methods. Journal of Process Control, 2014, 24(8):1156-1178 doi: 10.1016/j.jprocont.2014.03.010

[74]

Jschke J, Skogestad S. NCO tracking and self-optimizing control in the context of real-time optimization. Journal of Process Control, 2011, 21(10):1407-1416 doi: 10.1016/j.jprocont.2011.07.001

[75]

Skogestad S. Self-optimizing control: the missing link between steady-state optimization and control, Computers and Chemical Engineering, 2000, 24(2): 569-575

[76]

Skogestad S. Plantwide control:the search for the self-optimizing control structure. Journal of Process Control, 2000, 10(5):487-507 doi: 10.1016/S0959-1524(00)00023-8

[77]

De Souza G, Odloak D, Zanin A C. Real time optimization (RTO) with model predictive control (MPC). Computers & Chemical Engineering, 2010, 34(12):1999-2006 http://www.sciencedirect.com/science/article/pii/S0098135410002498

[78]

Marchetti A G, Ferramosca A, González A H. Steady-state target optimization designs for integrating real-time optimization and model predictive control. Journal of Process Control, 2014, 24(1):129-145 doi: 10.1016/j.jprocont.2013.11.004

[79]

Biegler L T, Yang X, Fischer G A G. Advances in sensitivity-based nonlinear model predictive control and dynamic real-time optimization. Journal of Process Control, 2015, 30:104-116 doi: 10.1016/j.jprocont.2015.02.001

[80]

Tosukhowong T, Lee J H. Real-time economic optimization for an integrated plant via a dynamic optimization scheme In:Proceedings of American Control Conference. Boston, Massachusetts:IEEE, 2004:233-238 https://www.sciencedirect.com/science/article/pii/S0959152415000281

[81]

Vega P, Revollar S, Francisco M, Francisco S M. Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Computers & Chemical Engineering, 2014, 68:78-95 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=5d777f5a02748ec3fa53532626dd6edc

[82]

Zhu G Y, Henson M A, Ogunnaike B A. A hybrid model predictive control strategy for nonlinear plant-wide control. Journal of process control, 2000, 10(5):449-458 doi: 10.1016/S0959-1524(00)00020-2

[83]

Zheng Y, Li S, Wang X. Distributed model predictive control for plant-wide hot-rolled strip laminar cooling process. Journal of Process Control, 2009, 19(9):1427-1437 doi: 10.1016/j.jprocont.2009.04.012

[84]

Fan J, Jiang Y, Chai T. MPC-based setpoint compensation with unreliable wireless communications and constrained operational conditions. Neurocomputing, 2017, 270:110-121 doi: 10.1016/j.neucom.2016.10.098

[85]

Xie S, Xie Y, Ying H, Gui W, Yang C. A hybrid control strategy for real-time control of the iron removal process of the zinc hydrometallurgy plants. IEEE Transactions on Industrial Informatics, 2018. http://ieeexplore.ieee.org/document/8315484/

[86]

Precup R E, Hellendoorn H. A survey on industrial applications of fuzzy control. Computers in Industry, 2011, 62(3):213-226 doi: 10.1016/j.compind.2010.10.001

[87]

Chai T, Ding J, Wu F. Hybrid intelligent control for optimal operation of shaft furnace roasting process. Control Engineering Practice, 2011, 19(3):264-275 doi: 10.1016/j.conengprac.2010.05.002

[88]

Chai T Y, Qin S J, Wang H. Optimal operational control for complex industrial processes. Annual Reviews in Control, 2014, 38(1):81-92 doi: 10.1016/j.arcontrol.2014.03.005

[89]

Ding J, Chen Q, Chai T, Wang H, Su C Y. Data mining based feedback regulation in operation of hematite ore mineral processing plant. In: Proceedings of American Control Conference, 2009. ACC'09. IEEE, 2009: 907-912

[90]

周平, 柴天佑.典型赤铁矿磨矿过程智能运行反馈控制.控制理论与应用, 2014, 31(10):1352-1359 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201410008

Zhou P, Chai T Y. Intelligent operational feedback control for typical hematite grinding processes. Control Theory & Applications, 2014, 31(10):1352-1359 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201410008

[91]

Wu Z, Wu Y, Chai T Y, Sun J. Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace. IEEE Transactions on Industrial Electronics, 2014, 62(3):1703-1715 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=494609c5ba6af9ad52aebe7610001ff8

[92]

Jiang Y, Fan J, Chai T, Li J, Lewis F L. Data-driven flotation industrial process operational optimal control based on reinforcement learning. IEEE Transactions on Industrial Informatics, 2018, 14(5):1974-1989 doi: 10.1109/TII.2017.2761852

[93]

李金娜, 高溪泽, 柴天佑, 范家璐.数据驱动的工业过程运行优化控制.控制理论与应用, 2016, 33(12):1584-1592 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201612005

Li Jin-Na, Gao Xi-Ze, Chai Tian-You, Fan Jia-Lu. Data driven industrial process operation optimization control. Control Theory & Applications, 2016, 33(12):1584-1592 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201612005

[94]

Yu G, Chai T, Luo X. Multiobjective production planning optimization using hybrid evolutionary algorithms for mineral processing. IEEE Transactions on Evolutionary Computation, 2011, 15(4):487-514 doi: 10.1109/TEVC.2010.2073472

[95]

Ding J, Chai T, Wang H, Wang J, Zheng X. An intelligent factory-wide optimal operation system for continuous production process. Enterprise Information Systems, 2016, 10(3):286-302 doi: 10.1080/17517575.2015.1065346

[96]

Ding J, Modares H, Chai T, Lewis F L. Data-based multiobjective plant-wide performance optimization of industrial processes under dynamic environments. IEEE Transactions on Industrial Informatics, 2016, 12(2):454-465 doi: 10.1109/TII.2016.2516973

[97]

Chai T, Ding J, Wang H. Multi-objective hybrid intelligent optimization of operational indices for industrial processes and application. IFAC Proceedings Vol. 2011, 2011, 44(1): 10517-10522

[98]

Ding J, Yang C, Chai T. Recent progress on data-based optimization for mineral processing plants. Engineering, 2017, 3(2):183-187 doi: 10.1016/J.ENG.2017.02.015

[99]

Chai T, Ding J, Wang H. Multi-objective hybrid intelligent optimization of operational indices for industrial processes and application. In: Proceedings of IFAC World Congress, Milan, Italy: 2011, 10517-10522

[100]

Yang C, Ding J. Constrained dynamic multi-objective evolutionary optimization for operational indices of beneficiation process. Journal of Intelligent Manufacturing, 2017, DOI: 10.1007/s10845-017-1319-1

[101]

Ding J, Chai T, Wang H. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization. IEEE Transactions on Neural Networks, 2011, 22(3):408-419 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dee3bbb4f85106750ddc4f36db4c4039

[102]

Ding J, Chai T, Cheng W, Zheng X. Data-based multiple-model prediction of the production rate for hematite ore beneficiation process. Control Engineering Practice, 2015, 45:219-229 doi: 10.1016/j.conengprac.2015.08.015

[103]

Liu C, Ding J, Toprac A J, Chai T. Data-based adaptive online prediction model for plant-wide production indices. Knowledge and Information Systems, 2014, 41(2):401-421 doi: 10.1007/s10115-014-0757-8

[104]

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). Hailar, China: IEEE, 2014. 1172-1175

[105]

Ding J, Zhao L, Liu C, Chai T. GA-based principal component selection for production performance estimation in mineral processing. Computers & Electrical Engineering, 2014, 40(5):1447-1459 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=169abd78c4f21ccf36b4647891bd1c15

[106]

Ding J, Chai T, Wang H, Chen X. Knowledge-based global operation of mineral processing under uncertainty. IEEE Transactions on Industrial Informatics, 2012, 8(4):849-859 doi: 10.1109/TII.2012.2205394