[1]
|
中国石油和化学工业联合会. 2016年中国石油和化工行业经济运行报告.中国石油和化工, 2017, 2017(3): 64-68 http://www.cnki.com.cn/Article/CJFDTOTAL-SYGD201702001.htmChina Petroleum and Chemical Industry Federation. 2016 China petroleum and chemical industries economy and operation report. China Petroleum and Chemical Industry, 2017, 2017(3): 64-68 http://www.cnki.com.cn/Article/CJFDTOTAL-SYGD201702001.htm
|
[2]
|
Qian F, Zhong W M, Du W L. 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
|
[3]
|
杨继刚. "智能制造+"石化行业, 打造中国石化行业升级版.中国工业评论, 2016, (6): 79 http://www.cnki.com.cn/Article/CJFDTOTAL-GYPL201606013.htmYang Ji-Gang. Smart manufacturing plus petroleum and chemical industries, upgrading China petroleum and chemical industries. China Industry Review, 2016, (6): 79 http://www.cnki.com.cn/Article/CJFDTOTAL-GYPL201606013.htm
|
[4]
|
覃伟中.积极推进智能制造是传统石化企业提质增效转型升级的有效途径.当代石油石化, 2016, 24(6): 1-4 http://www.cnki.com.cn/Article/CJFDTOTAL-SYGD201606001.htmQin Wei-Zhong. Intelligent process manufacturing—an efficient way to upgrade traditional refineries. Petroleum & Petrochemical Today, 2016, 24(6): 1-4 http://www.cnki.com.cn/Article/CJFDTOTAL-SYGD201606001.htm
|
[5]
|
曾天舒.九江石化:入选工信部智能制造试点.中国石油和化工, 2015, (8): 32 http://www.cnki.com.cn/Article/CJFDTOTAL-SYFG201508023.htmZeng Tian-Shu. Jiujiang petrochemical: selected as ministry of industry and information technology smart manufacturing pilot project. China Petroleum and Chemical Industries, 2015, (8): 32 http://www.cnki.com.cn/Article/CJFDTOTAL-SYFG201508023.htm
|
[6]
|
李德芳, 索寒生.加快智能工厂进程, 促进生态文明建设.化工学报, 2014, 65(2): 374-380 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201402002.htmLi De-Fang, Suo Han-Sheng. Accelerate the process of smart plant, promote ecological civilization construction. CIESC Journal, 2014, 65(2): 374-380 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201402002.htm
|
[7]
|
王基铭.我国石化产业面临的挑战及对策建议.当代石油石化, 2015, 23(11): 1-7 doi: 10.3969/j.issn.1009-6809.2015.11.001Wang Ji-Ming. Challenges facing China's petrochemical industry and their countermeasure suggestions. Petroleum & Petrochemical Today, 2015, 23(11): 1-7 doi: 10.3969/j.issn.1009-6809.2015.11.001
|
[8]
|
桂卫华, 阳春华, 陈晓方, 王雅琳.有色冶金过程建模与优化的若干问题及挑战.自动化学报, 2013, 39(3): 197-207 http://www.aas.net.cn/CN/abstract/abstract17799.shtmlGui Wei-Hua, Yang Chun-Hua, Chen Xiao-Fang, Wang Ya-Lin. Modeling and optimization problems and challenges arising in nonferrous metallurgical process. Acta Automatica Sinica, 2013, 39(3): 197-207 http://www.aas.net.cn/CN/abstract/abstract17799.shtml
|
[9]
|
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.shtmlChai Tian-You. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Automatica Sinica, 2009, 35(6): 641-649 http://www.aas.net.cn/CN/abstract/abstract18090.shtml
|
[10]
|
Zhang Y, Qian F, Zhang Y, Schietekat C M, van Geem K M, Guy, Marin G B. Impact of flue gas radiative properties and burner geometry in furnace simulations. AIChE Journal, 2015, 61(3): 936-954 doi: 10.1002/aic.v61.3
|
[11]
|
Wei M, Yang M L, Qian F, Du W L, Zhong W M. Integrated dual-production mode modeling and multiobjective optimization of an industrial continuous catalytic naphtha reforming process. Industrial & Engineering Chemistry Research, 2016, 55(19): 5714-5725
|
[12]
|
Joseph B, Brosilow C B. Inferential control of process: Part Ⅰ. steady state analysis and design. AIChE Journal, 1978, 24(3): 485-492 doi: 10.1002/(ISSN)1547-5905
|
[13]
|
Zhou P, Lu S W, Chai T Y. Data-driven soft-sensor modeling for product quality estimation using case-based reasoning and fuzzy-similarity rough sets. IEEE Transactions on Automation Science and Engineering, 2014, 11(4): 992-1003 doi: 10.1109/TASE.2013.2288279
|
[14]
|
Dote Y, Ovaska S J. Industrial applications of soft computing: a review. Proceedings of the IEEE, 2001, 89(9): 1243-1265 doi: 10.1109/5.949483
|
[15]
|
Gao X Y, Shang C, Jiang Y H, Huang D X, Chen T. Refinery scheduling with varying crude: a deep belief network classification and multimodel approach. AIChE Journal, 2014, 60(7): 2525-2532 doi: 10.1002/aic.v60.7
|
[16]
|
栾郭宏, 贺凯迅, 程辉, 钱锋.基于神经网络的近红外光谱辛烷值模型的研究及应用.计算机与应用化学, 2014, 31(1): 63-68 http://www.cnki.com.cn/Article/CJFDTOTAL-JSYH201401014.htmLuan Guo-Hong, He Kai-Xun, Cheng Hui, Qian Feng. Octane model based on neural network by near-infrared spectroscopy and its application. Computers and Applied Chemistry, 2014, 31(1): 63-68 http://www.cnki.com.cn/Article/CJFDTOTAL-JSYH201401014.htm
|
[17]
|
孙帆, 钱锋.乙二醇生产过程中环氧乙烷浓度的软测量研究.计算机与应用化学, 2010, 27(1): 6-10 http://www.cnki.com.cn/Article/CJFDTOTAL-JSYH201001003.htmSun Fan, Qian Feng. The soft-sensing measurement of ethylene oxide concentration in ethylene glycol production process. Computers and Applied Chemistry, 2010, 27(1): 6-10 http://www.cnki.com.cn/Article/CJFDTOTAL-JSYH201001003.htm
|
[18]
|
李智. 对苯二甲酸加氢精制过程建模、控制与监控研究[博士学位论文], 华东理工大学, 中国, 2017Li Zhi. Research on modeling, control and process monitoring for industrial terephthalic acid hydropurification process[Ph.D. dissertation], East China University of Science and Technology, China, 2017
|
[19]
|
赵恒平, 俞金寿.化工数据预处理及其在建模中的应用.华东理工大学学报(自然科学版), 2005, 31(2): 223-226 http://www.cnki.com.cn/Article/CJFDTOTAL-HLDX200502022.htmZhao Heng-Ping, Yu Jin-Shou. Chemical data pretreatment and its application in modeling. Journal of East China University of Science and Technology (Natural Science Edition), 2005, 31(2): 223-226 http://www.cnki.com.cn/Article/CJFDTOTAL-HLDX200502022.htm
|
[20]
|
张子羿, 胡益, 侍洪波.一种基于聚类方法的多阶段间歇过程监控方法.化工学报, 2013, 64(12): 4522-4528 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201312038.htmZhang Zi-Yi, Hu Yi, Shi Hong-Bo. Multi-stage batch process monitoring based on a clustering method. CIESC Journal, 2013, 64(12): 4522-4528 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201312038.htm
|
[21]
|
Jin Y K, Li J L, Du W L, Qian F. Adaptive sampling for surrogate modelling with artificial neural network and its application in an industrial cracking furnace. The Canadian Journal of Chemical Engineering, 2016, 94(2): 262-272 doi: 10.1002/cjce.v94.2
|
[22]
|
王海宁, 夏陆岳, 周猛飞, 朱鹏飞, 潘海天.过程工业软测量中的多模型融合建模方法.化工进展, 2014, 33(12): 3157-3163 http://www.cnki.com.cn/Article/CJFDTOTAL-HGJZ201412006.htmWang Hai-Ning, Xia Lu-Yue, Zhou Meng-Fei, Zhu Peng-Fei, Pan Hai-Tian. Multi-model fusion modeling method for process industries soft sensor. Chemical Industry and Engineering Progress, 2014, 33(12): 3157-3163 http://www.cnki.com.cn/Article/CJFDTOTAL-HGJZ201412006.htm
|
[23]
|
Chen T, Ren J H. Bagging for Gaussian process regression. Neurocomputing, 2009, 72(7-9): 1605-1610 doi: 10.1016/j.neucom.2008.09.002
|
[24]
|
陈贵华, 王昕, 王振雷, 钱锋.基于模糊核聚类的乙烯裂解深度DE-LSSVM多模型建模.化工学报, 2012, 63(6): 1790-1796 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201206021.htmChen Gui-Hua, Wang Xin, Wang Zhen-Lei, Qian Feng. Multiple DE-LSSVM modeling of ethylene cracking severity based on fuzzy kernel clustering. CIESC Journal, 2012, 63(6): 1790-1796 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201206021.htm
|
[25]
|
Qian F, Tao L L, Sun W Z, Du W L. Development of a free radical kinetic model for industrial oxidation of p-xylene based on artificial neural network and adaptive immune genetic algorithm. Industrial & Engineering Chemistry Research, 2012, 51(8): 3229-3237
|
[26]
|
段斌, 梁军, 费正顺, 杨敏, 胡斌.基于GA-ANN的非线性半参数建模方法.浙江大学学报工学版, 2011, 45(6): 977-983 http://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201106002.htmDuan Bin, Liang Jun, Fei Zheng-Shun, Yang Min, Hu Bin. Nonlinear semi-parametric modeling method based on GA-ANN. Journal of Zhejiang University (Engineering Science), 2011, 45(6): 977-983 http://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201106002.htm
|
[27]
|
席裕庚, 李德伟, 林姝.模型预测控制——现状与挑战.自动化学报, 2013, 39(3): 222-236 http://www.aas.net.cn/CN/abstract/abstract17874.shtmlXi Yu-Geng, Li De-Wei, Lin Shu. Model predictive control——status and challenges. Acta Automatica Sinica, 2013, 39(3): 222-236 http://www.aas.net.cn/CN/abstract/abstract17874.shtml
|
[28]
|
王晓强. 化工过程实时优化与预测控制集成研究[博士学位论文], 华东理工大学, 中国, 2017Wang Xiao-Qiang. Research on integration of real-time optimization and predictive control for chemical processes[Ph.D. dissertation], East China University of Science and Technology, China, 2017
|
[29]
|
Zanin A C, de Gouvêa M T, Odloak D. Integrating real-time optimization into the model predictive controller of the FCC system. Control Engineering Practice, 2002, 10(8): 819-831 doi: 10.1016/S0967-0661(02)00033-3
|
[30]
|
Adetola V, Guay M. Integration of real-time optimization and model predictive control. Journal of Process Control, 2010, 20(2): 125-133 doi: 10.1016/j.jprocont.2009.09.001
|
[31]
|
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
|
[32]
|
Lawryńczuk M, Marusak P M, Tatjewski P. Cooperation of model predictive control with steady-state economic optimisation. Control and Cybernetics, 2008, 37(1): 133-158
|
[33]
|
Wang X, Mahalec V, Li Z, Qian F. Real-time optimization and control of an industrial Ethylbenzene Dehydrogenation process. Chemical Engineering Transactions, to be published.
|
[34]
|
Kadam J V, Marquardt W, Schlegel M, Backx T, Bosgra O H, Brouwer P J, Dünnebier G, van Hessem D, Tiagounov A, de Wolf S. Towards integrated dynamic real-time optimization and control of industrial processes. In: Proceedings of Foundations of Computer-Aided Process Operations (FOCAPO2003). Florida, USA: FOCAPO, 2003. 593-596
|
[35]
|
Kadam J V, Marquardt W. Integration of Economical Optimization and Control for Intentionally Transient Process Operation. Berlin Heidelberg: Springer, 2007.
|
[36]
|
Wang X Q, Mahalec V, Qian F. Globally optimal dynamic real time optimization without model mismatch between optimization and control layer. Computers & Chemical Engineering, 2017, 104: 64-75
|
[37]
|
Castro P M. Normalized multiparametric disaggregation: an efficient relaxation for mixed-integer bilinear problems. Journal of Global Optimization, 2016, 64(4): 765-784 doi: 10.1007/s10898-015-0342-z
|
[38]
|
吉林石化乙烯装置节能创新示范项目通过国家验收. 乙醛醋酸化工, 2015, (6): 46-47The project of energy saving innovation demonstration project in Jilin petrochemical plant is approved by the state. Fine Chemical Industrial Raw Materials & Intermediates, 2015, (6): 46-47
|
[39]
|
Reibstein D J, Gatignon H. Optimal product line pricing: the influence of elasticities and cross-elasticities. Journal of Marketing Research, 1984, 21(3): 259-267 doi: 10.2307/3151602
|
[40]
|
张雪宁, 梁唯溪.企业多产品多目标的价格决策.武汉理工大学学报(信息与管理工程版), 2004, 26(5): 162-165 http://www.cnki.com.cn/Article/CJFDTOTAL-WHQC200405044.htmZhang Xue-Ning, Liang Wei-Xi. Price decision of multi-products for multi-targets. Journal of Wuhan University of Technology (Information & Management Engineering), 2004, 26(5): 162-165 http://www.cnki.com.cn/Article/CJFDTOTAL-WHQC200405044.htm
|
[41]
|
赵江安.基于买方市场环境下的价格决策.统计与决策, 2004, (12): 53-55 doi: 10.3969/j.issn.1002-6487.2004.12.031Zhao Jiang-An. Price decision based on the buyer's market environment. Statistics and Decision, 2004, (12): 53-55 doi: 10.3969/j.issn.1002-6487.2004.12.031
|
[42]
|
Prasad A, Sethi S P. Competitive advertising under uncertainty: a stochastic differential game approach. Journal of Optimization Theory and Applications, 2004, 123(1): 163-185 doi: 10.1023/B:JOTA.0000043996.62867.20
|
[43]
|
Sinitsyn M. Technical note-price promotions in asymmetric duopolies with heterogeneous consumers. Management Science, 2008, 54(12): 2081-2087 doi: 10.1287/mnsc.1080.0931
|
[44]
|
Herrera F, López E, Rodríguez M A. A linguistic decision model for promotion mix management solved with genetic algorithms. Fuzzy Sets and Systems, 2002, 131(1): 47-61 doi: 10.1016/S0165-0114(01)00254-8
|
[45]
|
Moro L F L, Zanin A C, Pinto J M. A planning model for refinery diesel production. Computers & Chemical Engineering, 1998, 22(S1): S1039-S1042
|
[46]
|
赵浩, 荣冈, 冯毅萍.炼油企业生产计划与重点装置工艺条件集成优化.控制理论与应用, 2014, 31(6): 773-778 http://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201406013.htmZhao Hao, Rong Gang, Feng Yi-Ping. Integrating refinery unit operations with production planning optimization. Control Theory & Applications, 2014, 31(6): 773-778 http://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201406013.htm
|
[47]
|
Li J, Xiao X, Boukouvala F, Floudas C A, Zhao B G, Du G M, Su X, Liu H W. Data-driven mathematical modeling and global optimization framework for entire petrochemical planning operations. AIChE Journal, 2016, 62(9): 3020-3040 doi: 10.1002/aic.15220
|
[48]
|
王贺. 面向订单式生产企业物料需求计划子系统的设计与实现[硕士学位论文], 哈尔滨工业大学, 中国, 2013Wang He. Design and implementation order-oriented manufacturing enterprise mrp subsystem[Master dissertation], Harbin Institute of Technology, China, 2013
|
[49]
|
De Boer L, Labro E, Morlacchi P. A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, 2001, 7(2): 75-89
|
[50]
|
Xia W J, Wu Z M. Supplier selection with multiple criteria in volume discount environments. Omega, 2007, 35(5): 494-504 doi: 10.1016/j.omega.2005.09.002
|
[51]
|
Mannan S. Lees' Loss Prevention in the Process Industries: Hazard Identification, Assessment and Control. (4th edition). Burlington, MA: Butterworth-Heinemann, 2012.
|
[52]
|
Kim G H, Spafford E H. The design and implementation of tripwire: a file system integrity checker. In: Proceedings of the 2nd ACM Conference on Computer and Communications Security. Fairfax, Virginia, USA: ACM, 1994. 18-29
|
[53]
|
Venkatasubramanian V, Rengaswamy R, Yin K W, Kavuri S N. A review of process fault detection and diagnosis: Part Ⅰ: quantitative model-based methods. Computers & Chemical Engineering, 2003, 27(3): 293-311
|
[54]
|
Sengul H, Santella N, Steinberg L J, Cruz A M. Analysis of hazardous material releases due to natural hazards in the United States. Disasters, 2012, 36(4): 723-743 doi: 10.1111/disa.2012.36.issue-4
|
[55]
|
Veltman L M. Incident data analysis using data mining techniques[Master dissertation], Texas A&M University, USA, 2008
|
[56]
|
曲彦光, 张勤, 朱群雄.动态不确定因果图在化工系统动态故障诊断中的应用.智能系统学报, 2015, 10(3): 354-361 http://cdmd.cnki.com.cn/Article/CDMD-10010-1015544412.htmQu Yan-Guang, Zhang Qin, Zhu Qun-Xiong. Application of dynamic uncertain causality graph to dynamic fault diagnosis in chemical processes. CAAI Transactions on Intelligent Systems, 2015, 10(3): 354-361 http://cdmd.cnki.com.cn/Article/CDMD-10010-1015544412.htm
|
[57]
|
Peng K X, Zhang K, You B, Dong J, Wang Z D. A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes. IEEE Transactions on Industrial Electronics, 2016, 63(4): 2615-2624
|
[58]
|
Dai Y Y, Zhao J S. Fault diagnosis of batch chemical processes using a dynamic time warping (DTW)-based artificial immune system. Industrial & Engineering Chemistry Research, 2011, 50(8): 4534-4544
|
[59]
|
Askarian M, Escudero G, Graells M, Zarghami R, Jalali-Farahani F, Mostoufi N. Fault diagnosis of chemical processes with incomplete observations: a comparative study. Computers & Chemical Engineering, 2016, 84: 104-116
|
[60]
|
De Visscher A. Air Dispersion Modeling: Foundations and Applications. New Jersey, USA: John Wiley & Sons, 2013.
|
[61]
|
Hanna S, Dharmavaram S, Zhang J, Sykes I, Witlox H, Khajehnajafi S, Koslan K. Comparison of six widely-used dense gas dispersion models for three recent chlorine railcar accidents. Process Safety Progress, 2008, 27(3): 248-259 doi: 10.1002/prs.v27:3
|
[62]
|
Tauseef S M, Rashtchian D, Abbasi S A. CFD-based simulation of dense gas dispersion in presence of obstacles. Journal of Loss Prevention in the Process Industries, 2011, 24(4): 371-376 doi: 10.1016/j.jlp.2011.01.014
|
[63]
|
Lauret P, Heymes F, Aprin L, Johannet A. Atmospheric dispersion modeling using Artificial Neural Network based cellular automata. Environmental Modelling & Software, 2016, 85: 56-69
|
[64]
|
Kanevce G H, Kanevce L P, Andreevski I B, Dulikravich G S. Inverse approaches in improvement of air pollution plume dispersion models for regulatory applications. In: Proceeding of Inverse Problems, Design and Optimization Symposium. Miami, Florida, USA: Taylor & Francis, 2007.
|
[65]
|
Berry J W, Fleischer L, Hart W E, Phillips C A, Watson J P. Sensor placement in municipal water networks. Journal of Water Resources Planning and Management, 2005, 131(3): 237-243 doi: 10.1061/(ASCE)0733-9496(2005)131:3(237)
|
[66]
|
Berry J, Hart W E, Phillips C A, Uber J. A general integer-programming-based framework for sensor placement in municipal water networks. In: Proceedings of the 2004 World Water and Environmental Resources Congress. Salt Lake City, Utah, USA: American Society of Civil Engineers, 2004, DOI: 10.1061/40737(2004)455
|
[67]
|
Legg S W, Wang C, Benavides-Serrano A J, Laird C D. Optimal gas detector placement under uncertainty considering conditional-value-at-risk. Journal of Loss Prevention in the Process Industries, 2013, 26(3): 410-417 doi: 10.1016/j.jlp.2012.06.006
|
[68]
|
Benavides-Serrano A J, Legg S W, Vázquez-Román R, Mannan M S, Laird C D. A stochastic programming approach for the optimal placement of gas detectors: unavailability and voting strategies. Industrial & Engineering Chemistry Research, 2014, 53(13): 5355-5365
|
[69]
|
梅辽颖, 陈彬.镇海炼化:插上智能的翅膀.中国石油石化, 2016, (11): 54-55 doi: 10.3969/j.issn.1671-7708.2016.11.013Mei Liao-Ying, Chen Bin. Sinopec Zhenhai refining & chemical company: insert the wings to smart. China Petrochem, 2016, (11): 54-55 doi: 10.3969/j.issn.1671-7708.2016.11.013
|
[70]
|
林镜.九江石化智能制造4.0.中国石油企业, 2016, (1): 36-37 http://www.cnki.com.cn/Article/CJFDTOTAL-IDER201605004.htmLin Jing. Jiujiang petrochemical smart manufacturing 4.0. China Petroleum Enterprise, 2016, (1): 36-37 http://www.cnki.com.cn/Article/CJFDTOTAL-IDER201605004.htm
|
[71]
|
柴天佑.工业过程控制系统研究现状与发展方向.中国科学:信息科学, 2016, 46(8): 1003-1015 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608005.htmChai Tian-You. Industrial process control systems: research status and development direction. Scientia Sinica Informationis, 2016, 46(8): 1003-1015 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608005.htm
|
[72]
|
Pan Y H. Heading toward artificial intelligence 2.0. Engineering, 2016, 2(4) : 409-413 doi: 10.1016/J.ENG.2016.04.018
|
[73]
|
桂卫华, 陈晓方, 阳春华, 谢永芳.知识自动化及工业应用.中国科学:信息科学, 2016, 46(8): 1016-1034 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608006.htmGui Wei-Hua, Chen Xiao-Fang, Yang Chun-Hua, Xie Yong-Fang. Knowledge automation and its industrial application. Scientia Sinica Informationis, 2016, 46(8): 1016-1034 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608006.htm
|
[74]
|
Tidriri K, Chatti N, Verron S, Tiplica T. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges. Annual Reviews in Control, 2016, 42: 63-81 doi: 10.1016/j.arcontrol.2016.09.008
|
[75]
|
Jung S. Facility siting and plant layout optimization for chemical process safety. Korean Journal of Chemical Engineering, 2016, 33(1): 1-7 doi: 10.1007/s11814-015-0242-4
|