水泥厂质量监控管理及决策支持系统的研究与设计
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摘要
面对日趋激烈的市场竞争，如何应用高新技术改造传统产业，既是企业十分关注而迫切需要得到解决的问题，又是控制技术应用中一个重大的研发领域。随着现代工业的飞速发展，工业生产过程的控制规模不断扩大，复杂程度不断增加。工业生产过程控制模式已由原来的局部自动化、DDC集中控制发展到先进的分布式控制系统(Distributed Control System，简称DCS)和FCS。DCS在保证一定的适应性、扩展性和可靠性的基础上，很好地解决了如何提高系统的控制能力的问题，然而目前市场推出的DCS系统通常以生产过程自动化为主要目标，优化控制仅限于生产过程的局部，对于全局性大系统的优化问题尚未得到应有的重视，影响了DCS的应用效果。
本文在DCS的基础上，提出了以水泥产品质量为直接控制目标的控制方式，将质量控制与生产过程控制结合起来，对水泥厂现有的生产工艺提供优化控制和质量管理的决策支持，对水泥的各阶段质量指标和最终质量指标—28天强度等进行动态追踪实时控制。针对水泥企业数据量巨大，控制点众多，工艺复杂等特有的生产特点，结合了当今工业应用中的前沿技术，以数据库为基础，集成了DSS、数据挖掘、面向对象技术、COM／DCOM、OPC等新兴技术以及一些优化决策算法，如线性规划、多元回归、正交试验等，将现代检测技术、自动控制技术、计算机技术、通讯技术与水泥生产工艺融为一体，在完成设备集成、信息集成的基础上实现全厂质量信息的快速采集、科学处理、工艺设计、质量调
控。
本文首先简单描述了系统的结构原理以及一些主要功能模块，对水泥生产
工艺进行了一定的阐述，接着重点介绍了水泥工艺优化和质量控制中行之有效
的控制算法，对运用这些算法实现在线质量控制和决策支持进行了详尽的论证
和说明，并给出应用实例与应用效果。最后给出与DCS实现通讯的方式。
本系统不仅能显著简化浩繁的化验室数据及其他控制点的数据，大大提高
劳动生产率，降低工人劳动强度，而且能够提供一个有效的生产工艺优化平台，
辅助企业质量管理人员完成质量控制决策，通过现场设备，实现质量闭环调控，
优化生产工艺过程，最终达到保证水泥生产质量，提高企业经济效益的目的。
Facing the more and more severe market competition, the enterprises are very concerned and want to find the way to apply advanced technology to remold traditional industries. And this is also an important research and development area in the applied control technology. With the dramatic development of modern industry, industry production process has experienced enlargement in the scale of control and increase in the degree of complexity. The control mode in the industry production process has been developed from the original partial automatic, DDC concentrated control to the advanced DCS and PCS. DCS not only has the proper adaptive, extension and liability, but also solved the problem of enhance the control ability of the system. However, the current DCS system on the market generally aims at the production process automation, which is limited to the part of the production process. The DCS system is not receiving adequate attention in the comprehensive optimization problem in large systems, and this has bad
ly affected its application efficiency.
Based on the DCS, this paper points out the control methods that are directly aimed at controlling the cement production quality. It combines the quality control with the production process control. According to the current production technology
in cement plants, I provide an optimization control and decision support for quality managers. By this control method, you can dynamically follow and real-time control the quality indexes in various stages and in the final stage, such as the 28-day intensity. Most cement plants have large amount of data and control points, and their technology are complicated. Therefore, based on database, I combine many advanced technology in the frontier of current applied industry and integrate many new technology such as DSS, data mining, oriented-object, COM/DCOM, OPC, and some optimization decision methodology such as linear programming, multiple regression. By combining the modern testing technology, automation control technology, computer sciences, communication technology and cement production technology, I have realized the fast collecting, scientifically analyzing, technology devising and quality control of the quality information in the whole plant.
This paper begins with a general description of the structure principals of the system and some main function. It then introduces the cement production technology and emphasizes on the efficient control algorithms in the cement technology optimization and quality control. I give out detailed proof and demonstration for the application of these methodologies in the realization of online quality control and decision support, as well as application examples and application outcomes. At last, I present the methods to communicate with DCS.
This system can significantly simplify the tedious data in the lab and other control points, and thus greatly increase the production efficiency and decrease the amount of work by the workers. Moreover, this system can also provide a flat for production technology optimization, helping the quality controllers to make quality control decisions. Quality controllers can realize the closed loop quality adjustment and improve the production technology via the on-site equipment. Finally the quality of cement can be guaranteed and the goal of increasing profits is achieved.
本文在DCS的基础上，提出了以水泥产品质量为直接控制目标的控制方式，将质量控制与生产过程控制结合起来，对水泥厂现有的生产工艺提供优化控制和质量管理的决策支持，对水泥的各阶段质量指标和最终质量指标—28天强度等进行动态追踪实时控制。针对水泥企业数据量巨大，控制点众多，工艺复杂等特有的生产特点，结合了当今工业应用中的前沿技术，以数据库为基础，集成了DSS、数据挖掘、面向对象技术、COM／DCOM、OPC等新兴技术以及一些优化决策算法，如线性规划、多元回归、正交试验等，将现代检测技术、自动控制技术、计算机技术、通讯技术与水泥生产工艺融为一体，在完成设备集成、信息集成的基础上实现全厂质量信息的快速采集、科学处理、工艺设计、质量调
控。
本文首先简单描述了系统的结构原理以及一些主要功能模块，对水泥生产
工艺进行了一定的阐述，接着重点介绍了水泥工艺优化和质量控制中行之有效
的控制算法，对运用这些算法实现在线质量控制和决策支持进行了详尽的论证
和说明，并给出应用实例与应用效果。最后给出与DCS实现通讯的方式。
本系统不仅能显著简化浩繁的化验室数据及其他控制点的数据，大大提高
劳动生产率，降低工人劳动强度，而且能够提供一个有效的生产工艺优化平台，
辅助企业质量管理人员完成质量控制决策，通过现场设备，实现质量闭环调控，
优化生产工艺过程，最终达到保证水泥生产质量，提高企业经济效益的目的。
Facing the more and more severe market competition, the enterprises are very concerned and want to find the way to apply advanced technology to remold traditional industries. And this is also an important research and development area in the applied control technology. With the dramatic development of modern industry, industry production process has experienced enlargement in the scale of control and increase in the degree of complexity. The control mode in the industry production process has been developed from the original partial automatic, DDC concentrated control to the advanced DCS and PCS. DCS not only has the proper adaptive, extension and liability, but also solved the problem of enhance the control ability of the system. However, the current DCS system on the market generally aims at the production process automation, which is limited to the part of the production process. The DCS system is not receiving adequate attention in the comprehensive optimization problem in large systems, and this has bad
ly affected its application efficiency.
Based on the DCS, this paper points out the control methods that are directly aimed at controlling the cement production quality. It combines the quality control with the production process control. According to the current production technology
in cement plants, I provide an optimization control and decision support for quality managers. By this control method, you can dynamically follow and real-time control the quality indexes in various stages and in the final stage, such as the 28-day intensity. Most cement plants have large amount of data and control points, and their technology are complicated. Therefore, based on database, I combine many advanced technology in the frontier of current applied industry and integrate many new technology such as DSS, data mining, oriented-object, COM/DCOM, OPC, and some optimization decision methodology such as linear programming, multiple regression. By combining the modern testing technology, automation control technology, computer sciences, communication technology and cement production technology, I have realized the fast collecting, scientifically analyzing, technology devising and quality control of the quality information in the whole plant.
This paper begins with a general description of the structure principals of the system and some main function. It then introduces the cement production technology and emphasizes on the efficient control algorithms in the cement technology optimization and quality control. I give out detailed proof and demonstration for the application of these methodologies in the realization of online quality control and decision support, as well as application examples and application outcomes. At last, I present the methods to communicate with DCS.
This system can significantly simplify the tedious data in the lab and other control points, and thus greatly increase the production efficiency and decrease the amount of work by the workers. Moreover, this system can also provide a flat for production technology optimization, helping the quality controllers to make quality control decisions. Quality controllers can realize the closed loop quality adjustment and improve the production technology via the on-site equipment. Finally the quality of cement can be guaranteed and the goal of increasing profits is achieved.
引文
[1] J. Shen , R. Li , F. Zhang , Z. Rengel, C. Tang. Orthogonal polynomial models to describe yield response of rice to nitrogen and phosphorus at different levels of soil fertility. Nutrient Cycling in Agroecosystems, 2003, 65:243-251
[2] J.Tobin. A Statistical Demand Function for food in the U.S.A[J]. Journal of Royal Statistical society, Set.A, 113-141
[3] Alexei Bespalov. Orthogonal systems of singular functions and numerical treatment of problems with degeneration of data. Advances in Computational Mathematics., 2003, 19: 159-182
[4] Mac Nally, Ralph. Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables[J]. Biodiversity and Conservation, 2002.8, 11(8): 1397-1401
[5] Xiong, M.; Wang, J.; Wang, P. Differential-Algebraic Approach to linear programming. Journal of Optimization Theory and Applications, 2002.08, 114(2): 443-470
[6] Liu, C.; Green,S. M; Watkins, N. D.; Gregg, P. J.; McCaskie,A. W. Effect of restraint on the creep behavior of clinical bone cement. Journal of Materials Science Letters, 2002.6, 21(12): 959-962
[7] Chang, T. S.; Adachi, J.; Wang, X.; Chen, T.R. On the Equivalence of the Simplex Methods and a Multiplier-Alike Method for linear programming. Journal of Optimization Theory and Applications, 2002.06, 113(3): 487-512
[8] Duffy, Nigel; Helmbold, David. Boosting Methods for regression. Machine Learning, 2002.5, 47(2/3): 153-200
[9] Romas'ko, V. S.; Chernyavs' kyi, V. L. On "Unpredictable" Structure-Changing Effects in cement Materials. Materials Science, 2002.05, 38(3): 413-417
[10] Granichin, O. N. Estimating the Parameters of Linear regression in an Arbitrary Noise. Automation and Remote Control, 2002.1, 63(1): 25-35
[11] Perraki.M.; Perraki, T.; Kolovos, K.; Tsivilis, S.; Kakali, G. Secondary raw materials in cement industry. Journal of ThermalAnalysis and Calorimetry, 2002, 70(1): 143-150
[12] Kivinen,J.; Warmuth, M. K. Relative Loss Bounds for Multidimensional regression Problems. Machine Learning, 2001.12, 45(3): 301-329
[13] Evdokimova, N. G; Murtazin, F. R.; Tselishchev, l. V. Regression Analysis of Operation of a Modified Paving Asphalt Production Plant. Chemistry and Technology of Fuels and Oils. 2001.5, 37(3): 163-166
[14] Shubin, V. I&period. The Effect of Temperature on the Lining of Rotary cement Kilns. Refractories and Industrial Ceramics, 2001.05/06, 42(5/6): 216-221
[15] Tyurin,A. N. The Development of a regression Model for Estimating the Stability of a System for Transferring the Dimensions of the Units of Physical Quantities. Measurement Techniques, 2001.2, 44(2): 174-180
[16] Walczuch, Rita M.; Watson, Richard T. Analyzing Group Data in MIS Research: Including the Effect of the Group. Group Decision and Negotiation, 2001.01, 10(1): 83-94
[17] Zlobec, S, Stability in linear programming Models: An Index Set Approach. Annals of Operations Research, 2001.01, 101(1/4): 363-382
[18] Mac Nally, Ralph. Regression and model-building in conservation biology, biogeography and ecology. Biodiversity and Conservation, 2000.5, 9(5): 655-671
[19] Robinson, D; Lysaght. P. Methods of exploiting simulation technology for simulating the timing of dynamically reconfigurable logic. IEE Proceedings: Computers and Digital Techniques, 2000.5, 147(3): 175-180
[20] Guo, Shenghui; Sun, Yufang. Information system design based on the dictionary database. JISUANJI XUEBAO. 2000.4, 23( 4): 414-418
[21] Bohner, M.; Merkle,H. P.; Landuyt, P. Van; Trophardy, G.; Lemaitre,J. Effect of several additives and their admixtures on the physico-chemical properties of a calcium phosphate cement. Journal of Materials Science: Materials in Medicine, 2000.02, 11(2): 111-116
[22] FIESTRAS-JANEIRO, M. G.; GARC]A-JURADO, I.; PUERTO, J. The Concept of Proper Solution in linear programming. Journal of Optimization Theory and Applications, 2000.09, 106(3): 511-525
[23] Glazebrook, K.D; Nio‐Mora,J. A linear programming approach to stability, optimisationand performance analysis for Markovian multiclassqueueing networks.
Annals of Operations Research, 92(1): 1-18
[24] M.C.ER. Decision Support Systems: A Summary, Problems, and Futures[J]. DSS, 1998, 23(4): 55-363.
[25] A.W.Brown(Ed.). Component-Based Software Engineering[R]. Newyork, lEEE Computer Soc., 1997:12-23.
[26] http://www.opcconnect.com/delphi.php
[27] http://www.tmssoftware.com/new.htm
[28] H. NIEMANN, D. SCHMIDT, U. MAAS. An efficient storage scheme for reduced chemical kinetics based on orthogonal polynomials[J], Journal of Engineering Mathematics, 1997, 31:131-142
[29] Steve Teixeira，Xavier Pacheco．Delphi 5开发人员指南[M]．机械工业出版社，2000．7
[30] 李维．Delphi 5．x分布式多层应用系统篇[M]．机械工业出版社，2000．4
[31] 李维．Delphi 5．x ADO/MTS/COM+高级程序设计篇[M]．机械工业出版社，2000．11
[32] 刘艺．Delphi 6企业级解决方案及应用[M]．机械工业出版社，2002．7
[33] 刘龙．水泥工厂实用新技术[M]．武汉工业大学出版社，2000．1
[34] 李明豫，丁卫东．地方水泥企业化验室工作手册[M]．中国建材工业出版社，1994．12
[35] 周德泽，袁南儿，应英．计算机智能监测控制系统的设计及应用[M]．清华大学出版社，2002．1
[36] 陈希孺．数理统计引论[M]．科学出版社，1999．2
[37] 甘应爱，田丰等．运筹学[M]．清华大学出版社，2002．9
[38] Don Box．COM本质论[M]．中国电力出版社，2001．8
[39] Jiawei Han，Micheline Kamber．数据挖掘与技术[M]．机械工业山版社，2001．8
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[2] J.Tobin. A Statistical Demand Function for food in the U.S.A[J]. Journal of Royal Statistical society, Set.A, 113-141
[3] Alexei Bespalov. Orthogonal systems of singular functions and numerical treatment of problems with degeneration of data. Advances in Computational Mathematics., 2003, 19: 159-182
[4] Mac Nally, Ralph. Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables[J]. Biodiversity and Conservation, 2002.8, 11(8): 1397-1401
[5] Xiong, M.; Wang, J.; Wang, P. Differential-Algebraic Approach to linear programming. Journal of Optimization Theory and Applications, 2002.08, 114(2): 443-470
[6] Liu, C.; Green,S. M; Watkins, N. D.; Gregg, P. J.; McCaskie,A. W. Effect of restraint on the creep behavior of clinical bone cement. Journal of Materials Science Letters, 2002.6, 21(12): 959-962
[7] Chang, T. S.; Adachi, J.; Wang, X.; Chen, T.R. On the Equivalence of the Simplex Methods and a Multiplier-Alike Method for linear programming. Journal of Optimization Theory and Applications, 2002.06, 113(3): 487-512
[8] Duffy, Nigel; Helmbold, David. Boosting Methods for regression. Machine Learning, 2002.5, 47(2/3): 153-200
[9] Romas'ko, V. S.; Chernyavs' kyi, V. L. On "Unpredictable" Structure-Changing Effects in cement Materials. Materials Science, 2002.05, 38(3): 413-417
[10] Granichin, O. N. Estimating the Parameters of Linear regression in an Arbitrary Noise. Automation and Remote Control, 2002.1, 63(1): 25-35
[11] Perraki.M.; Perraki, T.; Kolovos, K.; Tsivilis, S.; Kakali, G. Secondary raw materials in cement industry. Journal of ThermalAnalysis and Calorimetry, 2002, 70(1): 143-150
[12] Kivinen,J.; Warmuth, M. K. Relative Loss Bounds for Multidimensional regression Problems. Machine Learning, 2001.12, 45(3): 301-329
[13] Evdokimova, N. G; Murtazin, F. R.; Tselishchev, l. V. Regression Analysis of Operation of a Modified Paving Asphalt Production Plant. Chemistry and Technology of Fuels and Oils. 2001.5, 37(3): 163-166
[14] Shubin, V. I&period. The Effect of Temperature on the Lining of Rotary cement Kilns. Refractories and Industrial Ceramics, 2001.05/06, 42(5/6): 216-221
[15] Tyurin,A. N. The Development of a regression Model for Estimating the Stability of a System for Transferring the Dimensions of the Units of Physical Quantities. Measurement Techniques, 2001.2, 44(2): 174-180
[16] Walczuch, Rita M.; Watson, Richard T. Analyzing Group Data in MIS Research: Including the Effect of the Group. Group Decision and Negotiation, 2001.01, 10(1): 83-94
[17] Zlobec, S, Stability in linear programming Models: An Index Set Approach. Annals of Operations Research, 2001.01, 101(1/4): 363-382
[18] Mac Nally, Ralph. Regression and model-building in conservation biology, biogeography and ecology. Biodiversity and Conservation, 2000.5, 9(5): 655-671
[19] Robinson, D; Lysaght. P. Methods of exploiting simulation technology for simulating the timing of dynamically reconfigurable logic. IEE Proceedings: Computers and Digital Techniques, 2000.5, 147(3): 175-180
[20] Guo, Shenghui; Sun, Yufang. Information system design based on the dictionary database. JISUANJI XUEBAO. 2000.4, 23( 4): 414-418
[21] Bohner, M.; Merkle,H. P.; Landuyt, P. Van; Trophardy, G.; Lemaitre,J. Effect of several additives and their admixtures on the physico-chemical properties of a calcium phosphate cement. Journal of Materials Science: Materials in Medicine, 2000.02, 11(2): 111-116
[22] FIESTRAS-JANEIRO, M. G.; GARC]A-JURADO, I.; PUERTO, J. The Concept of Proper Solution in linear programming. Journal of Optimization Theory and Applications, 2000.09, 106(3): 511-525
[23] Glazebrook, K.D; Nio‐Mora,J. A linear programming approach to stability, optimisationand performance analysis for Markovian multiclassqueueing networks.
Annals of Operations Research, 92(1): 1-18
[24] M.C.ER. Decision Support Systems: A Summary, Problems, and Futures[J]. DSS, 1998, 23(4): 55-363.
[25] A.W.Brown(Ed.). Component-Based Software Engineering[R]. Newyork, lEEE Computer Soc., 1997:12-23.
[26] http://www.opcconnect.com/delphi.php
[27] http://www.tmssoftware.com/new.htm
[28] H. NIEMANN, D. SCHMIDT, U. MAAS. An efficient storage scheme for reduced chemical kinetics based on orthogonal polynomials[J], Journal of Engineering Mathematics, 1997, 31:131-142
[29] Steve Teixeira，Xavier Pacheco．Delphi 5开发人员指南[M]．机械工业出版社，2000．7
[30] 李维．Delphi 5．x分布式多层应用系统篇[M]．机械工业出版社，2000．4
[31] 李维．Delphi 5．x ADO/MTS/COM+高级程序设计篇[M]．机械工业出版社，2000．11
[32] 刘艺．Delphi 6企业级解决方案及应用[M]．机械工业出版社，2002．7
[33] 刘龙．水泥工厂实用新技术[M]．武汉工业大学出版社，2000．1
[34] 李明豫，丁卫东．地方水泥企业化验室工作手册[M]．中国建材工业出版社，1994．12
[35] 周德泽，袁南儿，应英．计算机智能监测控制系统的设计及应用[M]．清华大学出版社，2002．1
[36] 陈希孺．数理统计引论[M]．科学出版社，1999．2
[37] 甘应爱，田丰等．运筹学[M]．清华大学出版社，2002．9
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