摘要
钢铁行业是我国支柱型产业,近年来在国家政策的拉动下发生了飞速发展。同时,它也是能源密集型产业,消耗的能源占全国能源总消耗量的15%。因此,如何提高能源利用效率,降低能源浪费成为钢铁企业一个亟待解决的问题。其中副产煤气的综合利用是节能降耗的关键突破口。
副产煤气是钢铁企业在生产过程中产生的重要二次能源,占钢铁企业总能源消耗的30%,其优化调度对于整个企业节能降耗发挥重大作用。但是,目前关于副产煤气优化调度的研究还处于起步阶段,因此对该课题的研究意义更加重大。在此背景下,本文以钢铁企业副产煤气系统为研究对象,对其优化调度建模进行了深入研究,并将建立的优化模型应用于我国K钢铁企业,达到了降低钢铁企业能源消耗、减少生产成本的目的。主要研究成果包括以下几方面内容:
(1)在对钢铁企业副产煤气系统进行深入、详细描述分析的基础上,首次提出了适用于所有钢铁企业副产煤气系统的“三系统两层面”分析架构。“三系统”是指根据副产煤气的工艺流程,把整个副产煤气系统划分成三个相互关联的子系统,分别定义为存储系统、产消系统和转化系统。其中产消系统中的用户根据它们消耗煤气的不同特点分为两大类。第一类是只消耗某一种煤气的用户,这类用户的煤气消耗量无法人为进行优化调度;第二类是可以混烧两种以上煤气或者其它燃料的用户,这类用户和存储系统、转化系统的用户构成的集成系统是本研究优化调度的对象。“两层面”是指在对系统建立优化调度模型的目标函数中,要综合考虑显性成本和隐性成本两方面因素。
(2)针对三种副产煤气的发生机理复杂、影响因素众多的特点,选取ARMA时间序列模型对三种煤气的产生量进行建模预测,通过算例分析验证,得到了较高的预测精度。用此模型的预测结果作为优化系统的输入值。
(3)对产消系统中只消耗某一种煤气用户的消耗量进行建模预测。根据消耗用户的不同特点将其分为四类。分别采用时间序列方法、基于Levenberg -Marquardt (LM)算法的BP神经网络方法、平滑指数法和线性回归法对其消耗的煤气量进行建模预测,通过算例分析验证,得到了较高的预测精度。用此模型的预测结果作为优化系统的输出值。
(4)建立了钢铁企业副产煤气系统动态优化调度模型。选取副产煤气系统生产成本最小化为目标函数,充分考虑影响副产煤气系统生产成本的所有因素,包括外购燃料成本、副产煤气的放散成本、副产煤气柜煤气量波动成本以及锅炉操作成本等;以物料守恒、能量守恒、设备操作要求等作为约束条件;采用混合整数线性规划模型建模,对副产煤气系统进行优化调度。实例分析中,将建立的优化模型应用我国K钢铁企业,节省30%的生产成本。
(5)首次将环境成本引入到副产煤气系统优化调度模型中,建立了基于环境成本的钢铁企业副产煤气系统绿色优化调度模型。模型在考虑生产成本的基础上,综合考虑了副产煤气放散、燃烧排放和外购燃料燃烧排放所带来的环境成本。实例分析中,将基于环境成本的绿色优化调度模型与第六章优化模型对比,总成本节约了1.3%。
最后,对本文的研究所取得的成果进行了总结,并对本领域未来的研究方向进行了展望。
As one of the fundamental pillar industries in China, the iron and steel industry has developed rapidly in recent years. Meanwhile, it is an energy-intensive industry, whose energy cost accounts for 15% of the total energy cost of China. Therefore, efficient use of energy is crucial for reducing total operation cost. Byproduct gases, which are produced during the production of iron and steel without additional cost, are important energy sources to meet energy needs of the process of making iron and steel. Thus, optimization of byproduct gases will play a great role in energy saving. However,few research works have been reported on this field. As a result, it is extremely valuable to give a comprehensive research on this topic.
Based on this background, this dissertation studies on byproduct gas system in the iron and steel industry, and makes a deeply research on optimization scheduling of byproduct gases. The optimal model is applied in an iron and steel plant in China, and an optimal result in terms of total cost reduction is obtained. The main contributions in this dissertation are as follows:
(1) Based on a deeply and comprehensive analysis of byproduct gas system in the iron and steel industry, a“three systems, two aspects”theory is proposed. Three systems”means the whole byproduct gas system is delimited into three relative systems: produce-and-consume system, store system and transformation system. In produce-and-consume system, byproduct gas consuming users are divided into two kinds, the first kind of which is single-consuming users, and the second kind is mixed-consuming users. Byproduct gas consuming users belonging to the first kind of produce-and-consume system, store system and transformation system compose the objective, which is to be optimized.“Two aspects”means both explicit cost and inexplicit cost should be considered when an optimal model is established.
(2) According to different affecting factors of three kinds of byproduct gases, different ARMA models are established separately to predict the supply of byproduct gases. The results obtaining from the forecasting models are used as the input of the system to be optimized. (3) The consumption forecasting models of byproduct gases are also established. Consuming users of byproduct gases are divided into four kinds according to their different consuming characters. Time series prediction model, Levenberg-Marquardt BP (LM-BP) neural network prediction model, linear regression prediction model, and exponential smoothing prediction model are used to predict the demands of four kinds of byproduct gas consuming users. The results obtained from the forecasting models are used as the output of the system to be optimized.
(4) An Mixed Integer Linear Programming (MILP) model of byproduct gases in the iron and steel industry is established. The objective function is to reduce total operation cost in the entire energy system for multi-period operation through optimizing byproduct gas distribution. The total operation cost includes penalty cost for emission or shortage of byproduct gases, penalty cost for deviation from normal gasholder levels, penalty cost for burners switching and fuel load changing in the boilers, oil purchasing cost. Besides, certain relative constraints are given and an optimal model is presented. The model is applied in K iron and steel plant and 30% of the total operation cost is reduced.
(5) The“environmental cost”is considered in the optimization of byproduct gas system in the iron and steel industry, and a green scheduling model including environmental cost of byproduct gas system is proposed for the first time. The penalty costs for the emission of byproduct gases, and waste gases produced from burned byproduct gases and purchased oil, are all calculated in the objective function to make the model more reasonable. Compared with the previous model proposed in Chapter 6, the green optimal model could save 1.3% total cost.
Finally, a summary of the research referred above is concluded and some interesting and challenging topics, which deserve further investigation in the future, are discussed.
引文
[1] Adams F. G., Shachmurove Y., Modeling and Forecasting Energy Consumption in China: Implications for Chinese Energy Demand and Imports in 2020, Energy Economics, 2008, 30 (3): 1263~1278
[2] Ang B. W., Monitoring Changes in Economy– Wide Energy Efficiency: From Energy-GDP Ratio to Composite Efficiency Index, Energy Policy 2006, 34 (5): 574~582
[3]黄陆军,钢铁企业应用先进节能技术的思考,河南冶金,2008,10,12(5):1,19~20
[4]中国金属新闻网,国内外钢铁企业能耗及环保指标比较研究, http://www.metalnews.cn/gt/show-35474-1.html
[5]中国矿业网,2009年钢铁企业能耗降低技术分析, http://www.chinamining.com.cn/news/listnews.asp?siteid=233542&ClassId=154
[6]张春霞,齐渊洪,严定鎏等,中国炼铁系统的节能与环境保护,钢铁,2006, 41(11):1~5
[7]傅兵,利用钢厂副产煤气资源发展一碳化工,酒钢科技,2006,4:138~41
[8]张明新,刘日新,蓄热燃烧技术及其在冶金工业炉中的应用,节能,2000, (7):18~20
[9]侯长连,胡和平,董为民等,高效蓄热式工业炉的开发和应用,钢铁,2002, 37(1):65~68
[10]中国钢铁新闻网,2008年中国钢铁工业能源利用状况述评, http://www.csteelnews.com/101392/101430/50641.html
[11]油气通信网,中国钢铁工业能耗现状与节能前景,http://www.infopetro.com.cn/dissertation/view.asp?id=4177&cid=23
[12]杨晓,张玲,钢铁工业能源消耗而二次能源利用途径及对策,钢铁,2000, 35 (20):64~67
[13]符小林,焦炉气制取甲醇项目开发方案:[硕士学位论文] ,四川;四川大学,2006
[14]王太炎,焦炉煤气开发利用的问题与途径,燃料与化工,2004, 35(6):1~3
[15]张琦,蔡九菊,杜涛,钢铁联合企业煤气系统优化利用,冶金能源,2005,(5):9~16
[16]李新,王立新,许志宏,钢铁厂煤气化工利用途径的探讨,计算机与应用化学,2001,18(1):59~63
[17]陈志斌,国内转炉煤气回收利用技术的现状及发展,冶金动力,2003,1:9~13
[18]邵玉良,钢铁企业能源中心可行性研究,钢铁,1996, 31(增刊):105~108
[19] Valsalam S. R., Muralidharan V., Implementation of energy management system for an integrated steel plant, Proceedings of Energy Management and Power Delivery, 1998, (2):661~666
[20]判治洋一,范锤利,采用大规模DCS系统的现代化能源中心,冶金能源,1993, 12(5):43~47
[21] Stark R. J., Computerized energy management in an integrated steel plant, Proceedings of the 1984 American Control Conference, 1984, 2: 638~643
[22] Ramani B., Subramonian T., Energy management system in Bhilai steel plant-implementation and future enhancements, Electricity Conservation Quarterly, 1997, 18 (2): 3~9
[23] Valsalam S. R., Muralidharan V., Implementation of energy management system for an integrated steel plant, Proceedings of Energy Management and Power Delivery, 1998, (2): 661~666
[24] Larsson M., Dahl J., Reduction of the specific energy use in integrated steel plant-the effect of an optimization model, ISIJ International, 2003, 43 (10): 1664~1673
[25] Worrell E., Price L., An integrated benchmarking and energy savings tool for the iron and steel industry, International Journal of Green Energy, 2006, 3(2): 117~126
[26] Jebaraj S, Dagl J, A review of energy models, Renewable and Sustainable Energy Reviews, 2006, 10 (11): 281~311
[27] Larsson M., Wang C., Dahl J., Development of a method for analyzing energy environmental and economic efficiency for an integrated steel plant, Applied Thermal Engineering, 2006, 26 (13): 1353~1361
[28]吴泾,宝钢能源中心实时监控与信息管理系统开发,能源技术,1999, 4: 8~13
[29]陈光,陆钟武,蔡九菊,宝钢能源优化模型的研究,冶金能源,2003, 22(1): 5~9
[30]冯为民,宝钢三期工程能源中心系统,能源动力,1997, 5:1~5
[31]陈尚恒,苏利平,龚伟,强化计量检测管理提高煤气数据准确率,工业计量,2002, 1:19~21
[32]刘祥官,李吉弯,冶金生产过程的系统优化,系统工程理论与实践,1994, 6:54~59
[33]初明,梅钢计算机能源管理系统介绍,梅山科技,2006,(12):53~55
[34]张娣,李佳红,现代化能源监控及管理系统的集成与应用,2005,(4v28~31
[35]唐新义,企业煤气平衡基本思路,冶金能源,2000, 19(1):10~14
[36]陈尚恒,苏利平,强化计量监测管理提高煤气数据准确率,工业计量,2002, (1):19~21
[37]王鼎,方喆，能源中心在宝钢能源生产中的作用和发展趋势，中国冶金，2005,(1):21~23
[38]冯晶,田小果,EMS系统在钢铁厂能源中心的应用,自动化与仪器仪表,2005,(3):16~19
[39]邓万里,陈伟昌,宝钢高炉煤气系统平衡实践,宝钢技术,2003,(2):31~33
[40]孙贻公,对大型钢铁联合企业煤气平衡问题的探讨,钢铁,2003, 38(6):59~64
[41]赵立合,林秀贞,朱和杰,钢铁企业煤气系统优化管理模型及其应用,冶金能源,1994, 13(6):10~13
[42]李业,预测学,广州:华南工学院出版社,1988
[43]陈尚恒,苏利平,强化计量监测管理提高煤气数据准确率,工业计量,2002, (1):19~21
[44]唐新义,企业煤气平衡基本思路,冶金能源,2000,19(1):10~14
[45]吴成忠,焦德美,用AR (p)动态模型预测高炉煤气发生量,冶金能源,1988, 7(4):48~51
[46] Fukuda K, Makino H, Suzuki Y, Optimal energy distribution control at the steel works, IFAC Simulation of Control Systems. Vienna, Austria, 1986, 337~342
[47]李雨膏,用时间序列分析预测焦炉煤气发生量,冶金自动化,1990,14(4):57~58
[48]戴朝晖,钢铁企业基于消耗预测模型的煤气自动平衡方法及其应用: [硕士学位论文] ,长沙;中南大学,2004
[49]刘渺,钢铁企业主工序分厂煤气量预测方法研究: [硕士学位论文] ,长沙;中南大学,2006
[50]汤振兴,钢铁焦炉煤气产消及柜位预测方法与应用: [硕士学位论文] ,大连;大连理工大学,2009
[51]张琦,谷延良,提威,钢铁企业高炉煤气供需预测模型及应用,东北大学学报(自然科学版),2010, 31(12):1737~1740
[52]邱东,陈爽,仝彩霞,钢铁企业高炉煤气平衡与综合优化,计算机技术与发展,2009,19(3):196~199
[53]李文兵,纪扬,李华德,钢铁企业煤气产生消耗动态模型研究,冶金自动化, 2008, 32(3):28~33
[54]李文兵,李华德,钢铁企业高炉煤气系统动态仿真,冶金自动化,2009, 33(2):33~51
[55]李玲玲,曹敏,吴卫华,基于多层递阶回归分析的轧钢煤气用量预测,控制工程,2004,11(增刊):33~35
[56]聂秋萍,吴敏,曹卫华,一种基于消耗预测的钢铁企业煤气平衡与数据校正方法,化工自动化及仪表,2010,37(2):14~18
[57]梁青艳,钢铁企业煤气供需动态预测问题的研究:[硕士学位论文],北京;冶金自动化研究院,2009
[58]姜曙光,济钢能源中心煤气平衡预测模型研究:[硕士学位论文],济南;山东大学,2009
[59]济南钢铁集团总公司,基于柜位预测的钢铁企业煤气动态平衡实时控制方法,中国专利,200710016562.5,2008-01-26
[60]熊永华,煤气平衡认证分析系统应用软件的研究与开发:[硕士学位论文],长沙;中南大学,2006
[61] Mousa Mohsen, Bilal Akash, Energy analysis of the steel making industry, International Journal of Energy Research, 1998, 22 (12): 1049~1054
[62] Naoki H., Yoshitshgu W., Takeyoshi K., Minimizing energy consumption in industries by cascade use of waste energy, IEEE Transactions on Energy Conversion, 1999, 3 (14): 795~801
[63] Belyaev B., Patrikeev V., Reduction the balance in gas distribution using the VMM algorithm, Measurement Techniques, 2002, 45 (3): 314~317
[64] Kouiss Khalid, Pierreaval Henri, Nasser Mebarki,Using multiagent architecture in FMS for dynamic scheduling, Journal of Intelligent Manufacturing, 1997, 8 (1): 41~47
[65] Blackstone J., Philips D., Hogg G., A state-of-the-art survey of dispatching rules for Manufactuing Job Shop Operations, International Journal of Operational Research, 1982, 20 (1): 27~46
[66]王秀纯,范锤利,兆春民,浅谈冶金企业煤气平衡,中国冶金,2005,15(8):7~8
[67]孙贻公,对大型钢铁联合企业煤气平衡问题的探讨,钢铁,2003, 38(6):59~64
[68]赵立合,林秀贞,朱和杰,钢铁企业煤气系统优化管理模型及其应用,冶金能源,1994, 13(6):10~13
[69]江文德,钢铁企业能源动态平衡和优化调度问题研究和系统设计:[硕士学位论文],浙江:浙江大学,2006
[70]明德廷,钢铁企业煤气优化调度模型研究,计算机工程与设计,2008, 29(6):33~38
[71]钱俊磊,马晓峰,杨志刚,混沌系统基于T-S模糊模型的控制方法,系统仿真学报,2005,12:2987~2990
[72]钱俊磊,钢铁企业煤气的预测与平衡:[硕士学位论文],北京:北京科技大学,2008
[73] Akimoto K., Sannomiya N., Nishikawa Y., An optimal gas supply for a power plant using a mixed integer programming model, Automatica, 1991, 27 (3): 513~518
[74] Sinha G. P., Chandrasekaran B. S., Mitter N., Strategic and operational management with optimization at Tata steel, Interfaces, 1995, 25 (1): 6~19
[75] Kim J. H., YI H. S., Han C., Optimal byproduct gas distribution in the iron and steel making process using Mixed Integer Linear Programming, International Symposium on Advanced Control of Industrial Processes, 2002, 581~586
[76] Kim J. H., YI H. S., Han C., Plant~wide multiperiod optimal energy resource distribution and byproduct gas holder level control in the iron and steel making process under varying energy demands, Process Systems Engineering, 2003, 15 (2): 882~887
[77] Kim J. H., YI H. S., Han C., A novel MILP model for plantwide multiperiod optimization of byproduct gas supply system in the iron~and steel~making process, Chemical Engineering Research & Design, 2003, 81 (8): 1015~1025
[78] Kim J. H., Han C. H., Short-term multiperiod optimal planning of utility systems using heuristics and dynamic programming, Industrial & Engineering Chemistry Research, 2001, 40 (8): 1928~1938
[79]张建良,王妤,钢铁企业煤气系统的优化利用模型,包头钢铁学院学报, 2002,21(3):280~282
[80]柯超,钢铁企业煤气调度仿真系统的研究:[硕士学位论文],北京;冶金自动化研究设计院,2010
[81]曾玉娇,钢铁企业蒸汽系统的动态仿真研究:[硕士学位论文],北京;冶金自动化研究设计院,2010
[82] Grish Bhave, Enhancing the effectiveness of the utility energy supply chain in intefrated steel manufacturing: [D], West Virginia University, 2003
[83] Iyer R. R., Grossmann I. E., Optimal multiperiod operational planning for utility systems, Computers and Chemical Engineering, 1997, 21 (8): 787~800
[84] Varbanov P. S., Doyle S., Modelling and optimization of utility systems, Chemical Engineering Research and Design, 2004, 82 (5): 561~578
[85] Han I. S., Lee Y. H., Han C. H., Modeling and optimization of the condensing steam turbine network of a chemical plant, Industrial & Engineering Chemistry Research, 2006, 45 (2): 670~680
[86] Yi H. S., Kim J. H., Periodical replanning with hierarchical repairing for the optimal operation of a utility plant, Control Engineering Practice, 2003, 11 (8): 881~894
[87] Varbanov P., Perry S., Synthesis of industrial utility systems: cost~effective carbonisation, Applied Thermal Engineering, 2005, 25 (7): 985~1001
[88]汝方济,赵世杭,华贲,石化企业热能动力系统的优化调度,石油炼制, 1993, 24(7):18~21
[89] Nath R., Kumaua D., Holiday F., Optimum dispatching of plant utility systems: to minimize cost and local NOx emissions, Proceeding of the Industrial Power Conference, 1992. 59~64
[90] Yokoyama R., Matsumoto K., Optimal sizing of a gas turbine cogeneration plant in consideration of its operational strategy, Journal of Engineering for Gas Turbines Power, 1994, 116 (1): 32~38
[91] Papalexandri K. P., Pistikopoulos E. N., Operation of a steam production network with variable demands modeling and optimization under uncertainty, Computers & Chemical Engineering, 1996, 20(Suppl pt A): 763~768
[92] Lee M. H., Lee S. J., Hierachical on-line data reconciliation and optimization for an industrial utility plant, Computers and Chemical Engineering, 1998, 22: 247~254
[93] Strouvalis A. M., Heckl I., An accelerated branch-and-branch algorithm for assignment problems of utility systems, Computers and Chemical Engineering, 2002, 26 (4-5): 617~630
[94] Ueo Y. K., Roh H. D., Optimal operation of utility system in petrochemical plants, Korean J. Chem., 2003, 20 (2): 200~206
[95] Cheung K. Y., Hui C. W., Total-site scheduling for better energy utilization, Journal of Cleaner Production, 2004, 12 (2): 171~184
[96] Cheung K. Y., Hui C. W., Sakamoto H.,Short-term site-wide maintenance scheduling,Computers & Chemical Engineering, 2004,28 (1): 91~102
[97] Hui C. W., Natori Y., An industrial application using mixed-integer programming technique: A multi~period utility system model, Computers and Chemical Engineering, 1996, 20 (suppl.): S1577~1582
[98] Hui C. W., Determining marginal values of intermediate materials and utilities using a site model, Computers & Chemical Engineering, 2000, 24: 1023~1029
[98] Hirata K., Sakamoto H., Multi-site utility integration- an industrial case study, Computers and Chemical Engineering, 2004, 28(1-2): 139~148
[100] Zhang B. J., Hua B., Effective MILP model for oil refinery-wide production planning and better energy utilization, Journal of Cleaner Production, 2007, 15 (5): 439~448
[101] Francisco A., Matos H., Multiperiod synthesis and operational planning of utility system with environmental concerns, Computers and Chemical Engineering, 2004, 28 (5): 745~753
[102]张冰剑,华贲,刘金平等,石化企业蒸汽动力系统的多周期优化运行,华北电力大学学报,2005,32(2):90~92
[103]梁彬华,王中华,田宏斌,炼油调度辅助决策系统开发与应用,广东化工,2006,33(5):56~59
[104]李树文,炼油企业瓦斯气平衡,辽宁化工,2008,37(7):496~498
[105] Zhang J. D., Rong G., An MILP model for multi-period optimization of fuel gas system scheduling in refinery and its marginal value analysis, Chemical Engineering Research & Design, 2008, 86 (2): 141~151
[106] Nishio M., Itoh J., A thermodynamic approach to steam-power system design, Industrial & Engineering Chemistry, Process Design and Development, 1981,19 (2): 306~312
[107] Yoo Y. H., Modeling and simulation of energy distribution systems in a petrochemical plant, Korean J. Chem. Eng. , 1996, 13 (4): 384~392
[108]中国钢铁工业协会科技环保部,中国钢铁工业能耗现状与节能前景,冶金管理,2004,9:15~19
[109]陈道海,马钢公司副产煤气回收利用效果及改进方向的研究:[硕士学位论文],南京;南京理工大学,2004
[110]谷葵,钢铁企业副产品煤气的合理使用,江苏冶金,2002,30(3):6~7
[111]胡新亮,李会龙,刘长云,济钢煤气资源利用走节约型之路,节能与环保,2007,11:21~24
[112]孟现俭,周茂林,龙海波,莱钢转炉煤气系统研究与优化,2003中国钢铁年会论文集,北京:中国金属协会,2003,891~895
[113]纪日耿,基于多Agent与集成优化的钢铁集成调度:[硕士学位论文],浙江;浙江大学,2010
[114]於春月,钢铁一体化生产计一划与调度优化问题研究:[博士学位论文],辽宁;东北大学,2009
[115]王志刚,冷轧生产优化调度问题研究与应用,[博士学位论文],辽宁;大连理工大学,2010
[116] Fu Y., Data Mining: Tasks, Techniques and Applications, IEEE Potetials, 1997, 18~20
[117] Madan S., Son W., Bollinger K. E., Application of data mining for power systems, Proceedings of the 1997 Canadian Conference on Electrical and Computer Engineering, Canada, 1997
[118] Brierley P., Batty B., Neural data mining and modeling for electrical load prediction, IEEE Colloquium on Knowledge Discovery and Data Mining, 1998
[119] Wang X. Z., Automatic classification for mining process operational data, Industrial Engineering & Chemical Research, 1998, 37: 2215~2222
[120] Han J., Kamber M., Data Mining: Concepts and Techniques. San Fransisco: Morgan Kaufmann Publishers, 2001
[121] Fayyad U., Piatetsky G., Smyth P., From data mining to knowledge discover: an overview, Advances in Knowledge Discovery and Data Minig, USA: AAAI/MIT Press, 1996
[122] Brillinger D. R., Time series:data analysis and theory. San Francisco 2nd Edition, 1981
[123] Fox A. J., Outliers in time series, Statist, 1972, 34: 350~363
[124] Abraham B., Box G. E. P., Bayesian analysis of some outlier in time series, Biometrika, 1979, 66: 229~236
[125] Knorr E., Ng R., Algorithms for mining distance based outliers in large datasets. Proc of the 24th VLDB Conference, New York, USA, 1998: 392~403
[126] Piegl L. A., Tiller W., Algorithm for finding All K-nearest neighbors, Computer Aided Design, 2002, 34 (2): 167~172
[127] Knorr E., Ng R., A unified approach for mining outliers properties and computation. Newport Beach: Proceedings of International Conference Knowledge Discovery and Data Mining, 1997: 219~222
[128]郑斌祥,杜秀华,席裕庚,一种时序数据的离群数据挖掘新算法,控制与决策,2002,17(3):324~327
[129]杜元顺,煤气时符合预测用的回归分析方法,煤气与热力,1982,(4): 26 ~28
[130]邓聚龙,灰预测与灰决策,武汉:华中科技大学出版社,2002:1~6
[131] Chen J. Y., Design of a stable grey prediction controller for nonlinear systems, The Journal of Grey System, 1996, 8(4): 381~396
[132]焦文玲,严铭卿,廉乐明,城市燃气负荷的灰色预测,煤气与热力,2001,21(5):387~389
[133]王希勇,张家彬,袁宗明,城市燃气长期负荷预测模型的灰色方法,管道技术与设备,2004,(6):6~8
[134]严铭卿,廉乐明,焦文玲等,燃气负荷及其预测模型,煤气与热力,2003, 23(5):259~262
[135]杜顺明,煤气时负荷系统的短期预测,煤气与热力,1981,(5):46~51
[136]焦文玲,展长虹,廉乐明等,城市燃气短期负荷预测的研究,煤气与热力,2001, 21(6):483~486
[137] Srinivasam D., Evolving artificial neural networks for short term load forecasting, Neurocomputing, 1998, 23: 265~276
[138] Metaxiotis K., Kagiannas A., Askounis D., Artificial intelligence in short term electric load forecasting: A state-of-art survey for researcher, Energy Convers Manage, 2003, 44: 1525~1534
[139] Yalcinoz T., Eminoglu U., Short term and medium term power distribution load forecasting by neural networks., Energy Conversion and Management, 2005, 46: 1393~1405
[140]谭羽非,陈家新,焦文玲等,基于人工神经网络的城市煤气短期负荷预测,煤气与热力,2001,21(3):199~202
[141]杨昭,刘燕,苗志彬等,人工神经网络在天然气负荷预测中的应用,煤气与热力,2003,(6):331~332
[142]刘涵,刘丁,郑岗等,城市天然气短期负荷预测研究,天然气工业,2005,25(7):105~107
[143]豆连旺,冯良,基于神经网络的城市燃气短期负荷预测,煤气与热力,2005,22(3):10~14
[144]肖久明,基于模糊逻辑技术的燃气负荷预测,煤气与热力,2004,24(10):547~549
[145]姚昭章,炼焦学,北京:冶金工业出版社,1983. 84~98
[146]易丹辉,数据分析与Eviews应用,北京:中国统计出版社,2002
[147] James W. Taylor,Exponential smoothing with a damped multiplicative trend, International Journal of Forecasting, 2003, 19 (4): 715~725
[148] Everette S., Gardner Jr., Exponential smoothing: The state of the art-Part II, International Journal of Forecasting, 2006, 22 (4): 637~666
[149] Baki Billah, Maxwell L. King, Ralph D, Exponential smoothing model selection for forecasting, International Journal of Forecasting, 2006, 22 (2): 239~247
[150] Sonia M. Bartolomei, Arnold L. Sweet, A note on a comparison of exponential smoothing methods for forecasting seasonal series, International Journal of Forecasting, 1989, 5 (1): 111~116
[151] Robert M. Oliver,Exponential smoothing, Operations Research Letters, 1984, 3 (3): 111~117
[152] Lorena Rosas, Vitor M. Guerrero, Restricted forecasts using exponential smoothing techniques, International Journal of Forecasting, 1994, 10 (4): 515~527
[153] Cadenas E., Jaramillo O. A. , Rivera W., Analysis and forecasting of wind velocity in chetumal using the single exponential smoothing method, Renewable Energy, 2010, 35 (5): 925~930
[154] Ana Corberán-Vallet, JoséD. Bermúdez, Enriqueta Vercher, Forecasting correlated time series with exponential smoothing models, International Journal of Forecasting, 2011, 27 (2): 252~265
[155] Modarres M., Nasrabadi E., Nasrabadi M. M.,Fuzzy linear regression models with least square errors, Applied Mathematics and Computation, 2005, 163 (2): 977~989
[156]Mohammad Mehdi Nasrabadi, Ebrahim Nasrabadi, A mathematical-programming approach to fuzzy linear regression analysis, Applied Mathematics and Computation, 2004, 155 (3): 873~881
[157] Aldo Goia, Caterina May, Gianluca Fusai, Functional clustering and linear regression for peak load forecasting, International Journal of Forecasting, 2010, 26 (4): 700~711
[158] Vincenzo Bianco, Oronzio Manca, Sergio Nardini, Electricity consumption forecasting in Italy using linear regression models, Energy, 2009, 34 (9): 1413~1421
[159] Roohollah Noori, Amir Khakpour, Babak Omidvar, Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic, Expert Systems with Applications, 2010, 37 (8): 5856~5862
[160] Bruce D. Baker, Craig E. Richards,A comparison of conventional linear regression methods and neural networks for forecasting educational spending, Economics of Education Review, 1999, 18 (4): 405~415
[161] Pagowski M., Grell G. A., Devenyi D., Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts, Environment, 2006, 40 (18): 3240~3250
[162] Zhang Yongjuan, Zhang Xiong, Grey correlation analysis between strength of slag cement and particle fractions of slag powder, Cement and Concrete Composites, 2007, 29 (6): 498~504
[163] Jia Zhen-Yuan, Ma Jian-Wei, Wang Fu-Ji, Characteristics forecasting of hydraulic valve based on grey correlation and ANFIS, Expert Systems with Applications, 2010, 37 (2): 1250~1255
[164] Khotanza. A., Maratuk, L., A self-artificial neutral network short-term load forecaster-generation three, IEEE Trans. on Power Systems, 1998, 13 (4): 1413~1422
[165] Jyh-shing R. J. A., Adaptive network-based fuzzy in inference system, IEEE transactions on systems, Man and Bybernetics, 1996, 23 (3): 665~684
[166]余雪丽,孙承惫,神经网络与实例学习,北京:中国铁道出版社,1996. 145~160
[167]闫滨,大坝安全监控及评价的智能神经网络模型研究:[博士学位论文],辽宁;大连理工大学,2006
[168]王光政,基于Levenberg-Marquardt算法的煤层突出危险性预测评价:[硕士学位论文],山东;山东科技大学,2009
[169] Kalaitzakis K., Starakakis G. S., Short-term load forecasting based on artificial neural networks parallel implementation, Electric power systems research, 2002, 63: 185~196
[170] Cetin Karata, Adnan Sozen, Emrah Dulek, Neural network based temporal feature models for short~term railway passenger demand forecasting, Expert Systems with Applications, 2009, 36 (2): 3514~3521
[171] Henry C. Co., Rujirek Boosarawongse, Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks, Computers & Industrial Engineering, 2007, 53 (4): 610~627
[172] Tomonobu Senjyu,Hitoshi Takara,One-hour-ahead load forecasting using neural network, IEEE Transactions on Power Systems, 2002, 17 (1): 1l3~1l8
[173] Simon Haykin,Neural networks, Tsinghua University Press, 2006
[174] Satish Kumar,Neural networks, Tsinghua University Press, 2006
[175] Simon Haykin , Neural networks a comprehensive foundation, Tsinghua University Press, 2001
[176] Mukta Paliwal, Usha A Kumar, Neural networks and statistical techniques: A review of applications, Expert Systems with Applications, 2009, 36 (1): 2~17
[177] Heisler J., Glibert P. M., Burkholder J. M., Improving artificial neural networks’performance in seasonal time series forecasting, Harmful Algae, 2008, 8 (1): 3~13
[178] Yu Lean, Lai Kin Keung, Wang Shouyang, Multistage RBF neural network ensemble learning for exchange rates forecasting, Neurocomputing, 2008, 71 (16-18): 3295~3302
[179]黄巧,四川省电力需求预测:[硕士学位论文],四川;西南交通大学,2007
[180]黄永福,重庆市物流需求预测方法及其应用研究:[硕士学位论文],四川;重庆交通大学,2009
[181] Whitley D., Starkweather T., Bogart C., Genetic algorithms and neural networks: optimizing connections and connectivity, Parallel Computing, 1990, 14 (3): 355~361
[182] Louis Gosselin, Maxime Tye-Gingras, Fran?ois Mathieu-Potvin, Review of utilization of genetic algorithms in heat transfer problems, International Journal of Heat and Mass Transfer, 2009, 52 (9-10): 2169~2188
[183] Hwang Shun-Fa, He Rong-Song, Improving real-parameter genetic algorithm with simulated annealing for engineering problems, Advances in Engineering Software, 2006, 37 (6): 406~418
[184] Aryanezhad M. B., Hemati Mohammad, A new genetic algorithm for solving nonconvex nonlinear programming problems, Applied Mathematics and Computation, 2008, 199 (1): 186~194
[185] Yasmin H. Said,On genetic algorithms and their applications, Handbook of Statistics, 2005, 24: 359~390
[186] Bonnie Rubenstein Montano, Victoria Yoon, Kevin Drummey, Agent learning in the multi-agent contracting system [MACS] , Decision Support Systems, 2008, 45 (1): 140~149
[187] Chen Kai-Ying, Chen Chun-Jay, Applying multi-agent technique in multi-section flexible manufacturing system, Expert Systems with Applications, 2010, 37 (11): 7310~7318
[188] Doniec Arnaud, Mandiau René, Piechowiak Sylvain, A behavioral multi-agent model for road traffic simulation, Engineering Applications of Artificial Intelligence, 2008, 21 (8): 1443~1454
[189] Vengattaraman T., Abiramy S., Dhavachelvan P., An application perspective evaluation of multi-agent system in versatile environments, Expert Systems with Applications, 2011, 38 (3): 1405~1416
[190] Nemhauser G. L. ,Wolsey L. A., Integer and Combinatorial Optimization, New York: Wiley, 1988
[191] Bemporad A. ,Morari M., Control of system integrating logical,Dynamics and Constraints, Automatica, 1999, 35: 407~427
[192] Tyler M. L., Morari M., Propositional logical in control and monitoring problems, Automatica, 1999, 35: 565~582
[193] Sahinidis N. V., Grossmann I. E., MINLP model for cyclic multiproduct scheduling on continuous parallel lines, Comp Chem Eng, 1991, 15: 85
[194]王朝晖,陈浩勋,胡保生,用Lagrangian松弛法解化工批处理调度问题,自动化学报,1998,24(1):1~8
[195]王朝晖,陈浩勋,甘文泉, Multipurpose批处理过程短期调度的滚动时域方法,控制理论与应用,1998,15(2):567~574
[196]李光华,陈昌领,邵惠鹤,批处理过程优化调度研究综述,化工自动化及仪表,2002,29(5):1~6
[197]殷瑞钰,中国需要什么钢铁业,科技和产业,2004,6:7~8
[198]李京文,赵立祥,钢铁工业生态化管理,北京:方志出版社,2008
[199]嵇囡囡,郑洪波,张树深,循环经济与钢铁行业的可持续发展,冶金能源,2006,25(2):7~10
[200]殷瑞玉,张春霞,钢铁企业功能拓展是实现循环经济的有效途径,钢铁, 2005,40(7):1~8
[201]赵燕娜,朝霞,我国大型钢铁企业可持续发展对策研究,科学管理研究,2006,24(4):46~48
[202] Bowen F. E., Cousins P. D., Horse for courses: explaining the gap between the theory and practice of green supply, Greener Management International, 2001, 35: 41~60
[203] Zilahy G., Organizational factors determining the implementation of cleaner production measures in the corporate sector, Journal of Cleaner Production, 2004, 12: 311~319
[204]杨洁,环境成本内在化及其技术经济和政策评价,技术经济与管理研究,2000,(2):37~38
[205] Matteo Bartolomeo, Martin Bennett, Environmental management accounting in Europe, European Accounting Review, 2000, (1): 9~10
[206] Haripriya G. S., Integrated environmental and economic accounting: an application to the forest resources in India, Environmental and Resource Economics, 2001, 19: 73~95
[207] Richard S. J., Estimates of damage costs of climate change, Environmental and Resource Economics, 2002, 21: 47~73
[208] Robert D., Green accounting for an externality, Pollution at a Mine, Environmental and Resource economics, 2004, 27: 409~427
[209] Hilary Nixon, Impacts of motor vehicle operation on water quality in the US-cleanup costs and policies, Transportation Research Part D: Transport and Environment, 2007, 12 (8): 564~576
[210]甘泽广,杨广汉,李平安,环境经济学概论,西安:西北工业大学出版社,1987
[211] Yuri J., Theory of accounting measurements, Sarasota: American Accounting Association, 1979
[212] Robson M., Turnot W. J., Proceedings of the institution of mechanical engineers, Journal of Power and Energy, 1994, 208 (3): 79~190
[213]戴文智,石化企业蒸汽动力系统运行优化研究:[博士学位论文],辽宁;大连理工大学,2010
[214]吴荻,集成型循环经济模式研究:[博士学位论文],辽宁;大连理工大学,2009
[215]八木裕之,环境成本概念的分析,会计,1999,156(2):258
[216] Lars D., Environmental costs of mercury pollution, Science of the Total Environment, 2006, 368 (1): 352~370
[217] Damigos D., An overview of environmental valuation methods for the mining industry, Journal of Cleaner Production, 2006, 14 (3-4): 234~247
[218] Sulaiman A., The relations among environmental disclosure, environmental performance, and economic performance: a simultaneous equations approach, Accounting, Organizations and Society, 2004, 29 (5-6): 447~471
[219] Austin Reitenga, Environmental regulation, capital intensity, and cross-sectional variation in market returns, Journal of Accounting and Public Policy, 2000, 19(2): 189~198
[220] Smit B., Spaling H., Methods for cumulative effects assessment, Environmental Impact Assessment Review, 1995 (15): 81~103
[221] Robert Costanza, The value of the world’s ecosystem services and natural capital, Nature, 1997 (5): 253~259
[222] Nobuyuki Sato, Tsutomu Okubo, Economic evaluation of sewage treatment processes in India, Journal of Environmental Management, 2007, 84 (4): 447~460
[223] Mrtberg U. M., Balfors B., Landscape ecological assessment: a tool for integrating biodiversity issues in strategic environmental assessment and planning, Journal of Environmental Management, 2007, 82 (4): 457~470
[224] Georg Muller, Gunter Stephan, Integrated assessment of global climate change with learning-by-doing and energy-related research and development, Energy Policy, 2007, 35 (11): 5298~5309
[225] Claire A., Changes in attitudes about importance of and willingness to pay for salmon recovery in Oregon, Journal of Environmental Management, 2006, 78 (4): 330~340
[226]刘光中,李晓红,污染物总量控制及排污收费标准的制定,系统工程理论与实践,2001,10:107~114
[227]王金南,排污收费理论学,北京:中国环境科学出版社,1997
[228]舒型武,应用费用最小法计算排污收费标准,钢铁技术,2003,1:42~44
[229] Craig Deegan, Environmental disclosure and share price: a discussion about efforts to study this relationship, Accounting Forum, 2004, 28: 87~97
[230]魏学好,周浩,中国火力发电行业减排污染物的环境价值标准估算,环境科学研究,2003,16(1):53~56
[231] Bartelmus Peter, Integrated environmental and economic accounting-methods and applications, Journal of Official Statistics, 1993, 9 (1): 179~182
[232] Peter A., Jana D., The willingness to pay to remove billboards and improve scenic amenities, Journal of Environmental Management, 2007, 85 (4): 1094~1100
[233] Guleda Engin, Ibrahim Demir, Cost analysis of alternative methods for wastewater handling in small communities, Journal of Environmental Management, 2006, 79 (4): 357~363
[234] Sinden J. A., Estimating the opportunity costs of biodiversity protection in the Brigalow Belt, New South Wales, Journal of Environmental Management, 2004, 70 (4): 351~362