油轮静电防爆智能控制系统
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摘要
油轮静电，对油轮的安全营运是极大的潜在危险因素。在油品的装卸，
运输及油轮的洗船过程中。众多潜在的因素会导致静电引燃引爆，由于其产
生机理复杂具有极大的偶然性且缺乏再现性，所以目前国内外油轮的各种生
产作业过程都是按照一定的安全法规，技术规则或严格的操作规程执行。本
文从控制的角度出发，提出一个基于模糊逻辑的油轮静电防爆监控系统。其
方法是利用模糊逻辑控制的优点，不需建立精确的数学模型，根据专家的经
验，油轮安全规则制定出各危险因素（如水温，水压，氧气浓度，压力，温
度等）的模糊隶属函数和一系列的控制规则，利用模糊逻辑（MIN－MAX）
来处理各种模糊信息，使整个推理过程运算简单易于实现。同时还辅之以神
经网络（B—P算法），对不能直接现场测得的因素（舱内电位，电场强度），
用离线神经网络进行预测，为模糊控制服务，从而达到对各种危险因素的监
控，达到防患于未然的控制目的。文中结合现场总线技术，设计了集散式控
制系统。数据采集和通信由安装在现场的智能模块完成，对现场采集的数据
进行实时的控制，简化了系统结构，增强了可靠性，实现过程自动化控制，
将事故发生率降低至最小。还分别对系统在洗舱，装卸两种状态下进行了基
于实验数据的计算机控制模拟，并取得良好的效果。
Static electricity is a very dangerous potential factor to the safety of oil tankers. And many potential factors may give rise to electrostatic ignition or electrostatic explosion in the process of loading, unloading or shipping oil cargoes or during the course of washing tanks. So far all kinds of operations on oil tankers are carried out in accordance with specific safety regulations, technical rules or strict operation rules both at home and abroad because such accidents are complicated, accidental and unrepresentable.
This dissertation, from the viewpoint of control, is aimed to set up a fuzzy-logic-based supervising system to prevent electrostatic explosion of oil tankers. This system makes use of the advantages of fuzzy logic, and needs not to set up an accurate mathematics model. According to specialists?experience, such dangerous factors as water temperature, water pressure, oxygen density in the tank, the temperature and pressure of water vapor, ect. As well as control rules are made out in safety regulations for oil tankers. By using fuzzy logic (MIN-MAX reasoning), all sorts of fuzzy information are processed so as to simplify the control procedure in addition to realize it.
At the same time, neural networks (back-propagation algori-thm) is applied to calculate the dangerous factors (the voltage and the electric field intensity in tanks) which can not be measured on the spot, and to aid fuzzy FDcontrol. Thus the dangerous factors can be supervised and controlled, and electrostatic explosion is prevented. Based on field-bus control system (FCS), an intelligent monitor system is designed in which intelligent modes are used to complete data collection, simultaneous supervision and communication. This method simplifies the structure of the monitor and control system, enhances its reliability and has realized whole-process control, therefore greatly decreases the possibility of accident.
Furthermore, simulations computer test based on test data has been carried out in the two procedures of cargo loading and unloading and has shown very encouraging results.
运输及油轮的洗船过程中。众多潜在的因素会导致静电引燃引爆，由于其产
生机理复杂具有极大的偶然性且缺乏再现性，所以目前国内外油轮的各种生
产作业过程都是按照一定的安全法规，技术规则或严格的操作规程执行。本
文从控制的角度出发，提出一个基于模糊逻辑的油轮静电防爆监控系统。其
方法是利用模糊逻辑控制的优点，不需建立精确的数学模型，根据专家的经
验，油轮安全规则制定出各危险因素（如水温，水压，氧气浓度，压力，温
度等）的模糊隶属函数和一系列的控制规则，利用模糊逻辑（MIN－MAX）
来处理各种模糊信息，使整个推理过程运算简单易于实现。同时还辅之以神
经网络（B—P算法），对不能直接现场测得的因素（舱内电位，电场强度），
用离线神经网络进行预测，为模糊控制服务，从而达到对各种危险因素的监
控，达到防患于未然的控制目的。文中结合现场总线技术，设计了集散式控
制系统。数据采集和通信由安装在现场的智能模块完成，对现场采集的数据
进行实时的控制，简化了系统结构，增强了可靠性，实现过程自动化控制，
将事故发生率降低至最小。还分别对系统在洗舱，装卸两种状态下进行了基
于实验数据的计算机控制模拟，并取得良好的效果。
Static electricity is a very dangerous potential factor to the safety of oil tankers. And many potential factors may give rise to electrostatic ignition or electrostatic explosion in the process of loading, unloading or shipping oil cargoes or during the course of washing tanks. So far all kinds of operations on oil tankers are carried out in accordance with specific safety regulations, technical rules or strict operation rules both at home and abroad because such accidents are complicated, accidental and unrepresentable.
This dissertation, from the viewpoint of control, is aimed to set up a fuzzy-logic-based supervising system to prevent electrostatic explosion of oil tankers. This system makes use of the advantages of fuzzy logic, and needs not to set up an accurate mathematics model. According to specialists?experience, such dangerous factors as water temperature, water pressure, oxygen density in the tank, the temperature and pressure of water vapor, ect. As well as control rules are made out in safety regulations for oil tankers. By using fuzzy logic (MIN-MAX reasoning), all sorts of fuzzy information are processed so as to simplify the control procedure in addition to realize it.
At the same time, neural networks (back-propagation algori-thm) is applied to calculate the dangerous factors (the voltage and the electric field intensity in tanks) which can not be measured on the spot, and to aid fuzzy FDcontrol. Thus the dangerous factors can be supervised and controlled, and electrostatic explosion is prevented. Based on field-bus control system (FCS), an intelligent monitor system is designed in which intelligent modes are used to complete data collection, simultaneous supervision and communication. This method simplifies the structure of the monitor and control system, enhances its reliability and has realized whole-process control, therefore greatly decreases the possibility of accident.
Furthermore, simulations computer test based on test data has been carried out in the two procedures of cargo loading and unloading and has shown very encouraging results.
引文
1．R．C．佩奇，A．W．加德纳著，叶宝聪译．油船安全，人民交通出版社，1985．
2．JR休斯(英)．石油储存和装卸的防火防爆，石油工业出版社，1987．
3．范辛耕编．油轮静电，大连海运学院出版社，1988．
4．油船作业安全技术要求，人民交通出版社，1996．
5．D．Ru therford(英)．油轮货油装卸，人民交通出版社，1988．
6．交通部油船安全生产管理规则，人民交通出版社，．
7．王国福，任德俊编．油轮安全管理文献汇编，大连海运学院出版社，1990．
8．实船静电测试总结报告．(交通部上海船研所1994)
9．原油船蒸舱、洗舱可行性研究陆上模拟试验报告，(原油船蒸舱，洗船试验课题组1992)．
10．原油船蒸舱洗舱陆上静电测试报告，(原油船蒸舱，洗船试验课题组1992)．
11．诸静．模糊控制原理与应用，机械工业出版社，1995．
13．李晓忠，汪培庄．模糊神经网络，贵州科技出版社，1993．
14．张立明．人工神经网络的模型及其应用，复旦大学出版社，1993．
15．刘有才．模糊专家系统原理与设计，北京航空航天大学出版社，1995．
16．金以慧．过程控制，清华出版社，1993．
17．焦李成．神经网络系统理论，西安电子科技大学出版社，1990．
18．关于模糊规则的提取及利用神经网络进行优化的问题，自动化与仪器仪表，1996，NO．4．
19．模糊理论在油罐测量系统中的应用，自动化与仪器仪表，1996，NO．2．
20．模糊控制在聚乙烯反应釜温控中的应用，自动化与仪器仪表，1997，NO．1．
21．液化石油气罐装厂计算机监控系统，自动化与仪器仪表，1997，NO．4．
22．汤天浩，王启字．船舶综合自动化系统的展望与探索，见：张湘伟主编．跨世纪人才论中国交通．重庆大学出版社，1995．755—759．
23．汤天浩，李杰仁．智能控制——21世纪船舶自动化系统的核心，见：邵维文等主编．第五届全国海事技术研讨会论文集．海洋出版社，1999．125—130．
24．周又丁．浅谈现场总统控制系统的出现及特点．自动化仪器仪表， 1998(6)．
25．苏情．基于现场总线技术的全开放分散控制系统．测控技术，1999，18(5)．
26．阳宪惠．现场总线技术及其应用．清华大学出版社，1999．
27．杨青红．LON网络控制技术及应用．西安电子科技大学出版社，1999．
28．孙自强．生产过程自动化及仪表．华东理工大学出版社，1999．
29．孙来业．基于LonWorks的分散智能控制系统的研究，工业控制计算机，1999，12(6：36—38)．
30．凌志浩等．基于Neuron芯片的远程数据采集装置的设计．计算机自动测量与控制，1999，7(4：26—29)．
31．李幼文．数据库系统原理和技术，科学技术文献出版社，1995
32．王卫兵等．计算机网络接口技术．机械工业出版社，1997
33．李大友等．微型计算机接口技术．清华大学出版社，1998．
34．何克忠，李伟．计算机控制系统，清华大学大学出版社，1996．
35．秋晨旭，王殊．Lonworks控制网络技术及其应用电脑与信息技术，1999．
36．叶学松．分布式过程实时数据库的一种设计与实现，计算机工程与应用，1999．7
37．杨小枫，顾红军．工业自动化领域的企业网模型研究，清华大学学报，1999，39(7)．
38．孙自强．生产过程自动化及仪表．华东理工大学出版社，1999．
39．船舶管理集成系统，邬曼君译自Lloyd's Ship Manager/June 1998/34,35.
40. Echelon Lonworks Products. Echelon Co. 1998.
41. Echelon Lonworks Products. Echelon Co. 1998.
42. Reji, Reza S. Control Networks and the internet. Echelon Co. 1998.
43. On fuzz modelling using fuzzy neural networks with the backpropagetion algorithm(IEEE transaction on neural networks. vol. 3 No. 5 Sept, 1992)
44. Practical and computer ascessments of ignition hazards during tank washing and during ware action in part ballasted OBO cargo tank (J.N. chubb, J. electrostatics, vol. 1)
45. electrostatic charge generation during tank washing, redusing hazardons space potentials in tanks, a theoretical and erperimental approach (Lindbauer. R.L.J. electrostatics, vol. L)
46. Self-learning fuzzy controllers based on temproral back propagation (IEEE transactions on neural networks, vol. No. 5 Sept, 1992.
48. Marcelo Luismltra. Fleldbus Control System. Advances In Instrumentation andControl, Vol. 51,1996.
49. Li Jieren, and J. MindykOwski, Development of Engine Room Monitoring and Alarm System, Proceedings of the 1997
International Marine Electrotechnology Conference and Exhibition lareCE'97,China, Shanghai 1997 P131-135．
50． Tang Tianhao, et al An integrated intelligent method for marine monitoring system. Preprints of 14th World Congress of IFAC, vol. Q, P. 55-60, Beijing, China, 1999．
51． Angeline, PJ., et al. An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans. on Neural Networks. 1994, 5 (1) P54-65．
52． Fu K S. Learning Control System and Intelligent Control System; An Intersection of Artificial Intelligence and Automatic Control [J]. IEEE Trans, 1971: AC 16(1) : 70-72．
53． Hush, D. , et al. Recursive neural networks for signal processing and control. In Pro c. of 1st IEEE-SP Workshop on Neural Networks for Signal Processing, Princeton, NJ, 1991 P 523-532．
2．JR休斯(英)．石油储存和装卸的防火防爆，石油工业出版社，1987．
3．范辛耕编．油轮静电，大连海运学院出版社，1988．
4．油船作业安全技术要求，人民交通出版社，1996．
5．D．Ru therford(英)．油轮货油装卸，人民交通出版社，1988．
6．交通部油船安全生产管理规则，人民交通出版社，．
7．王国福，任德俊编．油轮安全管理文献汇编，大连海运学院出版社，1990．
8．实船静电测试总结报告．(交通部上海船研所1994)
9．原油船蒸舱、洗舱可行性研究陆上模拟试验报告，(原油船蒸舱，洗船试验课题组1992)．
10．原油船蒸舱洗舱陆上静电测试报告，(原油船蒸舱，洗船试验课题组1992)．
11．诸静．模糊控制原理与应用，机械工业出版社，1995．
13．李晓忠，汪培庄．模糊神经网络，贵州科技出版社，1993．
14．张立明．人工神经网络的模型及其应用，复旦大学出版社，1993．
15．刘有才．模糊专家系统原理与设计，北京航空航天大学出版社，1995．
16．金以慧．过程控制，清华出版社，1993．
17．焦李成．神经网络系统理论，西安电子科技大学出版社，1990．
18．关于模糊规则的提取及利用神经网络进行优化的问题，自动化与仪器仪表，1996，NO．4．
19．模糊理论在油罐测量系统中的应用，自动化与仪器仪表，1996，NO．2．
20．模糊控制在聚乙烯反应釜温控中的应用，自动化与仪器仪表，1997，NO．1．
21．液化石油气罐装厂计算机监控系统，自动化与仪器仪表，1997，NO．4．
22．汤天浩，王启字．船舶综合自动化系统的展望与探索，见：张湘伟主编．跨世纪人才论中国交通．重庆大学出版社，1995．755—759．
23．汤天浩，李杰仁．智能控制——21世纪船舶自动化系统的核心，见：邵维文等主编．第五届全国海事技术研讨会论文集．海洋出版社，1999．125—130．
24．周又丁．浅谈现场总统控制系统的出现及特点．自动化仪器仪表， 1998(6)．
25．苏情．基于现场总线技术的全开放分散控制系统．测控技术，1999，18(5)．
26．阳宪惠．现场总线技术及其应用．清华大学出版社，1999．
27．杨青红．LON网络控制技术及应用．西安电子科技大学出版社，1999．
28．孙自强．生产过程自动化及仪表．华东理工大学出版社，1999．
29．孙来业．基于LonWorks的分散智能控制系统的研究，工业控制计算机，1999，12(6：36—38)．
30．凌志浩等．基于Neuron芯片的远程数据采集装置的设计．计算机自动测量与控制，1999，7(4：26—29)．
31．李幼文．数据库系统原理和技术，科学技术文献出版社，1995
32．王卫兵等．计算机网络接口技术．机械工业出版社，1997
33．李大友等．微型计算机接口技术．清华大学出版社，1998．
34．何克忠，李伟．计算机控制系统，清华大学大学出版社，1996．
35．秋晨旭，王殊．Lonworks控制网络技术及其应用电脑与信息技术，1999．
36．叶学松．分布式过程实时数据库的一种设计与实现，计算机工程与应用，1999．7
37．杨小枫，顾红军．工业自动化领域的企业网模型研究，清华大学学报，1999，39(7)．
38．孙自强．生产过程自动化及仪表．华东理工大学出版社，1999．
39．船舶管理集成系统，邬曼君译自Lloyd's Ship Manager/June 1998/34,35.
40. Echelon Lonworks Products. Echelon Co. 1998.
41. Echelon Lonworks Products. Echelon Co. 1998.
42. Reji, Reza S. Control Networks and the internet. Echelon Co. 1998.
43. On fuzz modelling using fuzzy neural networks with the backpropagetion algorithm(IEEE transaction on neural networks. vol. 3 No. 5 Sept, 1992)
44. Practical and computer ascessments of ignition hazards during tank washing and during ware action in part ballasted OBO cargo tank (J.N. chubb, J. electrostatics, vol. 1)
45. electrostatic charge generation during tank washing, redusing hazardons space potentials in tanks, a theoretical and erperimental approach (Lindbauer. R.L.J. electrostatics, vol. L)
46. Self-learning fuzzy controllers based on temproral back propagation (IEEE transactions on neural networks, vol. No. 5 Sept, 1992.
48. Marcelo Luismltra. Fleldbus Control System. Advances In Instrumentation andControl, Vol. 51,1996.
49. Li Jieren, and J. MindykOwski, Development of Engine Room Monitoring and Alarm System, Proceedings of the 1997
International Marine Electrotechnology Conference and Exhibition lareCE'97,China, Shanghai 1997 P131-135．
50． Tang Tianhao, et al An integrated intelligent method for marine monitoring system. Preprints of 14th World Congress of IFAC, vol. Q, P. 55-60, Beijing, China, 1999．
51． Angeline, PJ., et al. An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans. on Neural Networks. 1994, 5 (1) P54-65．
52． Fu K S. Learning Control System and Intelligent Control System; An Intersection of Artificial Intelligence and Automatic Control [J]. IEEE Trans, 1971: AC 16(1) : 70-72．
53． Hush, D. , et al. Recursive neural networks for signal processing and control. In Pro c. of 1st IEEE-SP Workshop on Neural Networks for Signal Processing, Princeton, NJ, 1991 P 523-532．