A model-based machine learning to develop a PLC control system for Rumaila degassing stations
العناوين الأخرى
نموذج التعلم الآلي لتطوير نظام التحكم PLC لمحطات عزل غاز الرميلة
المؤلفون المشاركون
al-Radi, Muhammad Salah Hamzah
ak-Kamil, Safa Jamil Dawud
Tamas, Szakacs
المصدر
Journal of Petroleum Research and Studies
العدد
المجلد 2020، العدد 29 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
وزارة النفط مركز البحث و التطوير النفطي
تاريخ النشر
2020-12-31
دولة النشر
العراق
عدد الصفحات
18
التخصصات الرئيسية
الموضوعات
- التعلم الآلي
- المنشآت
- حقول النفط
- الأعلاف
- أنظمة التحكم
- استرجاع المعلومات
- الشبكات العصبية(الحاسبات الإلكترونية)
- أنظمة التحكم الإشرافي
الملخص EN
Degassing station breakdowns can be dangerous to the operator health and the environment.
Programmable logic controllers (PLCs) are key modules of manufacturing control systemsthat are applied in the complex oil and gas units to reduce manpower and unnecessary faults.
However, feeding a PLC with data is a difficult part due to the need of system log files whichrecords all events that occur in the oil fields and provide visibility to a given environment.
Moreover, most critical chemical processing plants and oil distributions are visualized andinspected by Supervisory Control and Data Acquisition Systems (SCADA).
These systemshave been focused on safety, and there are issues that they could be the target of worldwideterrorists.
Along with the frequently rising internet-related attacks, there is indication that ourdegassing stations may similarly be susceptible; for that reason, it is essential to secure PLCand SCADA from undesired incidents.
Recently, machine learning (ML) has been increasinginterest in industrial systems to detect, identify, and store information.
Therefore, we proposeto apply an advance ML based on deep neural networks to the PLC system with the purposeof: 1) detecting anomalous or irregular PLC actions; 2) Optimizing the operation of systemsand its facilities; 3) allowing the equipment to respond to changing and novel scenarios; 4)Making predictive maintenance possible.
The SIMATIC S7-1214 CPU universal TIAplatform was used as the main decision-making module.
Experimental results demonstratethe effectiveness and utility of the proposed approach to process large amounts of dataanalytics and sensor measurements, allows it to spot potential problems and provide Degassing station breakdowns can be dangerous to the operator health and the environment.
Programmable logic controllers (PLCs) are key modules of manufacturing control systemsthat are applied in the complex oil and gas units to reduce manpower and unnecessary faults.
However, feeding a PLC with data is a difficult part due to the need of system log files whichrecords all events that occur in the oil fields and provide visibility to a given environment.
Moreover, most critical chemical processing plants and oil distributions are visualized andinspected by Supervisory Control and Data Acquisition Systems (SCADA).
These systemshave been focused on safety, and there are issues that they could be the target of worldwideterrorists.
Along with the frequently rising internet-related attacks, there is indication that ourdegassing stations may similarly be susceptible; for that reason, it is essential to secure PLCand SCADA from undesired incidents.
Recently, machine learning (ML) has been increasinginterest in industrial systems to detect, identify, and store information.
Therefore, we proposeto apply an advance ML based on deep neural networks to the PLC system with the purposeof: 1) detecting anomalous or irregular PLC actions; 2) Optimizing the operation of systemsand its facilities; 3) allowing the equipment to respond to changing and novel scenarios; 4)Making predictive maintenance possible.
The SIMATIC S7-1214 CPU universal TIAplatform was used as the main decision-making module.
Experimental results demonstratethe effectiveness and utility of the proposed approach to process large amounts of dataanalytics and sensor measurements, allows it to spot potential problems and provide possiblesolutions
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Radi, Muhammad Salah Hamzah& ak-Kamil, Safa Jamil Dawud& Tamas, Szakacs. 2020. A model-based machine learning to develop a PLC control system for Rumaila degassing stations. Journal of Petroleum Research and Studies،Vol. 2020, no. 29, pp.1-18.
https://search.emarefa.net/detail/BIM-1266693
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Radi, Muhammad Salah Hamzah…[et al.]. A model-based machine learning to develop a PLC control system for Rumaila degassing stations. Journal of Petroleum Research and Studies No. 29 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1266693
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Radi, Muhammad Salah Hamzah& ak-Kamil, Safa Jamil Dawud& Tamas, Szakacs. A model-based machine learning to develop a PLC control system for Rumaila degassing stations. Journal of Petroleum Research and Studies. 2020. Vol. 2020, no. 29, pp.1-18.
https://search.emarefa.net/detail/BIM-1266693
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
-
رقم السجل
BIM-1266693
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر