A smart PLC-SCADA framework for monitoring petroleum products terminals in industry 4.0 via machine learning

作     者：Rashad, Ossama Attallah, Omneya Morsi, Iman 

作者机构：Arab Acad Sci Technol & Maritime Transport Coll Engn & Technol Dept Elect & Commun Engn Alexandria 1029 Egypt 

出 版 物：《MEASUREMENT & CONTROL》 (Meas. Control)

年 卷 期：2022年第55卷第7-8期

页      面：830-848页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 

基　　金：The author(s) received no financial support for the research  authorship  and/or publication of this article 

主　　题：industry 4 0 SCADA HMI programmable logic controller real-time systems oil refinery process petroleum products machine learning level detection 

摘      要：The paper introduces a Programmable Logic Controller (PLC) / Human Machine Interface (HMI) system incorporated along with machine learning (ML) classifiers. Although some other studies have incorporated ML techniques to predict and control petroleum product terminals in terms of concentration, the proposed framework incorporates Add On Instruction (AOI) programming, PLC, and ML methods to automatically monitor petroleum products terminals. The framework adds an AOI in programming to achieve maximum usage of processor capabilities. Moreover, it uses AOI for programming in cooperation with the ladder diagram (LD). This leads to simplifying the LD graphical programming language, reducing the time of scanning, and making facilitate troubleshooting. The AOI is merged with ML to automate tank level detection and maintain good operational conditions and consequently protect these expensive essential assets. The introduced framework consists of three stages. The first stage is the PLC programming phase where the PLC is created using Add-On instructions. Next, HMI graphic displays are drawn and linked to the PLC tags in the following stage. During the third stage, the actual process readings are applied to the system based on ML algorithms to test its functionality. The proposed system results indicates a reduction in the LD number, highest program size, and maximum time of scanning. The results indicate that the AOI can help to trace the program more easily in fault situations. Besides, additional program instructions could reduce processor memory, system construction costs, and upgrade projects.