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Big Data Analytics Applied to Real-World Control Systems: From Instruments to Advanced Controls

机译:应用于现实世界控制系统的大数据分析:从仪器到高级控制

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A modern control system at a chemical plant or refinery can gather gigabytes of data, every day. Savvy engineers have applied software tools and methodologies to extract immediate value. This presentation shows how users are applying big data analytics to improve the performance of all aspects of control systems, from instrumentation to advanced controls. Manufacturing control systems have been a source for big data since the late 1980's. Digital control systems started to capture data, and feed it to process data historians. Initially, users were selective, and only captured the most important process measurements, such as temperatures, flows, and pressures. As computer memory, storage, and network speeds increased capacity, users were quickly storing thousands of pieces of information about the process, at faster and faster sampling rates. Today, in a modern refinery or a large chemical plant, it is common to collect tens of thousands of process measurements, valve positions, and control system statuses, at sampling rates as fast as once per second. It is even possible to store all of that data without loss of information in data compression. Analyzing this flood of data to find the most important information has been a challenge to engineers for several decades. This presentation shows new tools and techniques that have demonstrated success in finding, prioritizing, and resolving major issues in process plants. Specific techniques and examples will be shown for: 1. Identification of instrument failures 2. Finding and quantifying mechanical issues with control valves 3. Measuring the effectiveness of the control system 4. Identifying control strategies that are no longer working properly 5. Measuring the performance of advanced process control. The author's analysis has shown that most plant control systems are operating far below their potential. Furthermore, using big data analytics, it is possible to drive significant, targeted improvements in a very short time. Attendees will also learn how to estimate the potential economic benefit potential at their plant site.
机译:化工厂或炼油厂的现代控制系统每天都可以收集千兆字节的数据。 Savvy工程师已应用软件工具和方法以提取立即值。此演示文稿显示了用户如何应用大数据分析,以提高控制系统的所有方面的性能,从仪器到高级控制。自20世纪80年代后期以来,制造控制系统一直是大数据的来源。数字控制系统开始捕获数据,并喂它以处理数据历史人员。最初,用户是选择性的,并且只捕获了最重要的过程测量,例如温度,流动和压力。随着计算机内存,存储和网络速度的增加容量,用户快速将数千条关于该过程的信息存储,更快,更快的采样率。如今,在现代化的炼油厂或大型化工厂中,常常收集成千上万的过程测量,阀门位置和控制系统状态,以每秒快速的采样率。甚至可以存储所有数据,而不会丢失数据压缩中的信息。分析这一数据泛滥,以找到最重要的信息是对工程师几十年来对工程师的挑战。此演示文稿显示了新的工具和技术,这些工具和技术在寻找,优先顺序和解决过程工厂中的主要问题方面取得了成功。将显示具体技术和实例:1。仪器故障的识别2.使用控制阀的找到和定量机械问题3.测量控制系统的有效性4.识别不再正常工作的控制策略5.测量性能。测量性能高级过程控制。作者的分析表明,大多数植物控制系统都远低于其潜力。此外,使用大数据分析,可以在很短的时间内推动显着的目标改进。与会者还将学习如何估算其工厂网站的潜在经济效益潜力。

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