Abstract
In oil and gas plants, the cost of devices applicable for supervising and controlling systems directly depends on the transmission and storage systems, which are related to the data size of process variables. In this paper, process variables frequency-domain and statistical analysis results have been studied to infer if there exists any possibility to reduce data size of the process variables without loss of any necessary information. Although automatic control is not applicable in a shutdown condition, for generalization of the obtained results, unscheduled shutdown data has also been analyzed and studied. The main goal of this paper is to develop an applicable algorithm for oil and gas plants to decrease the data size in controlling and monitoring systems, based on well-known and powerful mathematical techniques. The results show that it is possible to reduce the size of data dramatically (more than 99% for controlling, and more than 55% for monitoring purposes in comparison with existing methods), without loss of vital information and performance quality.
Highlights
In order to respond to increasing demand for safe and efficient oil and gas plant operation while considering environmental regulations, the subject of “process control” has become increasingly important in recent years [1]
Some activities such as supervisory control, data logging and performance monitoring, with their hierarchical relation illustrated in Figure 1, are pursued based on available process variables [2]
As a result of fast Fourier transform (FFT) analysis of process variables and based on Nyquist theorem, we found 5 h as the shortest sampling interval for process variables with DC component and 0.7 h for process variables without DC frequency component for change analysis
Summary
In order to respond to increasing demand for safe and efficient oil and gas plant operation while considering environmental regulations, the subject of “process control” has become increasingly important in recent years [1]. Some activities such as supervisory control, data logging and performance monitoring, with their hierarchical relation illustrated, are pursued based on available process variables [2] These variables include pressure and flow rate of fluids, temperature of flame or materials, liquid level in tanks, and other quantitative items [3]. Some of these variables are measured by sensors, transferred on industrial networks, processed in distributed or central control systems, and monitored in control consoles [4]. Transmission and storage of these variables has considerable cost in many industrial plants, such as oil and gas refineries, and so reduction of these expenses is vital for managers [6]
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