Abstract

A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.

Highlights

  • Pneumatic Control Valves Based on a Processing plants consist of a large number of components

  • Data obtained with failure elements were modeled to fit the statistical reliability models mentioned in the previous section

  • The paper aimed to visualizes faults and present a reliability assessment of pneumatic valve failure, which is often used in processing plants

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Summary

Introduction

Pneumatic Control Valves Based on a Processing plants consist of a large number of components. As industries have already moved into the era of digitization, these automatized operations need to be reliable. It is necessary to look into all parameters which affect the cost of overall operation; assessments of reliability are essential for any plant. Each machine/plant consists of a large number of sub-systems and each sub-system has many large components attached with each other. Reliability analysis can be performed by both qualitative and quantitative measurements, as [1] reliability is the probability of equipment staying operational without failing for a given time interval [2]. The reliability of the plant/machine can be found in regard to normal operating conditions or during high production or scheduled outages [3]

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