Abstract
Reducing process variability is crucial to reach a more profitable operating point. Periodical disturbances, however, impose barriers to achieve this goal. Their effect can be strong since one disturbance that appears in a specific loop of a highly coupled plant can be seen in several loops. Thus, isolating their source and diagnosing their cause are essential. In this work, we describe the application of spectral independent component analysis to isolate a periodical disturbance that has a strong impact on the final variability in a polyethylene plant located in Southern Brazil. After the first analysis, the source was detected and the cause identified: valve stiction. To identify the cause (valve, bad tuning, or periodic disturbance), we used the methodology based on higher-order statistics. Once the valve problem had been overcome, the product variance was reduced by 93%.
Highlights
One frequent cause of poor process performance is the presence of plant-wide periodical disturbances (Thornhill and Horch, 2007) whose effect can spread through the entire plant, inhibiting the process from achieving a more profitable operating point
This section describes the application of the methodologies in the petrochemical plant previously described
In plants with a high number of recycles, periodical disturbances can strongly affect loop variability, because one oscillating loop can have a widespread effect in the whole plant
Summary
One frequent cause of poor process performance is the presence of plant-wide periodical disturbances (Thornhill and Horch, 2007) whose effect can spread through the entire plant, inhibiting the process from achieving a more profitable operating point. One oscillation that starts in a specific loop can be propagated to the entire process, increasing the product variability. It is clear that detecting and eliminating plant-wide oscillations are essential to ensure process profitability and reduce product variability. The diagnostics of the loop that is the source of the disturbance and the cause of the oscillation is not straightforward. This is the scenario seen in a petrochemical plant located in Southern Brazil. One periodical oscillation affects the product variability and, because of plant recycles, the engineers could not detect either the source or the cause. Our goal is to detect and find the source of the oscillation, eliminating it (if possible) after diagnosing the cause
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