Oscillations occurring in industrial process plants often reflect the presence of severe disturbances affecting process operations. Accurate detection and root-cause analysis of oscillations is of great interest for the economic viability of the process operation. Standard oscillation detection and root cause analysis methods require a large enough number of data samples. Unrelated transient changes superimposed on the oscillation pattern reduce the number of useful data samples. The present paper proposes simple heuristic methods to effectively detect and remove two types of transient changes from oscillatory signals, namely step changes and spikes. The proposed methods are used to pre-process oscillatory time series. The accuracy gained when using auto-correlation function method for oscillation detection 1 and transfer entropy method for oscillation propagation 2 is experimentally evaluated. The methods are carried out on a 1.3-Butadiene production process where several measurements showed an established oscillation occurring after a production level change.
A key performance indicator (KPI) is a metric used to evaluate factors that are crucial to the success of an organization. Usually these factors are represented by normalized numbers (e.g. 0-100%) and maximizing or minimizing them is equivalent to making progress toward operational or strategic goals of the organization. Different KPIs of variable levels of complexity exist, rating factors such as customer and employee satisfaction, compliance, security or economic success. This article focuses on KPIs closely related to process operations like production performance, energy consumption, availability and safety. These KPIs are mainly based on measurable process data.
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