Abstract
This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is used as an index for steady-state determination. In this contribution, the novel SSI method is compared to other available techniques, namely the F-like test, wavelet transform and a polynomial-based approach. These methods are implemented for SSI of three different case studies. In the first case, a simulated dataset is used for calibrating the output-based SSI methods. The second case corresponds to a literature nonlinear continuous stirred-tank reactor (CSTR) example running at different steady states in which the ARX-based approach is tuned with the available input-output data. Finally, an industrial case with real data of a depropanizer column from PETROBRAS S.A. considering different pieces of equipment is analyzed. The results for a reflux drum case indicate that the wavelet and the F-like test can satisfactorily detect the steady-state periods after careful tuning and when respecting their hypothesis, i.e., smooth data for the wavelet method and the presence of variance in the data for the F-like test. Through a heat exchanger case with different measurement frequencies, we demonstrate the advantages of using the ARX-based method over the other techniques, which include the aspect of online implementation.
Highlights
Steady-state identification (SSI) methods allow the determination of when the process has attained its stationary operation
Phenomenological models that are used for online implementation of process systems frameworks, such as model predictive control (MPC) and real-time optimization (RTO), are typically based on lumped parameters that contain phenomena information embedded in their values [1]
We present the four addressed steady-state identification techniques, which range from the classical to novel ones
Summary
Steady-state identification (SSI) methods allow the determination of when the process has attained its stationary operation. Korbel et al [19] presented an approach for SSI for online applications by a combination of wavelet and statistical techniques This method was applied to a paper machine and large-scale processes for steady-state detection in real time. The following techniques are applied to the case studies: the F-like test [4], the polynomial-based approach [22], the wavelet method [18] and the proposed ARX-based framework. In each of these techniques, tests are proposed to help guide the user decision on whether the system considered is at the steady state or not. The paper is closed with conclusions and future directions
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