Abstract

Process models play an important role in the process industry. They are used for simulation purposes, quality control, fault detection, and control design. Many researchers have been engaged in model identification. However, it is difficult to find an analytical identification method that provides a good model and requires a relatively simple experiment. This is the advantage of the method of moments. In this paper, an analytical method based on the measurement of the process moments (characteristic areas) is proposed, to identify the five-parameter model (second-order process with zero plus time delay) from either the closed-loop or open-loop time responses of the process (in the time-domain), or the general-order transfer function with time delay (in the frequency-domain). The only parameter required by the user is the type of process (minimum phase or non-minimum phase process), which in practice can be easily determined from the time response of the process. The method can also be used to reduce the higher-order process model. The proposed identification method was tested on several illustrative examples, and compared to other identification methods. The comparison with existing methods showed the superiority of the proposed method. Moreover, the tests confirmed that the algorithm of the proposed method works properly for a wide family of process models, even in the presence of moderate process noise.

Highlights

  • A process model is a set of equations that describe the input and output relationships of the actual process

  • The only parameter required by the user is the type of process, which in practice can be determined from the time response of the process

  • The proposed identification method was tested on several illustrative examples, and compared to other identification methods

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Summary

Introduction

A process model is a set of equations that describe the input and output relationships of the actual process. The same characteristic areas can be obtained from any process transfer function with a time delay (frequency-domain approach) [43], and SOPDT (four-parameter model) can be calculated analytically from the moments or characteristic areas, as shown in [28,44]. We propose a method to identify the five-parameter model from characteristic areas: the second-order process with zero plus time delay. The characteristic areas can be calculated from an arbitrary change in the steady-state of the process, in the closed-loop or open-loop configuration (time-domain approach). The characteristic areas can be obtained from the arbitrary process transfer function with a time delay (frequency-domain approach). To save the reader effort and time, all MATLAB files that are needed to implement the calculation of the characteristic areas are available online [46]

Model Identification
Illustrative Examples
Model Identification from a General-Order Transfer Function with a Time Delay
Model Identification from an Open-Loop Time Response of the Process
Conclusions
Full Text
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