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In this work methods for generating sets of excitation signa ls for system identification are discussed and evaluated. This study is focused on ap plic tions in the process industries, particularly in oil refining. The operational constraints of the oil refining industry are becoming increasingly severe due to increased energy demand, quality of products, oil price variations, market competition and other economic, environmental and energy e fficiency factors. In this scenario the use of model predictive control techniques is i ncreasing, thus the demand for plant identification as well. The particular characteristics of the processing plants im pose restrictions to the project and application of excitation signals. Various met hods for generating signals accessible in the literature and three new others proposed i n this work are compared with reference to these restrictions. One of the main constraints for applying excitation signals for identification is relative to the period available for excitation of the plant . Thus, for the proper use of this time interval, it is necessary to ensure the success o f an experiment prior to its implementation. In the literature there are several per formance measurements for evaluation of sets of excitation signals prior to the experi m nt. This work proposes two new measures to complement the evaluation. The effectiveness of the generating methods and performance measu rements for excitation signals is evaluated by simulation of multivari able identification of two typical oil refining plants. The conclusions of this work briefly present these evaluations, as well as some suggestions of future work for the continuity of he current research.

Projeto de sinais de excitação para identificação multivariável de plantas industriais.