Performance assessment of multivariate process using time delay matrix

Abstract

Researchers keep trying to find a way to reduce the requirement knowledge of Multivariate Process for performance assessments. Till now, the knowledge of interactor matrix or first several Markov parameter matrices are at least required to obtain the minimum variance benchmark in the performance assessment of Multivariate Process. In this paper, a novel minimum variance performance assessment technique is proposed for multivariate processes. Under the condition that the time delay indicator matrix can be written as row echelon form by row or/and column shift operations, the delay order is determined and the minimum variance (MV) benchmark can be straightforward obtained when the knowledge of time delay matrix is available. Comparing with the traditional approaches, the first several Markov parameter matrices and the knowledge of the plant are both not necessary required based on the proposed technique. It has been proved that the proposed technique can directly solve the problem of the performance assessment of Multivariate Process. The validity of the proposed algorithm will be verified through numerical examples, which include the practical industrial model - ‘Shell’ oil fractionator process.