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.

研究人员一直在努力寻找一种方法来减少对绩效评估的多元过程的需求知识。到目前为止，在多变量过程的性能评估中，至少需要获得交互器矩阵或前几个马尔可夫参数矩阵的知识才能获得最小方差基准。在本文中，提出了一种针对多元过程的新型最小方差性能评估技术。在可以通过行或/和列移位操作将时延指示符矩阵写为行梯形形式的条件下，当了解时延矩阵时，可以确定延迟顺序并可以直接获得最小方差（MV）基准。可用。与传统方法相比，基于所提出的技术，前几个马尔可夫参数矩阵和工厂知识都不是必需的。实践证明，该技术可以直接解决多元过程性能评估的问题。将通过数值示例验证所提出算法的有效性，其中包括实用的工业模型-“壳牌”石油分馏工艺。