LAUSR

Abstract Multivariate data analysis is a powerful tool for process monitoring and data analysis. The theoretical methodology of real-time multivariate data analysis has been studied in the last decade. However, the effect of dataset size on modeling structure and fault detection ability has not been reported yet. In this paper, requirements for a minimum dataset for multivariate data analysis modeling are studied, and a practical approach is provided to evaluate the modeling structure. A method based on statistical index g2 and cross-validation is proposed to determine a minimum dataset size of a valid model for statistical process monitoring. The proposed method was built on the linear PLS model and elaborated by case studies using both batch and continuous processes. This paper provides theoretical development of multivariate data analysis and demonstrates its application in chemical processes.