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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
