(183a) MKS Data Analytics Solutions Umetrics Suite - A Platform for BIG Data Analytics, Multivariate Process Modeling, Monitoring and Prediction | AIChE

(183a) MKS Data Analytics Solutions Umetrics Suite - A Platform for BIG Data Analytics, Multivariate Process Modeling, Monitoring and Prediction

Authors 

Persson, A. - Presenter, MKS Data Analytics Solutions
MKS Data Analytics Solutions contributes in the area of BIG Data Analytics as a world leading provider of user friendly software packages for multivariate data analysis (MVDA) and design of experiments (DOE) through the Umetrics Suite of products. Software solutions are graphically driven and complete with surrounding services ranging from training to consulting, implementation and support. With 25 years of experience MKS Data Analytics Solutions is proud to offer easy-to-use robust and mature solutions as well as a highly praised customer support program. The Umetrics Suite includes the products MODDE Go, MODDE Pro, SIMCA, SIMCA-Q and SIMCA-online and application implementations span a range of industries including biopharmaceutical, petrochemical, chemical, food, semiconductor and pulp and paper.

The presentation will highlight a number of success stories of off-line and on-line applications of Umetrics Suite while emphasizing graphical power and capability of rapidly extracting information from and making sense of volume datasets originating from complex arrangements of multiple data sources. Visualization and effective utilization of software tools to fulfill objectives, create return on investment and increase the bottom line will be discussed.    

Examples of applications include real-time multivariate process monitoring, open and closed loop multivariate model predictive control, prediction of critical process quality attributes, increasing process understanding, process optimization and process trouble shooting. MKS Data Analytics Solutions Umetrics Suite is utilized throughout research, development and manufacturing, both for continuous and batch processes.