STATISTICA Solutions for Chemical and Petrochemical
Chemical and Petrochemical organizations are among the largest users of STATISTICA applications, benefiting from STATISTICA analytics both in Research & Development and Manufacturing.
Research & Development
One contributing factor in a chemical/petrochemical company’s success is the ability of the R&D scientists to discover and develop a product formulation with useful properties.
The STATISTICA platform results in hard and soft ROI by:
- Empowering scientists with the analytic and exploratory tools to make more sound decisions and gain greater insights from the precious data that they collect
- Saving the scientists’ time by integrating analytics in their core processes
- Saving the statisticians’ time to focus on the delivery and packaging of effective analytic tools within the STATISTICA framework
- Increasing the level of collaboration across the R&D organization by sharing study results, findings, and reports
STATISTICA provides a broad base of integrated statistical and graphical tools including:
- Tools for basic research such as Exploratory Graphical Analysis, Descriptive Statistics, t-tests, Analysis of Variance, General Linear Models, and Nonlinear Curve Fitting.
- Tools for more advanced analyses such as a variety of clustering, predictive modeling, classification and machine learning approaches including Principal Components Analysis, The STATISTICA platform meets the needs of both scientists and statisticians in your R&D organization.
Chemical and Petrochemical organizations have deployed STATISTICA within their manufacturing processes in several ways:
- These organizations have arrived at a greater understanding of their process parameters and their relationship to product quality by applying STATISTICA‘s multivariate statistical process control (SPC) techniques. STATISTICA integrates with their process information repositories and LIMS systems to retrieve the data required to perform these analyses.
- These organizations have also utilized the deployment capabilities of STATISTICA‘s Data Mining algorithms to integrate advanced modeling techniques such as Neural Network, Recursive Partitioning approaches (CHAID, C&RT, Boosted Trees), MARSplines, Independent Components Analysis, and Support Vector Machines. STATISTICA allows them to deploy a fully-trained predictive model in Predictive Modeling Markup Language (PMML), C++ and Visual Basic for ongoing monitoring of a process. These models based, once trained and evaluated on historical data, are deployed as “soft sensors” for the ongoing monitoring and control of process parameters.