Aspen Process Statistical Analyzer

(formerly Aspen IQmodel Powertools)

Empirical model-building environment for process analysis applications

Additional Resources

Product Brochure

Aspen Process Statistical Analyzer is a tool set for data manipulation and identification of non-linear models such as the Bounded Derivative Network (BDN). The export function allows these models to be used directly in inferential applications or the models can be used indirectly to provide gain predictions at a given operating point to a control application. Aspen Process Statistical Analyzer is a core element of AspenTech's aspenONET Advanced Process Control applications.

Features

Data Management – Data import from Microsoft® Excel or MATLAB
Data Manipulation – Data matrix management including combining data sets, variable transforms, re-sampling, removing or adding variables, and combining variables through calculations
Data Visualization – Data plotting including trending and scatter plots
Data Pre-Screening – Data cleaning through a variety of a variety of mechanism including filtering, multivariate outlier detection, and manual cutting
Multivariate Analysis – Powerful multivariate statistical analysis for outlier detection and/or process monitoring applications (includes PLS, PCA and highly interactive 2D/3D multivariate charts, Q/T2 Stats, Contribution Plots)
SPC Support – Q statistics allow users to view contributions from key variables over time to investigate which variables are causing the Q statistic to violate previously defined upper control limits
Cause and Effect Investigation – Correlation analysis for determining cause and effect relationships and for time aligning data
Training/Testing Data Selection – Complete flexibility over data used for building and validating models
Regression – Support for a variety of regression model types including Multiple Linear Regression, SERVA analysis, Partial Least Squares, and Principal Components Analysis
Advanced Modeling – A variety of nonlinear model types including static or dynamic (first order Hammerstein) Gain Constrained models (Bounded Derivative Networks)
Nonlinear Controller Modeling – Capability to build and export Aspen Nonlinear Controller models, which are gain-constrained general order nonlinear Wiener models with guaranteed bounds on the steady state gains (licensed separately with Aspen Nonlinear Controller)

Benefits

Reduces the time and effort required to transform process data into suitable data sets for building empirical models
Simplifies the task of building model types whose structure and configuration are especially suited to online applications
Improves manufacturing operations by enabling better predictions of how the process will perform in the future through greater model accuracy