aspenONE^® V7 Manufacturing
and Supply Chain

Driving Higher Profitability in a Changing Global Economy

Inferential Measurements and Prediction Modeling

Inferentials are a supplement for infrequently measured qualities or critical sensors and help support environmental compliance. Aspen IQ enables modeling and implementation of inferred product qualities, and makes it possible to implement linear or non-linear inferential sensors online. Inferential sensors are fundamental elements of many advanced process control systems. Key parameters (such as naphtha 95% point and polymer melt index) are often inferred rather than directly measured.

Through Aspen IQ, you can model and implement inferential sensors across your site.

Example Applications:

• Refining: Distillation, viscosity, cloud point
• Emissions Monitoring: NO2, CO2, particulate
• Polymers: Melt Index, viscosity, coatability
• Food & Beverage: Food taste, wine grade
• Steel: 30-day hardness
• Pulp and Paper: Brightness / Kappa Index
• Semiconductors: Plasma etch selectivity, variance, and rate

Inferential model types include FIR, PLS, fuzzy PLS, BDN, hybrid neural net, monotonic neural net, linearized rigorous model-based, and custom equations. Along with empirical inferentials, the Aspen Control Platform provides the ability to incorporate Aspen’s industry-leading engineering simulation models for building rigorous inferential applications. Aspen HYSYS®, Aspen HYSYS® Petroleum Refining (formerly Aspen RefSYS®), Aspen Polymers (formerly Aspen Polymers Plus), and Aspen Customer Modeler® are supported.

Online Features

• Built-in steady-state detector
• Provides both analyzer and lab model updating
• Wide range of DCS and information system interfaces available
• Enabled for remote monitoring
• No code generation or programming required

Offline Features

• A broad suite of tools for developing inferential sensors: PLS, fuzzy PLS, and neural-network and linear-ized rigorous model-based approaches
• Graphical analysis tools for model evaluation
• Model building tools such as variable selection, dead-time detection, and dynamics analysis
• Prediction libraries allow for future expansion (e.g., with process-specific models)

Data Pre-processing Features

• Handles multiple files
• Allows both graphical and numeric cutting of bad data
• Allows interpolation for replacing bad or missing data
• Allows averaging of training data to support cumulative lab samples

Supported Model Types

• Linear PLS
• Fuzzy PLS
• Hybrid Neural Net (HNN)
• Linear-ized Rigorous Models
• Algebraic and FIR Models