Predictive Modelling (available December 2019)

Combining a comprehensive suite of empirical modelling tools and a simplified user interface, the platform enables users to quickly start making sense of their data.

Both linear and non-linear models are available, utilising state-of-art model identification algorithms. Models are validated using standard performance measures, sensitivity analysis and cross validation metrics. Any models may be interchanged between process monitoring, control and optimization within the same environment, to quickly develop the optimum improvement strategy.

Key features include:

  • RLS, PLS regression algorithms
  • Sensitivity analysis for Steady State, Transfer Function and Time Series format
  • Radial Basis Function Neural Network for Non-linear Modelling
  • Integration with PSE gPROMS mechanistic modelling
  • Linking with Python Libraries provides a user-friendly interface with NumPy and SciPy


Our Clients & Partners

Selection of our clients and key partners we work with to improve process efficiency

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