Validating a process model with experimental data is a key part in ensuring that the model is both accurate and predictive across a range of scales. PSE has recently augmented its state-of-the-art model validation technology, significantly streamlining the validation workflow. It has always been possible to process steady state and dynamic experimental, pilot or operational data from a variety of sources, estimate a large number of parameters simultaneously, and receive a detailed statistical analysis to quantify the predictive accuracy of the model. The new developments make it much easier to do so. They include easy-to-use data-import and processing, the ability to select some of the data for fitting and some for independent ‘blind test’ validation, plus significantly improved reporting and visualisation of results. In addition, the model validation activity can take advantage of PSE’s new high-performance computing capabilities , with full parallelisation to significantly reduce time for parameter estimation.

Topics covered

Topics will include:

  • Model validation workflow
  • Data import
  • Parameter fitting
  • Blind tests
  • Reporting
  • Parallel computing


Mayank Patel is a Senior Application Engineer at Process Systems Enterprise. A Chemical Engineer with a degree from Imperial College London, Mayank supports Chemicals and Petrochemicals companies in the application of Advanced Process Modelling.



45 minutes plus Q&A

Who should attend?

The webinar will be of use to anyone tasked with creating and maintaining predictive models including engineers and managers in the process industries as well as researchers and students from academic institutions.