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PSE Model-Based Innovation Prize

Judges' comments and judgement criteria

The MBI Prize judges

Prof. Stratos Pistikopoulos (Chair)

Prof. Stratos Pistikopoulos
Stratos Pistikopoulos is Professor of Chemical Engineering at Imperial College London and the Director of its Centre for Process Systems Engineering (CPSE).

Prof. Rafiqul Gani

Prof. Rafiqul Gani
Rafiqul Gani is Professor in Systems Design at the Department of Chemical Engineering, Technical University of Denmark, co-editor of Computers and Chemical Engineering and Member of the Executive Board of the European Federation of Chemical Engineers.

Asst. Prof. Michael Georgiadis

Asst. Prof. Michael Georgiadis
Michael Georgiadis is Associate Professor in the Department of Engineering Informatics at University of Western Macedonia, Greece and Honorary Research Fellow at Imperial College London.

The judges made the following comments about the winning paper and runners-up:

Winning paper

Our summary of Dynamic modelling and simulation of a heated brine spray system" by Raquel Durana Moita, Henrique A. Matos, Cristina Fernandes, Clemente Pedro Nunes and Mário Jorge Pinho is as follows:

"Excellent work presenting a detailed integrated dynamic model of a heated brine spray system that is used in NaCl salt recrystallization ponds and integrated with a cogeneration unit, to increase the global process efficiency. The model has been implemented in gPROMS and validated using both literature and real-life industrial data. Model predictions are in good agreement with available data. A detailed sensitivity analysis of key model parameters was performed. The developed model provides a reliable basis for the simulation, design and optimisation of similar systems. In summary excellent modelling work followed by detailed validation studies ".

Runners-up

Online model-based redesign of experiments for parameter estimation in dynamic systems by Fabrizio Bezzo, Federico Galvanin and Massimiliano Barolo:

"Excellent model-based application work which presents a novel strategy for the online model-based redesign of experiments based on intermediate parameter estimations. The approach has been implemented in gPROMS illustrating significant reduction in the number of experimental trials that are needed to reach a statistically sound estimation of the model parameters."

Optimization of PSA process for producing enriched hydrogen from plasma reactor gas by Mladen Eic, Qinglin Huang and Amir Malekian:

"Very good work presenting a rigorous Pressure Swing Adsorption Model (PSA) model for high purity hydrogen production. Extensive simulation results in gPROMS were well matched with experimental results from a pilot-scale process. Optimisation studies were also performed using gPROMS's dynamic optimisation capabilities. In summary excellent modelling work followed by validation and optimisation studies."

Judgement criteria

Papers were judged against the following specific five criteria, taking into account the general guidelines published on the PSE website:

  • Innovation with respect to model-based concepts [25%]
  • Innovation with respect to construction and application of gPROMS models [25%]
  • Innovation with respect to integration, use and implementation of model-based activities [25%]
  • Scope and significance of the results obtained [15%]
  • Overall scientific interest and relevance [10%].

As by definition the papers have already been reviewed and accepted for publication they are deemed to have scientific merit, and were thus not judged explicitly on this criterion other than to award ‘tie-breaker’ points in the last category.

Implicitly taken into account were the modelling themes that PSE is promoting for advanced process modelling and model-based innovation in general:

  • high-fidelity modelling, going to chemical engineering first principles where possible
  • model validation using experimental data in order to integrate theoretical models to observed values
  • multipurpose process modelling – i.e. using the same model for a variety of model-based activities in order to enhance return on modelling investment.