Model-Based Innovation

Accelerated, effective R&D through modelling

Model-Based Innovation (MBI) involves combining high-fidelity models of processes or products with modern R&D methodologies to provide high-quality information for innovation decision support.

This helps companies to manage risk in innovation, design and operational enhancement based on accurate quantitative information.

The result is faster innovation, improved designs of processes and products, enhancement of existing operations and more effective R&D programmes.

PSE's ModelCare® Model-Based Innovation service covers all process industry sectors, and can be applied from laboratory R&D, through process and detailed design, to online operation.

In fact, MBI is a key mechanism for integrating R&D, design and operational activities, to achieve mutually beneficial objectives.

The benefits of Model-Based Innovation

The application of MBI can result in significant competitive advantage through modelling. Some specific benefits are:

  • effective risk management. Modelling provides quantitative data on which to base R&D and design decisions, allowing you to manage risk with confidence.
  • fast screening of alternatives. Modelling quickly shows you which alternatives to discard and which to pursue.
  • streamlining of experimental programmes. Model-based experiment design can reduce time and cost of experimentation significantly.
  • direction of Research & Development spending. The information generated by MBI techniques can be used to rank R&D investment alternatives by value.
  • understanding the relationship between experimental uncertainty and design margin.
  • availability of accurate validated models for design, leading to effective and accurate process or product design right from the research stage.

PSE's ModelCare

PSE's ModelCare service is designed to help companies innovate rapidly with relatively low investment and fast payback.

A key aim of ModelCare is to transfer MBI know-how to customers to help them build their own internal Model-Based Innovation capability.

Model-Based Innovation methodology: the fundamentals

PSE's Model-Based Innovation methodology is underpinned by major advances in modelling technology and thinking:

1. Modelling technology has come of age

Advanced Process Modelling tools and methodologies now allow modelling of many types of complex process, products or equipment - from detailed reactor systems to tablets delivering drugs in the human body - to a degree of predictive accuracy which is capable of supporting real innovation in design and operation.

Hybrid "APM-CFD" methodologies make it possible to investigate the detailed effects of equipment geometry on the behaviour of complex processes, enabling accurate scale up.

Beyond simple trial-and-error simulations, models can now be used directly for rigorous optimisation of design and operating conditions involving both continuous and discrete decisions (e.g. the structure of the plant flowsheet).

2: Modelling and experimentation can be closely coupled

As shown below for the simultaneous design of a new catalyst and reactor, model-supported micro-experimentation[1] can use small catalyst samples[2] together with models of the experimental apparatus[3] to generate accurate values of model parameters[4].

Model-based experiment design[1] minimises the number and cost of such experiments by ensuring that each one generates the maximum amount of information.

Once the parameters are known, then they can be used in models of full-scale equipment[5] coupled with rigorous optimisation[6] techniques to determine the optimal reactor design[7] and operation[8] that are achievable using this particular catalyst.

This provides a screening mechanism, reserving pilot plant testing[9] only for catalysts which appear to be promising.

MBI for catalyst innovation

3: Modelling can establish a quantitative link between uncertainty and risk

Even formally validated models are still subject to uncertainty in their parameter values.

It is important to be able to map this uncertainty to uncertainty in Key Performance Indicators (KPIs) of the process, to allow a trade-off between research and risk.

All parameters are uncertain, but not all of them are critical.

Parameter probability distribution linked to controllability and profitability

Some parameters may have a small effect on KPIs; uncertainty in other parameters may be counteracted by control actions.

Models can help establish the quantitative relationship between fundamental R&D uncertainty in process development and technological risk during process operation.

By establishing which parameters affect KPIs most, research investment can be directed towards the most critical parameters.

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