Typical applications in brief

Chemical Process Industries

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

This allows companies to innovate and manage the associated risk 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.

Model-Based Innovation is systematically used for:

Design of new reactors
Design of new catalysts
Scale-up of complex operations such as crystallisation
Development of new technologies such as fuel cells
Reducing pilot plant testing
Reducing R&D costs and time

Why Model-Based Innovation rather than just "modelling"?

Set-ups for targeted experiments Some applications of MBI - such as the HiDiC example below - involve simply using modelling to reduce innovation risk.

However, full Model-Based Innovation uses special techniques such as model-based experiment experimentation to ensure that process design and R&D activity are formally related through a modelling framework.

This brings benefits in both directions, and results in accelerated innovation and more cost-effective – and time-effective – R&D.

Single and multi-pellet model-targeted
experiments to determine accurate reaction kinetics

Example #1 - Design of a novel acrylic acid reactor

PSE worked closely with Korea's largest chemical manufacturer using Model-Based Innovation techniques to design a novel reactor/catalyst configuration for a petrochemical process.

Multitubular reactor First, PSE provided a high-fidelity reactor model constructed using the gPROMS^® Advanced Model Library for Fixed-Bed Catalytic Reactors (AML:FBCR). This was linked to the design team's existing Fluent CFD model of the shell side geometry.

Then PSE Consultants directed the "model-targeted" experimental program within the company's labs to determine high-accuracy values for reaction kinetics and key process parameters such as bed conductivity. This resulted in a model with an extremely high degree of predictive accuracy.

The model was then used to develop catalyst profiles that would give uniform temperatures and concentrations across the reactor, resulting in significantly improved performance and overall stability.

As a consequence the company was able to finalise the detailed design itself at a considerable cost saving. Equally important, the company has now become one of the world's four companies with their own technology for this process.

Example #2 - Reliable, confident scale-up of crystallisation processes

Crystallisation processes are notoriously difficult to scale up. For instance, an approach that produces coarse crystals on a small scale in the lab may produce fine crystals in industrial-scale equipment, leading to poor filterability and hence a reduced production capacity.

How do you design a new crystalliser that can be guaranteed to work?

The answer - as with design of any equipment - is to take into account all relevant phenomena in the design. In the case of crystallisation this involves many experimentally-determined parameters (e.g. nucleation and growth rates) coupled with solution of population balance Model-Based Innovation cycle relationships.

Crystallisation is one of the areas that benefits most from Model-Based Innovation techniques. For example, models of lab-scale equipment can be used with Model-Based Experiment Design techniques to design of the optimal set of experiments.

This maximises the information content of experimental results while while minimising time and cost of experimentation, leading to optimal product performance and process behaviour.

PSE regularly works with companies to accelerate their innovation in crystallisation process design and operation, from industrial scale processes for compounds such as terepththalic acid, bis-phenol A or sucrose to much smaller-scale pharmaceutical operations.

By coupling the R&D and design processes, R&D time can be minimised and design made much more effective

These techniques have been successfully applied in companies such as Mitsubishi Chemical, BP Chemicals, PURAC, Danisco and SQM Nitratos.

Example #3 - UP TO 60% energy saving with novel distillation techniques

Heat-Integrated Distillation Columns (HIDiCs) can save up to 60% of the energy used for distillation, itself some 40% of the total energy used in the process industries.

However HIDiCs are yet not widely implemented. Why not? The reason is that, because of the HIDiC concentric tube column

difficulty in verifying that a design will start up and operate correctly, there is a high perceived degree of risk associated with their implementation.

Modelling provides an ideal way to minimise the risk of implementation. It enables companies to verify designs and establish safe and viable operating envelopes, to optimise safe and effective start-up procedures, to design control schemes that maximise operational flexibility, and many more.

It also helps to accelerate implementation, and, once the column is operational, provides a means to troubleshoot any operational problems.

PSE's work with a leading Japanese government research organisation has demonstrated the power of the model-based approach to HIDiC implementation.

HIDiCs are not yet widely implemented in the process industries. Why not?