Advanced modelling and simulation of polymerisation processes
Polymer manufacture is a complex operation involving many different process variables.
This means that there can be significant benefit from using process modelling to understand the physics and chemistry, create better designs and optimise operation.
As new polymer production capacity comes on line in the Middle East and China, the commercial drivers for efficient design and operation are increasing daily.
Successful application of model-based innovation and process enhancement techniques can result in millions of dollars of per year in enhanced revenues with little or no capital expenditure.
PSE's state-of-the-art gPROMS process modelling package is increasingly the environment of choice for polymer manufacturers who wish to extract more value from their plants.
— Senior Polymer Technology Manager BASF Polymers
PSE also provides sophisticated reactor models, and polymer modelling experts who work collaboratively with your personnel under PSE's ModelCare and Model-Based Innovation programmes.
They will guide experiment programmes and project implementation, assist in design and the implementation of online optimisation, and in general ensure fit-for-purpose applications.
Benefits
The benefits of modelling can be significant. For a start, you can improve yield, throughput and product quality simply by using optimisation to determine the optimal trajectories of key operating variables, particularly in batch processes.
Models can then be deployed to optimise online operation, providing benefits every day.
Modelling helps you understand processes better, and make better use of laboratory and operational data.
This enables you to troubleshoot poor operation and increase process reliability in existing processes. It also enables you to introduce new technology - designed to maximise end user product qualities - with confidence and reduced risk.
Examples
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Example 1: EPS Batch polymerisation
By building a high-fidelity detailed kinetic model of its batch Expanded Polystyrene (EPS) process and applying dynamic optimisation techniques, BASF was able to identify a 30% reduction in batch time.
The first-principles gPROMS batch process model (left) included detailed reaction kinetics, with parameters estimated from experimental data.
In addition it modelled heat and material balances, geometry details, transport and thermodynamic properties (calculated using the PC-SAFT equation of state) and plant operating procedures.
Dynamic optimisation was used to minimise batch time.
The results (below) show that the criteria for terminating the batch were satisfied 30% sooner than prior to optimisation.
Example 2: Optimal grade change in a Polyethylene (PE) process
Polyethylene has rigorous quality specifications for different grades. One of the major challenges in polyethylene manufacture is to minimise the production of off-spec product during grade change.
Borealis' BORSTARTM process (left) uses slurry loop and gas-phase reactors for producing high-grade PE.
Borstar PE has a bi-model molecular weight distribution that gives it a wide range of high quality end-user properties.
During grade change, it is necessary to move from one set of melt index and density values to another, through an off-specification region.
By using dynamic optimisation, then implementing the optimal trajectories using an advanced Model-based Predictive Controller, it was possible to demonstrate a time saving of nearly 60 % on a grade change.
Assuming a changeover frequency of one week on average, this represents around one week's additional production per year.
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