Integrating models and data to accelerate engineering
A Model-based Engineering (MBE) approach applies advanced process models in combination with observed (laboratory, pilot or plant) data to the engineering process.
The objective is to enable exploration of the process decision space as fully and effectively as possible, and support design and operating decisions with accurate information.
Typical application is in new process development, including scale-up, or optimization of existing plants.
What does model-based engineering involve?
MBE typically involves high-fidelity, first principles process models validated against data in the “model validation cycle”.
MBE places high-fidelity predictive models at the heart of process design or operational analysis.
Initial project effort is put into constructing a high-fidelity model of the plant or process that is predictive over the entire range of interest.
This model is then used to optimize design or operation, exploring a wide design space rapidly and at low cost, and applying optimization techniques to determine answers directly rather than by trial and error simulation.
MBE is based on three core approaches:
- First-principles modelling, where all relevant phenomena are described to an appropriate level of chemical engineering first principles representation. This typically involves detailed mass transfer, heat transfer and reaction equations.
- Multiscale modelling, where phenomena at all relevant scales are taken into account. The diagram on the right shows, for example, the scales that need to be taken into account for a multitubular reactor. The phenomena occurring at a microscale in a catalyst pore can have a significant influence on the overall (macroscale) reactor design.
- Integration with experimental data, by applying a model-targeted experimentation approach to refine the model and at the same time maximize the effectiveness of the experimental programme.
The guiding principle of model-targeted experimentation is “use experimentation to improve the accuracy of the model (rather than the process itself), then use the model to optimize the process design or operation”.
Why apply Model-based Engineering?
Key objectives of MBE are to:
- Accelerate process or product innovation, by providing fast-track methods to explore the design space while reducing the need for physical testing.
- Minimise (or effectively manage) technology risk by allowing full analysis of design and operational alternatives, and identifying and addressing areas of poor data accuracy.
- Integrate R&D experimentation and engineering design in order to maximize effectiveness of both activities and save cost and time.
- Reduce operating costs or improve throughput and product quality through high-accuracy analysis and model-based optimization techniques.
- Reduce the amount of experimentation, through better-targeted and better designed experiments
- Reduce (but not eliminate) the requirement for pilot plant testing. Many design options can be explored and eliminated using predictive modelling before the best candidates are chosen for pilot plant testing.
How does MBE work in practice?
MBE applies a family of methodologies centred on high-fidelity predictive models in a structured way.
- First-principles models and experimental data are combined to create a high-fidelity predictive representation of the key phenomena occurring in any process, often (particularly in the case of reactor design) through model-targeted experimentation.
- These sub-models of the phenomena are then used to build high-fidelity models of the full-scale process.
- The model is used in steady-state and dynamic simulation and optimization studies to achieve the project objectives.
If necessary it is possible to deploy a variety of well-established techniques – for example, combining the physics and chemistry models derived above with Computational Fluid Dynamics (CFD) hydrodynamic information, or using population balance models – to take into account mixing and other effects introduced at larger scales.
Examples of application
For comprehensive examples of application, see the following articles:
- LG Chem article Optimize terephthaldehyde operations [Hydrocarbon Processing, April 2007], describing the model-based design of a multitubular reactor
- Süd-Chemie article Enhanced methods optimize ownership costs for catalysts [Hydrocarbon Processing, June 2007], describing the application of MBE techniques to catalyst deactivation analysis for methanol synthesis and optimization of catalyst loading
- Repsol article Improve engineering via whole-plant design optimization [Hydrocarbon Processing, December 2010], describing the simultaneous optimization of reactor and separation section that improved plant economics by tens of millions of Euros.
Model-Based Engineering: key components
PSE’s MBE approach is founded on:
- The gPROMS ModelBuilder high-fidelity predictive modelling platform. ModelBuilder provides all the facilities needed to perform MBE within a powerful modelling and solution engine capable of generating the high-accuracy predictive information on which key design and operating decisions are based.
- A set of model-targeted experimentation techniques and methodologies proven over many years of application to industrial problems: parameter estimation, model-based data analysis, model-based experiment design.
- The state-of-the-art gPROMS advanced process models for catalytic reaction, complex separation, crystallisation and polymerisation.
- PSE’s hybrid modelling technologies which combine modelling in gPROMS with computational fluid dynamics (CFD) tools for ultimate scale-up accuracy.
- PSE’s extensive Consulting expertise.
In this sectionAPM overview How does it work? Digitalization Digital process design Digital process operations Equation-oriented approach What are the benefits? Application examples
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