Crystallization

Benefits of simulation and modelling

Enhanced crystal size distribution (CSD) or PSD

A key benefit: better crystal size distribution (CSD) and larger median size

How are these benefits achieved?

All of these are achieved by using the model to:

determine optimal process designs – choose the appropriate process, the right vessel size(s), the best impeller size and shape, etc.
scale up correctly from lab conditions, taking into account the hydro-dynamic effects on crystal growth, agglomeration and attrition
reduce batch times, using dynamic optimisation to determine optimal recipes
determine optimal process settings such as temperatures and pressures
determine optimal seeding location and policy
determine optimal batch recipes to achieve desired crystal characteristics

Modelling answers questions such as:

"How do I reduce batch time while satisfying operational and product quality constraints?"

"What is the optimal seeding strategy?"

"What is the optimal cooling or heating strategy?"

"What is the optimal mixing impeller frequency for uniformity of process conditions and crystal attrition?"

Advanced Process Modelling allows decision support in the design and operation of crystallisation processes to be based on quantitative information obtained by combining fundamental physics and experimental data from laboratory and/or production scale.

APM not only provides significant benefits in all aspects of production, leading to higher revenues, lower capital and operating costs and increased customer satisfaction. It also allows much more effective use of data from (in many cases existing) experimental programmes.

Examples of typical benefits are given below; how they can be achieved is described at the bottom of the page.

The benefits of crystallisation modelling

There are many benefits of applying modelling, in both design and operation. The financial benefits differ depending on the type of product, and scale of operation.

Satisfy customer product quality specifications. By applying rigorous models, it is possible to achieve better crystal sizes and better and more consistent crystal size distributions (CSD).
Benefit: higher prices for products; better downstream processing leading to lower production costs and lost revenue due to downtime; consistency leading to higher customer satisfaction.
Reduction in batch-to-batch variability. Better process and recipe design lead to more consistent operation. Model-based Advanced Process Control (APC) can also be applied to enhance consistency.
Benefit: better overall quality product, resulting in higher prices; reduction in lost revenue from spoilt batches; improved customer satisfaction.
Improved downstream solids handling. By improving the quality of crystalliser product, downstream processing problems – for example, filter clogging – can be minimised.

Benefit: reduced downtime leading to lower production costs and reduction in lost revenue; improved customer satisfaction.

Greater throughput. By optimizing the design and operation of crystallizers, it is possible – particularly by minimizing batch recipe time – to achieve significantly enhanced throughput.

Benefit: greater revenue from higher production; better ROI for capital investment; higher customer satisfaction.

One case where gPROMS was used resulted in an increase in revenue from a large-scale multi-stage unit of millions of dollars per year.

Optimised recipes. By using optimization techniques, it is possible to determine the minimum batch time subject to quality and other constraints.

Benefit: greater revenue from higher production; better return on capital invested; generally better product quality.

Faster time-to-market. By using model-based techniques it is possible to scale-up reliably and start commercial production in the minimum time.

Benefit: earlier revenue stream; longer production for patented products; more reliable start-up and initial operation resulting in improved economics.

Accelerated innovation. High-accuracy predictive modelling is an invaluable tool when designing new processes and equipment, or even when making simple process improvements.

Benefit: potential to increase competitive advantage leading to lower-cost or higher-value production.

Greater understanding and integration of R&D and engineering functions

There are also some 'soft benefits' to modelling of crystallisation processes that should not be underestimated:

Greater understanding of the process and its operation. Modelling significantly increases process understanding, allowing better-informed decisions to be taken at all levels of design and operation.
Greater knowledge. The model-based data analysis and model validation exercises required to build a validated crystalliser model lead to increased corporate knowledge about the process.
Integration of R&D and Engineering Design. The collaboration required for this modelling and validation effort, and the design of related experimental programmes, leads to a close integration of experimental work with the modelling effort for design optimisation.
This means that experimental work is targeted at design, and design requirements can be used to guide experimental work

This relationship can be formalised through the use of model-based experiment design techniques to determine the optimal set of experiments.
Better use of experimental data. For the reasons outlined above, experimental data can be leveraged and enhanced through its application in modelling
Confidence to innovate. The existence of a tool that provides accurate predictive information is a significant confidence booster when innovating.