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Industry Case Studies

Medical devices


Problem: Hydration process is the rate limiting step in the current production line of contact lenses manufacturing
MACSI Contribution:

  • Modelling of contact lens manufacturing process called hydration
  • Modelling of equipment to fit in new production rig

Outcome: Increased efficiency and improved design of production lines

Boston ScientificBoston Scientific

Problem: Hole formation during manufacturing renders stents unusable

MACSI Contribution:

  • Developed two models to account for the formation of holes

Outcome: Both models suggested that an increase in curing temperature will accelerate the monomer polymerization, while only slightly accelerating solvent evaporation, thereby reducing the likelihood of hole formation.


X company
Problem: Variability in dose delivered by new design of dry-powder inhaler

MACSI Contribution:

  • Mathematical model of new design of dry-powder inhaler
  • Understanding of particle-wall interactions within device

Minimising variability in drug delivery

Outcome: Improved understanding of process, now in compliance with device specifications

Multiple companiesMultiple Companies

Ongoing Problem: Uncertainty in the end point of primary drying in a freeze-drying process

MACSI and PMTC Contribution:

  • Development of a mathematical model for the precise determination of the end point of primary drying use data from several pharmaceutical companies.

Expected Outcome: Quantitative criterion for the end of primary drying based on non-invasive measurements made in the freeze drier. Optimization of the lyophilisation process

Semi-conductor IndustryAnalog Devices

Analog Devices

Problem: Reduce the need for costly experiments on polysilicon fuse components

MACSI contribution:

  • Developed a multiphysics compartment model describing electronic, thermodynamic and fluid mechanical phenomena.

Outcome: The model generated quantitatively accurate results, therebyreducing the amount of experiments required

Alumina Refinery

Rusal AughinishRusal

The Aughinish facility is Europe’s largest alumina refinery and the also the largest in the Rusal organisation. It is one of the most efficient alumina facilities globally.

Problem: Prediction of product quality 5 days in advance

MACSI contribution:

  • Developed mathematical and statistical models for process understanding using first principles simulation and data-driven stochastic dynamics.Aughinish Plant

Outcome: 200% increase in the accuracy of prediction of product quality. Model now employed on a daily basis.

Problem: Optimisation of bulk carrier loading and discharge at the Aughinish jetty

MACSI contribution:Aughinish Shipping

  • Developed a queuing theory model of the Rusal  Aughinish shipping process, with input from an analysis of historical data
  • Implemented the model in a stochastic simulation tool
  • Analysed the results to determine optimal ship sizes, berth occupancy, and loading rates 

Outcome: Specific recommendations for the optimal ship size and loading rates were delivered, that will yield improved berth through-put and avoid demurrage costs.  What-if  analysis of scenarios was also conducted to inform  the planning of possible future capital investment

Inkjet printing

Problem: The need to optimize the design of innovative inkjet  printing technologyInkjet Printing

MACSI contribution:

  • Developed a reduced fluid-structure interaction model, and implemented in Ansys simulation package
  • Using the simulation tool an optimization algorithm for finding design parameters was developed 

Outcome: An efficient algorithm for accurately targeting the region of design space for innovative new products, at a fraction of the computational cost of previous approaches.

Service Parts & Logistics


Problem: Forecasting of demand for service parts to support the warranty base of products in Europe, Middle East and Africa

MACSI contribution:

  • Analysed the datasets provided by Dell.
  • Assessed the accuracy of in-house forecasting methods.
  • Developed a Poisson process model of demand that improves the accuracy of forecasts.

Outcome: Improved method for forecasting demand for  service parts saving Dell over a million dollars per anumn

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