Food SimulationsFood process simulations
Before building or modifying a production line, and before running dozens of trials, a process can be understood in a model. Foodlab develops physics-based and numerical simulations that explain and predict selected food processes, and uses them in our daily development work and in Foodlab Academy courses.
heat transfermass transferArrheniusTgresidence time
What can be simulated
- Heating and cooling, pasteurization and sterilization: temperature profile at the cold point and P and F values.
- Drying and moisture transfer: freeze, spray and air drying, and moisture uptake in storage.
- Freezing and thawing.
- Lipid oxidation and shelf-life behaviour.
- Fermentation and acidification kinetics (including the yogurt simulator we built for the Academy).
- Mixing and residence-time effects.
- Scale-up sensitivity: what happens when geometry, heat transfer, shear or residence time change.
A simulation helps identify sensitive parameters, compare process scenarios and design more informative experiments.
Simulation and scale-up
Simulation helps identify how changes in equipment size, geometry, heat transfer, mixing, residence time and operating conditions may affect a product during scale-up. Together with laboratory measurements and pilot trials, we use it to identify critical variables, evaluate likely process limits, compare alternative operating conditions, plan measurements for a production trial, explain why a laboratory process behaves differently on industrial equipment, and reduce the number of poorly targeted trials.
A model should be calibrated and tested against relevant experimental or production data before it is used for quantitative decision-making.
Shelf-life and oxidation models
Shelf-life and oxidation models are used to compare scenarios, identify sensitive variables and improve the design of storage studies. Depending on the problem, a model may consider product composition, lipid composition, water activity, temperature, initial oxygen concentration, package oxygen transmission, headspace volume, package dimensions, light exposure, reaction-rate data and a defined sensory or analytical failure limit.
Reliable quantitative predictions normally require product-specific kinetic data and confirmation against storage results. We use modelling as a decision-support and experimental-design tool, not as an automatic substitute for real-time product evidence.
What simulation does not do
Simulation does not remove the need for laboratory and production trials. Its value is in improving understanding, narrowing the experimental range and reducing avoidable trial-and-error work. AI and models solve equations and calculate pasteurization data very well; they have never tasted the result. That part is done by people, in the lab, next to the equipment.
Frequently asked questions
Can I simulate a food process before building or modifying a production line?
Yes. We develop physics-based and numerical simulations that help explain and predict selected food processes before committing to extensive production trials or equipment investment. A simulation identifies sensitive parameters, compares scenarios and designs more informative experiments.
Can shelf life or oxidation be predicted by modelling?
Models can be used to compare scenarios and identify sensitive variables. Reliable quantitative prediction requires product-specific kinetic data and confirmation against storage results. The model is a decision-support and experiment-design tool, not a replacement for real-time testing.
A process that is hard to observe? Let us model it.
Tell us about the process, the equipment and the question you are trying to answer.
Talk to usQuestions about cost, timelines or who owns the formula? Read the FAQ