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You are here: Home1 / Simulation News2 / Simulation Cases3 / Simulation of an Automatic Batch Retort System (ABRS)

Simulation of an Automatic Batch Retort System (ABRS)

Below ABRS model is good example of how a simulation proves its worth in a production or material handling environment. Although the use of such virtual twin is often primarily seen as ‘concept design validation’, it goes way further than this. Besides equipment manufacturers, also producers (equipment user) have great benefit from having a functional virtual representation of their production line, so why not pass on the model from one to another and maximize the use.

But let’s zoom in on this specific case…

This Batch Retort System receives single lane cans from the upstream filling line, gathers them into baskets for thermal processing in retorts (sterilization) and afterwards discharges them on a single lane conveyor towards the downstream packaging line. Since this ABRS makes the product shelf-stable, it is of the utmost importance that it achieves the highest efficiency in order to be able to process the unsterilized product without unnecessary downtime.

The model we’ve designed shows a virtual copy of the real-life installation, meaning that all dimensions, kinematics and control logic are similar to the actual installation.

As mentioned, it has value for both the equipment manufacturer as equipment user to have such model, but let’s have a detailed look at what it really means:

For equipment manufacturers

Being the ultimate responsible for the well-functioning of the installation, the manufacturer must ensure that the system meets the desired customer performance when it comes to output, efficiency, sterilization times, formats handled, etc…

By making this simulation part of their design phase, bottlenecks are revealed at early stage and the effect of production variations can be determined. In addition, the 3D-visualisation provides excellent insight into the behaviour of the system and is useful for convincing stakeholders of a concept.

Benefits:

  • Determine the optimum amount of baskets and layer pads
  • Run ‘what if’ cases based on varying design parameters, like:
    • Kinematics (shuttle speed, loader acceleration, …)
    • Layout (single vs double shuttle, amount retorts, …)
    • Different retort process times
    • Automation logic
    • Amount of baskets and layer pads in system
    • Can supply speed
  • Demonstrate how the system responds to sudden stops, like:
    • Breakdowns
    • Starved material supplies (cans, layer pads)
    • Maintenance interventions
  • Dimension buffer size
  • Calculate output and efficiency

For producers

As a user of increasingly complex equipment, it is a continuous challenge for producers to build up and maintain sufficient knowledge of their installed base. On top of that, each new piece of equipment will have a certain influence on the up- and downstream line behaviour, which can lead to unexpected efficiency loss.
A simulation model helps to gain in-depth insight in the overall line behaviour in a very visual manner and allows for testing of process improvements without production interruption.

Benefits:

  • Identify bottlenecks and validate improvement actions
  • Simulate orders before start to optimize the production planning
  • Train employees on how to respond to unexpected situations
  • Estimate the impact of production changes, like:
    • New product formats
    • Amount of layers per basket
    • Retort process times
      etc…
  • Determine optimum amount of operators and their utilization rate
  • Test the system’s behaviour towards up- and downstream production fluctuation

Gain deeper insight into your own production system using simulation.

Contact us at info@talumis.com for more information.

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Link to: From a simulation model to a digital twin part II Link to: From a simulation model to a digital twin part II From a simulation model to a digital twin part IIWarehouseTotaal Link to: Simulation of a Food Production Factory Link to: Simulation of a Food Production Factory Simulation of a Food Production Factory
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