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Model Based Control of Refrigeration Containers. Kresten Kjær Sørensen Lodam electronics a/s and Aalborg University. Outline Developmental, commercial and scientific objectives System overview Project status Issues that should be addressed Research. Developmental Objectives

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model based control of refrigeration containers

Model Based Control of Refrigeration Containers

Kresten Kjær Sørensen

Lodam electronics a/s and

Aalborg University

slide2
Outline
  • Developmental, commercial and scientific objectives
  • System overview
  • Project status
  • Issues that should be addressed
  • Research
slide3
Developmental Objectives

“A set of methods that enable optimal, adaptive, constrained control of a refrigeration container that is also applicable to other thermodynamical systems”

slide4
Commercial Objectives
  • Better competitiveness through:
    • Reduced energy consumption
    • Reduced mechanical wear and tear
    • Improved adaptiveness of the controller
slide5
Scientific Objectives
  • To use MPC for refrigeration by:
    • Finding a suitable method for modeling nonlinear dynamics of the refrigeration system that may be used by an MPC.
    • Finding a method to cope with changes in system dynamics in the event of failure.
slide6
System overview
  • Known refrigeration system dynamics
  • Unknown cargo dynamics
  • Known constraints on cargo temperature
  • Economizer gives an additional degree of freedom
slide8
Project Status
  • A library of nonlinear refrigeration component models has been developed.
  • A composite simulation model has been developed.
  • A framework that allows to test a controller, working within Matlab, with real hardware is ready.
slide9
Simulation Model 1/2
  • Nonlinear simulation model of the refrigeration system.
  • Composed of smaller component models from a library.
  • Simulation is done in Matlab by a set of custom functions.
slide10
Simulation Model 2/2
  • The simulation model is described by a number of files
  • Each component are simulated separately for a short time step
  • All outputs at the end of the simulation is propagated to the relevant inputs
  • Simulation run again for another time step
  • Equivalent of simulating a large, combined function with ZOH’s inserted between components
slide11
Issues that must be addressed
  • The system is nonlinear and has a large area of operation for both hot and cold temperatures.
  • There are discrete actuators with strong influence on the dynamics that cannot be neglected.
  • In case of partial system malfunction it is very important to continue cooling.
slide12
Research
  • Adaptive MPC for increased robustness in the event of failure.
  • Auto-generation of a model usable with MPC, based on the simulation model.