Control of a refrigeration circuit
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Solution Overview
Problem
Current refrigeration circuit control methods focus on thermodynamic parameters but fail to optimize efficiency effectively, leading to suboptimal performance and energy consumption.
Innovation Solution
A controller that monitors prevailing conditions and uses an iterative optimization procedure to determine a limit setting for control variables, such as compressor speed, to target a performance threshold, optimizing operating efficiency by adjusting settings within defined ranges based on simulated operating points and performance parameters like heat transfer capacity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional control methods targeting thermodynamic parameters are used, then temperature control is achieved, but operating efficiency is suboptimal and energy consumption is high
Solution Approach 1:
The patent changes the control parameters from conventional thermodynamic parameters (superheat, temperature) to efficiency-based parameters (COP, power consumption). The controller dynamically adjusts control variables to maintain operation within an optimal efficiency range, transforming the control objective from temperature maintenance to efficiency optimization while maintaining required temperature control.
Solution Approach 2:
The patent replaces conventional mechanical control methods with a simulation-based control system. A simulation model predicts system performance and guides control decisions, substituting traditional empirical or fixed-rule control mechanisms with a sophisticated computational approach that continuously optimizes efficiency based on simulated operating points.
2Productivity
If control variables are adjusted to target thermodynamic parameters, then temperature control performance is maintained, but efficiency optimization is not achieved
Solution Approach 1:
The patent introduces a simulation model as an intermediary between the physical system and the controller. This simulation model acts as a virtual twin that predicts system behavior without requiring complex real-time measurements or control algorithms. The controller uses this intermediary model to determine optimal control settings, simplifying the actual control implementation while achieving efficiency optimization.
Solution Approach 2:
The patent performs preliminary simulation calculations to determine optimal control settings before actual system operation. By pre-calculating optimal operating points through simulation based on predicted conditions, the system avoids the need for complex real-time optimization algorithms, reducing control system complexity while maintaining efficiency optimization capability.
3Loss of energy
If a simulation model is used to determine optimal control settings, then efficiency is optimized, but computational requirements and control complexity increase
Solution Approach 1:
The patent applies partial optimization by focusing control efforts on the most influential parameters and operating ranges. Rather than optimizing all system parameters simultaneously, the simulation model concentrates computational resources on key control variables that have the greatest impact on efficiency, achieving energy optimization without requiring full-system complex control.
Data Source
AI summary
There is disclosed a controller for a refrigeration circuit, configured to monitor a set of prevailing conditions relating to the refrigeration circuit including a space temperature of a temperature-controlled space associated with the refrigeration circuit. The controller has a simulation module configured to determine a limit setting of a control variable for the refrigeration circuit by an iterative optimisation procedure based on a model corresponding to the refrigeration circuit. The objective function for the optimisation relates to an operating efficiency. The controller further comprises a dynamic control module configured to: adjust an operating setting of the control variable within an operating range to target a performance threshold for a monitored performance parameter, based on monitoring of the performance parameter during operation of the refrigeration circuit; and apply the limit setting received from the simulation module as a limit to the operating range.