Refrigeration Controller with Adaptive Baseline Performance Tracking
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Solution Overview
Problem
Refrigeration systems consume significant energy, and users lack expertise to accurately analyze performance and energy consumption data, leading to inefficiencies and high operational costs.
Innovation Solution
A system controller monitors and controls a refrigeration or HVAC system, comprising a compressor rack and condensing unit, to track power consumption, determine predicted and benchmark consumption, and generate alerts based on comparisons, while also optimizing operations to minimize energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If refrigeration systems operate continuously to maintain required temperature, then cooling function is ensured, but energy consumption increases significantly
Solution Approach 1:
The system dynamically adjusts compressor operation based on real-time monitoring of temperature, humidity, and door opening events. The controller modulates compressor runtime and intensity to match actual cooling demands, transitioning from static continuous operation to dynamic adaptive operation that maintains reliability while reducing energy waste during low-demand periods
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor temperature and humidity conditions, the controller processes this data against target parameters, and adjusts compressor operation accordingly. This closed-loop control ensures temperature maintenance reliability while optimizing energy consumption by responding only when deviations occur
2Productivity
If system operates at high capacity to meet peak demand, then cooling performance is sufficient, but energy consumption increases during low demand periods
Solution Approach 1:
The system dynamically scales cooling capacity based on real-time environmental conditions and demand signals. During low-demand periods, the controller reduces compressor capacity or extends run cycles, while automatically increasing capacity when temperature deviations or high-demand conditions occur, matching productivity to actual needs rather than operating at fixed high capacity
Solution Approach 2:
The system applies partial action by operating compressors at reduced capacity or extended intervals during low-demand periods, providing just enough cooling to maintain temperature within acceptable ranges. This avoids excessive cooling that would waste energy, while still ensuring sufficient capacity is available when needed
3Productivity
If multiple compressors operate simultaneously to meet high cooling demand, then cooling capacity is sufficient, but peak power demand exceeds supply constraints
Solution Approach 1:
The system performs preliminary action by pre-cooling spaces or pre-charging thermal storage before peak demand periods or anticipated high-demand events. This allows the system to meet future cooling demands with reduced immediate power draw, staggering compressor startup and avoiding simultaneous operation that would create peak power exceeds supply constraints
Solution Approach 2:
The system segments the cooling load across multiple compressors operating in staggered sequences rather than simultaneously. The controller activates compressors individually or in groups based on demand levels, ensuring that total power draw remains within supply constraints while collectively providing sufficient cooling capacity through coordinated partial contributions
Data Source
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AI summary
A system comprising: a controller for a refrigeration or HVAC system having a compressor rack with at least one compressor, wherein the controller is configured to track performance of a compressor in the compressor rack, wherein the system is configured to: in response to rated performance data for the compressor being unavailable, generate baseline data for the compressor and to assess the performance of the compressor by comparing operational data of the compressor to the baseline data for the compressor; in response to the rated performance data for the compressor being available, assess the performance of the compressor by comparing the operational data of the compressor to the rated performance data for the compressor; generate the baseline data for the compressor based on data received from the compressor immediately following installation of compressor; assess the performance of the compressor by comparing the baseline data to the operational data of the compressor obtained subsequent to developing the baseline data; perform a regression analysis on the rated performance data and the data obtained from the compressor during operation; generate a benchmark polynomial and a benchmark hull; and analyze data obtained from the compressor during operation using the benchmark polynomial and the benchmark hull and to assess the performance of the compressor based on the analysis.