Cooling Control Variable Optimization for Lower Chiller Power Use
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Solution Overview
Problem
Cooling systems consume excessive energy due to sub-optimal control variable settings based on manufacturer suggestions and personal experience, lacking consideration for historical data and the impact on air handling units, leading to inefficient power consumption.
Innovation Solution
A cooling control system that monitors various components using sensors to record historical data, calculates optimal setpoints for control variables, and explores new settings to minimize power consumption, incorporating a control module that adjusts and optimizes settings based on empirical data and sensor readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If control variables are set based on manufacturer suggestions and personal experience, then the system is easy to operate, but power consumption is excessive and energy efficiency is poor
Solution Approach 1:
The cooling control system automatically monitors sensor data, analyzes historical operating patterns, and self-adjusts control variable setpoints without requiring manual administrator input. The system serves itself by continuously optimizing its own operation based on empirical data collected from the cooling system components and environmental sensors.
Solution Approach 2:
The system implements continuous feedback loops where sensor readings from cooling system components are fed back to the control module, which analyzes the data and adjusts control variables accordingly. This closed-loop control enables the system to respond dynamically to changing conditions and optimize power consumption in real-time.
2Productivity
If optimization is based on equipment manufacturers' performance curves, then the approach is simple to implement, but the result is sub-optimal and does not minimize power consumption efficiently
Solution Approach 1:
The system transitions from static manufacturer performance curves to dynamic, real-time optimization based on actual operating conditions. Control variables are continuously adjusted based on current sensor readings, historical data patterns, and environmental conditions, enabling the system to adapt dynamically rather than relying on fixed manufacturer recommendations.
Solution Approach 2:
The patent replaces the mechanical engineering approach based on manufacturer-provided component models with a data-driven computational approach. Instead of combining manufacturer specifications into prediction models, the system uses machine learning algorithms that analyze actual sensor data from the cooling system to derive optimization strategies.
3Loss of information
If manual control variable settings are used based on administrator experience, then the system requires minimal automation, but it misses the opportunity to consider historical data and system impact
Solution Approach 1:
The system performs preliminary analysis of historical operating data to identify patterns and optimize control variable setpoints before actual operation. By pre-processing and learning from historical data, the system prepares optimized control strategies in advance, enabling it to anticipate optimal settings rather than reacting to current conditions alone.
Solution Approach 2:
The control module acts as an intermediary between raw sensor data and control variable adjustments. It collects and processes data from multiple sensors and cooling system components, analyzes historical patterns, and translates this information into optimized control commands, serving as a bridge between data collection and system control.
Data Source
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AI summary
The present invention is directed to an apparatus for minimizing power consumption in a cooling system. In one embodiment, the apparatus comprises one or more processors, one or more sensors associated with one or more regulated environments and one or more chillers that regulate temperature of the one or more regulated environments and a storage device, coupled to the one or more processors, storing instructions that when executed by the one or more processors performs a method. The method comprises gathering readings from the one or more sensors, determining a cost and power consumption associated with setting values for a plurality of control variables associated with the one or more chiller plants, selecting values for the control variables with a minimum cost as optimized control variable values and applying the optimized control variable values to the plurality of control variables to minimize power consumption of the cooling system.