System and method for controlling air conditioner
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Solution Overview
Problem
Conventional air conditioner control systems in vehicle painting process lines require excessive time to stabilize temperature and humidity, leading to energy loss due to operator-dependent initial operation and simultaneous operation of multiple control modules, resulting in prolonged wait times before vehicle arrival.
Innovation Solution
A system and method that utilize outdoor and indoor air measuring devices, an air conditioning controller, and a server to learn and predict stabilization times, adjusting the air conditioner's operation based on historical data to minimize initial operation time and energy loss by applying optimized control values for temperature and humidity stabilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the air conditioner is operated in advance to maintain temperature and humidity stabilization conditions, then the spray booth can achieve stable painting conditions, but the initial operation time becomes excessively long (40-80 minutes) causing energy loss
Solution Approach 1:
The system performs preliminary learning of the stabilization process during initial operations, storing the learned data in a database. This allows the controller to predict and prepare optimal control values in advance, reducing the actual stabilization time when the air conditioner is next operated without compromising the reliability of temperature and humidity stabilization.
Solution Approach 2:
The system continuously learns from historical operation data and feedback about actual stabilization times. The server accumulates learned data from multiple operations and uses this feedback to optimize control values, progressively improving the prediction accuracy and reducing energy loss while maintaining stable painting conditions.
2Reliability
If the air conditioner is operated with a margin before vehicle arrival to ensure stabilization conditions, then painting quality is maintained, but the wait time and energy consumption increase significantly
Solution Approach 1:
The system dynamically adjusts control values based on learned patterns from historical data. Instead of using fixed conservative margins, the controller adapts the operation timing and parameters according to actual environmental conditions and learned stabilization behaviors, reducing unnecessary wait time while ensuring painting conditions are met.
Solution Approach 2:
The system changes control parameters (burner operation, washer pump, reheater, steamer) based on learned optimal values from historical data. By adjusting these parameters dynamically according to outdoor air conditions and learned patterns, the system achieves stabilization faster without compromising painting quality.
3Productivity
If multiple control modules (burner, washer, reheater, steamer) are operated simultaneously at initial operation, then the air conditioner attempts to reach stabilization conditions faster, but the actual stabilization time becomes excessively long due to complex interactions
Solution Approach 1:
The system segments the control of multiple modules by learning their individual and combined effects from historical data. Instead of operating all modules simultaneously without coordination, the controller uses learned data to sequence and coordinate module operations, reducing conflicts and achieving faster stabilization.
Solution Approach 2:
The system applies partial action by selectively activating control modules based on learned optimal sequences. Rather than always operating all modules simultaneously, the controller uses historical data to determine which modules should be activated and when, avoiding unnecessary complex interactions that prolong stabilization time.
Data Source
AI summary
A system includes an air conditioner in each zone of a painting process line supplying heated air according to temperature/humidity stabilization conditions for a painting process of each zone of a spray booth; a controller transmitting operation information related to the air conditioner including temperatures and humidities of outdoor air flowing into the air conditioner and indoor air inside each zone of the booth and operating the air conditioner when a control value is received; and a server learning operation information history of the air conditioner collected through the air conditioning controller to accumulate the learned data in a database and extracting the control value for each of controllers of the air conditioner according to an initial operation condition of the air conditioner from the learned data in the database to control each of the controllers of the air conditioner for a predetermined time period based on the extracted data.