The automation control of the ice pipe machine achieves closed-loop management through sensor monitoring, PLC/intelligent controller decision-making, and actuator action. Combined with IoT technology, it can be further upgraded to a remote monitoring and predictive maintenance system. The following is the specific implementation method:
1、 Core control logic: sensor controller actuator closed-loop
Sensor monitoring
Temperature sensors: distributed in the stirring mechanism, condenser, ice bucket and other locations, real-time monitoring of temperature in key areas. For example:
When the temperature of the ice stirring mechanism is too low, abnormal motor current is detected (due to increased torque caused by blocked water flow), triggering the ice flushing program and directly introducing compressor refrigerant to accelerate the melting of the ice layer.
When the condenser temperature is too high, the analog signal is converted into a digital signal through A/D conversion, and the program determines and controls the fan motor speed or compressor working status.
The temperature sensor of the ice bucket detects the height of the ice cubes. When the temperature is lower than the set value (such as 7 ℃), the program determines to stop making ice.
Infrared photoelectric sensor: installed inside the storage refrigerator, detects the ice full status by emitting and receiving infrared radiation. When ice blocks the light, it triggers a shutdown. After the ice melts, the light returns and the machine restarts to avoid ice overflow.
Micro switch/flip plate sensor: A movable baffle is set below the ice outlet, and the micro switch is triggered when ice blocks accumulate and press against the baffle, stopping ice making; After removing the ice, the baffle falls back and the machine restarts.
Controller decision
PLC (Programmable Logic Controller): Large and medium-sized pipe ice machines use PLC as the core controller, achieve human-machine interaction through touch screen, display operating parameters, fault records, and support functions such as one click start stop, energy adjustment, remote monitoring, etc. For example:
Automatically adjust the compressor frequency based on temperature sensor data to achieve energy-saving operation.
Control the de icing cycle through time relays or ice thickness sensors to ensure uniform ice thickness.
Industrial control computer: In large-scale cold chain systems, the industrial control computer serves as the upper computer and is connected to the PLC to achieve group control management. It centrally monitors data from multiple units and displays it in the form of tables, curves, etc. for easy maintenance.
Execution mechanism action
Electromagnetic valve: Control the flow of refrigerant according to controller instructions, such as directly introducing compressor refrigerant to the ice stirring mechanism during ice flushing.
Compressor/fan motor: Adjust the speed based on temperature sensor feedback to maintain stable evaporator and condenser temperatures.
Ice cutter/water pump: When de icing, the ice cutter rotates to cut the ice tube, and the water pump circulates water to achieve continuous ice making.
2、 IoT technology upgrade: remote monitoring and predictive maintenance
Data collection and transmission
Real time collection of device operating status (such as temperature, pressure, current) and environmental parameters (such as humidity, water quality) through sensors, and uploading to cloud servers.
Cloud analysis and optimization
Based on big data and artificial intelligence technology, model and analyze historical data, identify poor operating modes (such as abnormal energy consumption and frequent start stop), and optimize control strategies. For example:
Adjust the outlet temperature of the chiller based on the cooling load prediction to reduce energy consumption.
Release energy-saving space through variable frequency regulation of the refrigeration pump.
Remote monitoring and alarm
Users can check the device status, receive abnormal alarms (such as temperature exceeding limit, ice full shutdown), and obtain fault diagnosis suggestions through computers or mobile terminals.
Manufacturers can remotely monitor equipment operation data, predict faults in advance, and reduce downtime.
3、 Adaptive Control Technology: Coping with Complex Working Conditions
Self correcting Control System (STC)
In response to the time-varying and nonlinear characteristics of refrigeration system parameters, the system is optimized for optimal performance through online parameter estimation and controller parameter adjustment. For example:
Automatically adjust PID parameters during initial operation or changes in operating conditions to avoid time-consuming manual debugging.
Dynamically adjust the fan speed according to the temperature changes of the condenser to improve energy efficiency.
Model Reference Adaptive Control (MRAS)
By setting expected performance indicators based on the reference model, comparing the error between the actual output and the model output, and adjusting the controller parameters to make the system approach the reference model. For example:
During the de icing process, the speed of the ice cutter is controlled by model reference to ensure uniform ice size.
