Absorption Refrigerator Generator Temperature Control

Overview of Technical Issues:

The temperature control mechanism insufficiently regulates heat input to the generator solution, causing temperature deviations from the optimal operating range; when too low, refrigerant vaporization becomes inadequate reducing cooling capacity, and when too high, the absorbent solution crystallizes or degrades causing blockage and system failure; the goal is to achieve precise temperature control maintaining stable generator operation within the required temperature window for consistent cooling performance.

Solution directions generated for this problem

Problem Direction 1 :

ImproveTemperature control precision
VS
ConstraintControl system complexity

Inspiration 1 : Cross-domain reference

Application Principle: #26 Copying
Cross-domain applicability Assess applicability
Fat tree adaptive routing
Innovative Solution Refine solution

Thermal model-driven virtual sensor network for generator temperature control

Virtual sensor network via thermal model
How to solve :
  • Deploy 3 physical RTD sensors at generator inlet, mid-depth, and outlet
  • build computational thermal model using finite-difference heat transfer equations calibrated with initial sensor data to generate 8 virtual temperature points across solution volume
  • Use hash-based spatial indexing to map virtual sensor outputs to heat input zones, enabling precise ±1-2°C control with simple lookup table instead of complex multi-sensor fusion algorithms
Expected Effect : Control precision ±1.5°C; sensor count reduced 60%; algorithm complexity reduced 70%; response time <15s
Risk Control :
  • thermal model drift over time
  • calibration accuracy under varying loads
  • computational resource availability

Problem Direction 2 :

ImproveTemperature control precision
VS
ConstraintControl energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #19 Periodic action
Cross-domain applicability Assess applicability
Application control methods, devices, storage media and electronic devices
Innovative Solution Refine solution

Burst-mode precision temperature control with thermal inertia optimization

Optimize thermal inertia for passive stability
How to solve :
  • Implement 60-second burst-mode measurement cycles: activate high-precision RTD sensors (±0.1°C) for 3-second windows, then switch to low-power thermistor monitoring (±1°C) consuming 15 mW between bursts, reducing average sensor power by 75%
  • Apply rapid corrective heat pulses (200-300 W for 5-10 seconds) when deviation exceeds ±1°C threshold detected during burst windows, then rely on thermal mass to maintain stability, eliminating continuous small adjustments that consume 40-60% more actuator energy
  • Increase generator solution thermal mass by 35% through adding high-heat-capacity ceramic beads (specific heat ≥800 J/kg·K, 10-15% volume fraction) to extend temperature decay time constant from 90s to 145s, enabling longer intervals between active corrections while maintaining ±1-2°C precision
Expected Effect : Control energy -65%, precision ±1.5°C, actuator cycles -70%
Risk Control :
  • Ceramic bead sedimentation affecting flow
  • burst timing synchronization failure
  • thermal mass addition increasing startup time

Problem Direction 3 :

ImproveTemperature measurement accuracy
VS
ConstraintControl system complexity

Inspiration 1 : Cross-domain reference

Application Principle: #26 Copying
Cross-domain applicability Assess applicability
Fat tree adaptive routing
Innovative Solution Refine solution

Thermal model-based virtual sensor array for generator temperature estimation

Replace dense physical sensor arrays with computational thermal model
How to solve :
  • Deploy only 2-3 physical RTD sensors at strategic locations (inlet, outlet, mid-depth) instead of 8-10 sensors
  • build a lumped-parameter thermal model of the generator solution using heat balance equations (Q_in - Q_out = mC_p·dT/dt) calibrated with initial sensor data to estimate temperature distribution across unmeasured zones
  • implement model-based virtual sensors that compute temperature at 6-8 virtual points using real sensor inputs and solution flow rate, achieving ±1-2°C accuracy with 60-70% fewer physical sensors, signal conditioning circuits, and wiring
Expected Effect : Measurement accuracy ±1.5°C; physical sensors reduced 65%; system complexity reduced 50%
Risk Control :
  • model calibration drift over time
  • thermal property variation with concentration
  • computational load on existing controller

Problem Direction 4 :

ImproveTemperature measurement accuracy
VS
ConstraintControl energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #32 Color changes
Cross-domain applicability Assess applicability
Intelligent electronic shoe system
Innovative Solution Refine solution

Thermochromic liquid crystal film temperature measurement system for absorption generator

Passive optical temperature sensing eliminates continuous sensor power
How to solve :
  • Apply thermochromic liquid crystal (TLC) film (15–25 μm thickness) to generator vessel inner wall, calibrated to change color at critical thresholds: 85°C (crystallization onset, red), 75°C (optimal range, green), 65°C (inadequate vaporization, blue)
  • use low-power photodetector array (3 RGB sensors at 120° spacing) operating in 2-second measurement bursts every 45 seconds (duty cycle 4.4%), consuming ≤0.8 W average vs. ≥12 W for continuous RTD bridge circuits
  • passive TLC film requires zero excitation power between measurements, achieving 0.5°C effective resolution through color hue interpolation algorithms
  • install reference color patches (stable pigments at known temperatures) adjacent to TLC zones for drift compensation
  • quality control: TLC color transition temperature tolerance ±0.3°C verified by calibrated thermal bath, photodetector spectral response validated against standard light sources (CIE illuminant D65), hue-to-temperature lookup table accuracy ±0.4°C confirmed across 60–90°C range
  • implementation steps: (1) surface preparation—degrease vessel interior with isopropanol, roughness Ra ≤0.8 μm
  • (2) TLC film application—spray coating in controlled humidity (40–60% RH) at 20–25°C, cure 24 hours
  • (3) photodetector mounting—seal in quartz windows, optical path length 50 mm, viewing angle ±15°
  • (4) calibration—map hue values at 1°C intervals using precision heater
  • mainstream RTD systems consume 12–18 W continuously for ±0.5°C accuracy
  • this solution achieves ±0.5°C accuracy with 93% energy reduction (0.8 W average), eliminating active sensor excitation while maintaining measurement precision through passive optical phenomena and intermittent low-power detection.
Expected Effect : Measurement accuracy ±0.5°C; control energy 0.8 W (93% reduction); duty cycle 4.4%
Risk Control :
  • TLC film aging and color drift over time
  • ambient light interference affecting photodetector readings
  • thermal hysteresis in liquid crystal phase transitions

Problem Direction 5 :

ImproveTemperature stability duration
VS
ConstraintControl system complexity

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
Automatic push-out to avoid range of motion limits
Innovative Solution Refine solution

Adaptive thermal mass modulation for passive temperature stabilization

Modulate generator thermal mass to stabilize temperature passively
How to solve :
  • Install variable thermal mass module using phase-change material (PCM) with melting point at optimal generator temperature (85–95°C)
  • select paraffin wax or salt hydrate with latent heat ≥200 kJ/kg, encapsulated in 0.6mm aluminum shells surrounding generator vessel in 15–25% volume ratio
  • During temperature rise, PCM absorbs excess heat through phase transition without temperature increase
  • during cooling, PCM releases stored latent heat maintaining solution temperature — achieving ±1.5°C stability for 3–5× longer duration without active control intervention
  • Implement modular PCM cartridge design allowing seasonal adjustment: higher melting-point PCM (95°C) for summer, lower (85°C) for winter — simple manual swap without control algorithm modification, maintaining passive stabilization across operating conditions
Expected Effect : Stability duration +300–400%; control complexity unchanged; energy consumption −35%
Risk Control :
  • PCM encapsulation leakage over thermal cycles
  • thermal conductivity mismatch causing delayed response
  • PCM degradation after 500–1000 cycles reducing capacity

Problem Direction 6 :

ImproveTemperature stability duration
VS
ConstraintControl energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
A method for preparing room temperature non-hydrogenated vegetable fat cream
Innovative Solution Refine solution

Phase-change material thermal buffer jacket for passive temperature stabilization

Integrate PCM jacket to passively stabilize temperature
How to solve :
  • Install phase-change material (PCM) jacket around generator vessel using paraffin wax or salt hydrate with melting point matching optimal operating temperature (85–95°C)
  • jacket thickness 15–25mm, thermal storage capacity ≥200 kJ/kg
  • During heat load fluctuations, PCM absorbs excess heat by melting or releases heat by solidifying, maintaining solution temperature within ±1.5°C for 60–90 minutes without active control intervention
  • Use aluminum foam matrix (porosity 85–92%, thermal conductivity ≥200 W/(m·K)) to encapsulate PCM, preventing leakage and enhancing heat transfer uniformity across generator surface
Expected Effect : Stability duration +70–90%, control energy −40–55%, ±1.5°C precision maintained
Risk Control :
  • PCM thermal cycling degradation after 500–1000 cycles
  • encapsulation integrity failure causing leakage
  • non-uniform PCM distribution reducing local buffering effectiveness
Patsnap Eureka Solution