Air Conditioning Room Temperature and Humidity Control (Temperature Control Model)

The air conditioning room temperature and humidity control system is a core component in modern intelligent buildings, with its primary objective being to maintain indoor environmental parameter stability by coordinating multiple subsystems (cooling, heating, humidification). The following analysis from a modeling perspective examines the design logic of its core modules:

Room Dynamic Model The room as the controlled object follows the first law of thermodynamics and mass conservation law for temperature and humidity changes. The temperature model must account for disturbance factors such as wall heat storage, human body heat dissipation, and equipment heat generation, typically expressed using first-order inertial elements. The humidity model requires calculation of the coupling relationship between water vapor content in air and dew point temperature, often described using differential equations to represent dynamic energy and mass balance.

Evaporator Cooling Model The core of the cooling system operates on the phase-change heat absorption principle to reduce air temperature, with its dynamic characteristics influenced by refrigerant flow rate, evaporation temperature, and fin heat exchange efficiency. Modeling must include nonlinear components – when evaporator surface temperature falls below the air dew point temperature, dehumidification effects occur, creating strong coupling between temperature and humidity control that requires decoupling algorithms. Implementation would typically involve state-space equations with conditional statements checking for dew point conditions.

Heater and Humidifier Models The electric heater model is relatively simple and can be treated as a first-order system with pure delay. The steam humidifier model must consider the interaction between water molecule diffusion rate and air flow. These two actuators exhibit significant response speed differences (heating faster than humidification), requiring layered control strategies in timing sequences. Code implementation would use different time constants and delay parameters for each actuator model.

Control Strategy Implementation Key Points Multivariable PID Control: Design decoupling compensators addressing temperature-humidity coupling characteristics Feedforward Compensation: Predictive adjustment for sudden disturbances like door/window openings Energy Consumption Optimization: Dynamically adjust equipment start-stop thresholds based on Model Predictive Control (MPC) Algorithm implementation would involve matrix operations for decoupling controllers and optimization solvers for MPC calculations.

System simulation verification typically requires parameter identification combined with measured data, such as determining room time constants through step response tests. Practical deployment must also consider how sensor placement locations affect feedback signal authenticity. Simulation code would include parameter estimation routines and sensor noise models.