Cascade control systems and dehumidifier systems for controlling a humidity level
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Solution Overview
Problem
Existing dehumidifier control systems face challenges in achieving optimal humidity control due to the complexity of environmental factors, requiring time-consuming and subjective tuning of PID parameters, and often result in suboptimal performance.
Innovation Solution
A modified cascade control system incorporating a model predictive control (MPC) module with machine learning algorithms to predict future humidity levels and adjust temperature setpoints, integrating sensor data for improved control precision and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PID controllers are used for humidity control, then the system structure is simple, but the control precision and response speed are insufficient due to time-consuming and subjective parameter tuning
Solution Approach 1:
The patent transforms the control approach from traditional PID parameter tuning to model predictive control by changing the fundamental control parameters and mathematical models used. The MPC module uses predictive algorithms and optimization techniques to determine control actions, replacing the empirical PID tuning process and achieving superior control precision without manual intervention.
Solution Approach 2:
The patent replaces the mechanical tuning process of PID controllers with an automated computational system. The MPC module uses machine learning algorithms and predictive modeling to automatically adjust control parameters based on system state and environmental conditions, eliminating the need for manual parameter adjustment and expert intervention.
2Reliability
If PID parameters are manually tuned to improve control performance, then control precision may be improved, but the tuning process is time-consuming and subjective
Solution Approach 1:
The MPC module operates autonomously without requiring manual parameter tuning or expert intervention. It continuously monitors system state, predicts future behavior using its internal model, and automatically adjusts control actions to maintain optimal performance. This self-service capability eliminates the time-consuming manual tuning process while ensuring reliable control.
Solution Approach 2:
The system implements continuous feedback loops where the MPC module monitors actual humidity levels, compares them with target values, and adjusts control actions in real-time. This closed-loop feedback mechanism ensures high reliability by automatically compensating for disturbances and maintaining optimal control without manual intervention.
3Stability of the object's composition
If a cascade control system is used to improve control stability, then humidity control stability improves, but the system complexity increases
Solution Approach 1:
The patent segments the control system into distinct functional modules: the MPC module for predictive control, the PID controller for execution, and the heater control system. This modular segmentation allows each component to perform its specific function efficiently while maintaining overall system stability through coordinated operation of the segmented parts.
4Speed
If traditional control systems are used, then the system architecture is simple, but the response speed to humidity changes is slow
Solution Approach 1:
The MPC module performs preliminary actions by predicting future humidity levels and preemptively adjusting control actions before deviations occur. It uses its predictive model to anticipate system behavior and environmental changes, taking proactive control measures that accelerate response speed and prevent humidity excursions before they happen.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The MPC module enables faster and more stable humidity control with reduced fluctuations, simplifies system architecture, and optimizes energy efficiency by addressing complex interdependencies.
Implementation Method 1
a heater for adjusting the humidity level of the outlet process air by heating the inlet reactivation air
Implementation Method 2
The desiccant rotor comprises a desiccant material. The dehumidifier is arranged to guide a process air stream and a reactivation air stream across separate axially extending channels through the desiccant material
Data Source
AI summary
A cascade control system of a dehumidifier for controlling a humidity level including: a heater for adjusting the humidity level of the outlet process air by heating inlet reactivation air, a primary control module for controlling the humidity level by controlling the dehumidifier, and a secondary control module for controlling the temperature of inlet reactivation air to match a desired temperature setpoint by controlling the heater. The primary control module is configured to regulate the humidity level to match a desired humidity setpoint while providing the desired temperature setpoint for the secondary control module as output. The primary controller is a Model Predictive Control, MPC, module configured to predict future humidity levels and to calculate the desired temperature setpoint for the secondary control module based on the predicted humidity levels.