Method for operating a fan system and fan system with a reverse curved radial fan
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fan systems with backward-curved centrifugal fans face challenges in precisely controlling volume flow without sensors, due to unclear relationships between motor current and generated volume flow, especially with various external influencing variables.
Innovation Solution
A method utilizing an artificial neural network to regulate the fan system by analyzing motor current, voltage, and fan speed, which learns to account for changes in outlet pressure and volume flow, allowing precise control without flow sensors through recorded and calculated operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If mathematical models or algorithms are used to control the fan based on motor current and operating parameters, then control can be achieved without flow sensors, but the volume flow determination is relatively imprecise due to unclear relationships between motor current and generated volume flow
Solution Approach 1:
The patent introduces an intermediary device (the determination device with neural network or support vector machine) that mediates between the motor current/operating parameters and the volume flow determination. This intermediary learns the complex, non-linear relationships between motor parameters and actual volume flow through training data, providing more accurate predictions than direct mathematical models while still avoiding the need for flow sensors
Solution Approach 2:
The patent changes the approach from using simple mathematical models to using machine learning models (neural networks or support vector machines) that can capture complex parameter relationships. The determination device is trained with multiple datasets containing operating parameters and corresponding volume flow measurements, allowing it to learn and adapt to non-linear relationships between motor current, speed, and actual volume flow
2Measurement precision
If flow sensors are used to measure volume flow for control purposes, then precise volume flow measurement is achieved, but the device complexity and cost increase
Solution Approach 1:
The patent extracts the measurement function from physical flow sensors and relocates it to a determination device that uses machine learning algorithms. Instead of measuring volume flow directly with sensors, the system extracts volume flow information from existing motor operating parameters (current, speed, voltage) through intelligent analysis, thereby eliminating the need for additional flow measurement hardware
Solution Approach 2:
The motor and control system serve dual purposes: they not only drive the fan but also provide the data needed for volume flow determination. The existing sensors that monitor motor operating parameters are repurposed to feed the machine learning model, allowing the system to self-determine volume flow without requiring external flow measurement devices
3Ease of operation
If traditional control methods are used without considering external influencing variables, then control simplicity is maintained, but the ability to regulate volume flow under varying external conditions deteriorates
Solution Approach 1:
The patent performs preliminary action by training the machine learning model with diverse training datasets that include various operating conditions and external influencing variables before actual operation. This pre-training allows the determination device to already know how to compensate for common disturbances and changes in operating conditions, enabling adaptive control without complex real-time calculations
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
The invention relates to a method for operating a fan system (10) and to such a fan system (10). The fan system (10) has a control unit (11) with an artificial neural network (19). The control unit (11) controls a motor (13) of a backward-curved radial fan (12). The radial fan (12) generates a gas flow (G) characterized by a flow rate, in particular the volume flow rate (Q). The flow rate is not detected by a sensor, but is determined by the artificial neural network (19) depending on input variables, and the motor (13) is controlled or regulated by the control unit (11) based on this.The motor current (I) and motor voltage (U) of the motor (13), as well as their temporal behavior, which can be determined by the time derivative (e.g., first-order gradient) or by at least one previous value at an earlier time, are fed to an input layer (30) of the artificial neural network (19). It is particularly advantageous if the artificial neural network (19) determines an output pressure value and feeds this value back to the input layer (30) internally or externally as an input.