Performance of Model-based Predictive Control of the Ventilation Rate with Axial Fans

Abstract

A new algorithm for ventilation rate control in mechanically ventilated buildings was developed and evaluated on a laboratory test installation. It was demonstrated that the current problems with traditional control of axial fans, such as instability due to dynamic wind actions, could be overcome by using a model-based predictive control (MBPC) algorithm. In the laboratory test installation, the performance of the MBPC algorithm was compared to the performance of a traditional proportional, integral, derivative (PID) controller in an airflow range from 1000 to 5000 m3/h (20–100% of fan capacity) and in a normal pressure range from 0 to 60 Pa. The accuracy and the stability of the ventilation rate control and the energy consumption were studied for both control algorithms. It was concluded that the main advantage of a MBPC algorithm over a PID controller lies in its stability in a wider range of the ventilation rates and pressure differences due to an automatic adaptation of the control parameters. The PID algorithm with fixed settings did not generate a stable control of the ventilation rate when the target value deviated more than 500 m3/h (16% of centre capacity) from the ventilation rate that it was optimised for, whereas the MBPC controller remained stable throughout the complete working range of the fan.