Neural network-based open-loop control method for static pressure of laboratory exhaust systems

The variable air volume (VAV) exhaust system in scientific research laboratories has a large operating adjustment range.To address the issues of system oscillation and high energy consumption caused by traditional static pressure control methods in VAV systems,this paper proposes a new control approach for laboratory exhaust systems.The method employs an artificial neural network (ANN) to predict optimal fan operating frequencies,thereby reducing energy consumption while avoiding oscillations inherent in closed-loop control.Taking a laboratory on the third floor of a scientific research experimental building as a case study,CFD simulations and experimental tests are conducted to obtain optimal fan static pressure values under partial operating conditions,forming the training dataset.A back propagation (BP) neural network model is utilized to develop the control model.Verified by CFD and physical experiments,the error between the predicted value and the simulated optimal value remains within 2.08%.Compared with conventional constant static pressure control algorithms,this method significantly reduces system operating pressure (by a maximum of 142.8 Pa) and achieves up to 23% energy saving.