Serious problem on the heating uniformity exist in the large-volume electric oven

which limits its extensive application in commercial and household fields. The commonly applied Proportion Integration Differentiation(PID)algorithm has concerns of long relaxation time and poor temperature control accuracy. This study involves a self-coding backpropagation neural network(BPNN)control strategy

aiming to improve the heating efficiency

temperature control accuracy and uniformity. The local velocity and temperature measurement as well as the egg-tart visualization methods are utilized to assess the control sensitivity of fan speed

power of airflow heating rods

power of radiation heating rods

and exhaust flowrate. Experimental result shows that

following effective data training and robustness enhancement

the BPNN control strategy can significantly reduce the prediction errors. In comparison to the PID strategy

the overheating is reduced by up to 6 ℃. Meanwhile

the maximum temperature difference decreases from 54% to 36%. Accordingly

the velocity difference drops from 71.4% to 39%

and the relaxation time shorts from 230 seconds to 100 seconds. It is indicated that the BPNN strategy can provide much quicker

more precise and uniform temperature control in the large-volume electric oven.