PO-BP neural network-based temperature prediction for tobacco drying machines

In the cut-tobacco drier production line, precise temperature control of the thin-plate roaster is essential for ensuring product quality and operational safety. However, existing temperature prediction models exhibit limited prediction accuracy, resulting in unstable product quality and potential safety risks. To address this challenge, a temperature prediction model for the thin-plate roaster is proposed, integrating a back-propagation (BP) neural network optimized by the parrot optimization algorithm (PO). Production data from a tobacco factory were collected, focusing on seven key operational parameters: IP4 steam film valve opening, process gas velocity, process gas temperature, process gas pressure, condensate water temperature, dehumidification air volume, and hood pressure. A BP neural network was constructed using these features as input variables. To enhance model performance, the PO algorithm was employed to optimize the initial weights and thresholds of the BP network. The proposed PO-BP model was systematically compared with traditional optimization algorithms, including particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Experimental results demonstrate that the PO-BP model achieves superior generalization ability and prediction accuracy. Specifically, the coefficient of determination ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> ) reaches 0.998, and the root mean square error (RMSE) is reduced to 0.001. These metrics indicate that the PO-BP model effectively mitigates the BP network’s tendency to converge to local minima, while simultaneously improving global search capability and convergence efficiency. Consequently, the proposed model provides a robust and accurate solution for thin-plate temperature prediction in industrial roasting processes.