Control of thermal uniformity in microwave heating process by BPNN and adaptive particle swarm optimization

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive Particle Swarm Optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of Particle Swarm Optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature closely follows the preset temperature curve. Experimental results demonstrate that the actual output value of the system closely aligns with the original preset temperature curve, with a root mean square error of only 0.74. This designed identification and control system effectively achieves the desired outcome.