Abstract:

Aiming at the nonlinear correlation and time-delay of microwave heating process, a microwave-heated lignite temperature prediction method based on wavelet denoising optimized by thresholds through correlation principles and support vector machine optimized by improved particle swarm optimization is proposed. The temperature for microwave heating of lignite is acquired via wavelets with an optimized threshold based on relevant principles. The particle swarm optimization algorithm is improved by introducing the similarity function and the control parameter α. It is used to optimize the penalty coefficient C, the insensitive loss function parameter ε and the kernel function parameter g of the microwave heating lignite temperature support vector machine prediction model parameters. As the similarity function increases, the random variability of the particles increases; the number of iterations increases, and the control parameter α decreases. The simulation results show that the optimized support vector machine regression prediction model improves the accuracy of microwave heating lignite temperature prediction, and provides a mathematical model for the study of microwave drying lignite control method.