Predicting Combined Solar and Conventional Drying of Wood with an Algorithm Based on Support Vector Regression and Particle Swarm Optimization

Abstract

Accurate prediction of wood-drying rates is essential for enhancing drying efficiency and product quality; however, reliable models remain scarce, particularly for solar drying systems. In this study, poplar wood was dried in a solar kiln, and moisture content was determined through periodic mass measurements. The drying rate was calculated from the moisture content reduction over time, while kiln temperature and relative humidity were continuously monitored using calibrated sensors. Experimental results showed that poplar wood with an initial moisture content of 73.2% required approximately 130 h to reach a final moisture content of 7.5% under solar drying conditions. Based on an analysis of key physical factors affecting the drying process, kiln temperature, relative humidity, and wood moisture content were selected as input variables, with the drying rate as the output variable. Several optimization algorithms were evaluated, and a particle swarm optimization–support vector regression (PSO-SVR) model demonstrated the best performance. The optimized model achieved a mean squared error of 0.00105 and a mean absolute error of 0.039, indicating high prediction accuracy. In addition, the training time was reduced to 0.275 s, reflecting excellent computational efficiency. These results confirm that the PSO-optimized SVR model can be both accurate and efficient, i.e., practical industrial wood-drying applications.