รายละเอียดเอกสาร

The near-infrared (NIR) sensor can be used for measuring the basis weight and moisture of tissue paper, but the measurement accuracy is not ideal for this paper grade. The weight range of the tissue is 10~30 g/m2, indicating that it is a low gram weight paper. The temperature and humidity of the production environment significantly impact an NIR sensor. This paper focuses on improving the measurement accuracy of tissue paper basis weight. In order to reduce the influences of temperature and humidity, a mathematical model based on a particle swarm optimization back propagation (PSO-BP) neural network is proposed. In comparison with multiple linear regression measurement models, the basis weight measurement error with the PSO-BP model is within ± 0.5 g/m2. This model can effectively improve the measurement accuracy and has a good effect on overcoming the basis weight nonlinear effect caused by the changes in ambient temperature and humidity.

Application: The PSO-BP neural network model can eliminate the nonlinear effects of temperature and humidity on basis weight, which provides a new idea for online measurement of paper basis weight for mills in the future.