Drying paddy with low-pressure superheated steam(LPSS)can effectively increase theγ-aminobutyric acid content in paddy.This study aimed to investigate the characteristics and mathematical models(MMs)of thin-layer dry...Drying paddy with low-pressure superheated steam(LPSS)can effectively increase theγ-aminobutyric acid content in paddy.This study aimed to investigate the characteristics and mathematical models(MMs)of thin-layer drying of paddy with LPSS.The experimentally obtained data werefitted by nonlinear regression with 5 MMs commonly used for thin-layer drying to calculate the goodness of fit of the MMs.Then,the thin-layer drying of paddy with LPSS was modeled with two machine learning methods as a Bayesian regularization back propagation(BRBP)neural network and a support vector machine(SVM).The results showed that paddy drying with LPSS is a reduced-rate drying process.The drying temperature and operating pressure have a significant impact on the drying process.Under the same pressure,increasing the drying temperature can accelerate the drying rate.Under the same temperature,increasing the operating pressure can accelerate the drying rate.The comparison of the model evaluation indexes showed that 5 common empirical MMs(Hederson and Pabis,Page,Midilli,Logarithmic,and Lewis)for thin-layer drying can achieve excellent fitting effects for a single experimental condition.However,the regression fitting of the indexes by calculating the coefficient(s)of each model showed that the empirical MMs produce poor fitting effects.The BRBP neural network-based model was slightly better than the SVM-based model,and both were significantly better than the empirical MM(the Henderson and Pabis model),as evidenced by a comparison of the training root mean square error(RMSE),testing RMSE,training mean absolute error(MAE),testing MAE,training R2,and testing R2 of the Henderson and Pabis model,the BRBP neural network model,and the SVM-based model.This results indicate that the MMs established by the two machine learning methods can better predict the moisture content changes in the paddy samples dried by LPSS.展开更多
文摘Drying paddy with low-pressure superheated steam(LPSS)can effectively increase theγ-aminobutyric acid content in paddy.This study aimed to investigate the characteristics and mathematical models(MMs)of thin-layer drying of paddy with LPSS.The experimentally obtained data werefitted by nonlinear regression with 5 MMs commonly used for thin-layer drying to calculate the goodness of fit of the MMs.Then,the thin-layer drying of paddy with LPSS was modeled with two machine learning methods as a Bayesian regularization back propagation(BRBP)neural network and a support vector machine(SVM).The results showed that paddy drying with LPSS is a reduced-rate drying process.The drying temperature and operating pressure have a significant impact on the drying process.Under the same pressure,increasing the drying temperature can accelerate the drying rate.Under the same temperature,increasing the operating pressure can accelerate the drying rate.The comparison of the model evaluation indexes showed that 5 common empirical MMs(Hederson and Pabis,Page,Midilli,Logarithmic,and Lewis)for thin-layer drying can achieve excellent fitting effects for a single experimental condition.However,the regression fitting of the indexes by calculating the coefficient(s)of each model showed that the empirical MMs produce poor fitting effects.The BRBP neural network-based model was slightly better than the SVM-based model,and both were significantly better than the empirical MM(the Henderson and Pabis model),as evidenced by a comparison of the training root mean square error(RMSE),testing RMSE,training mean absolute error(MAE),testing MAE,training R2,and testing R2 of the Henderson and Pabis model,the BRBP neural network model,and the SVM-based model.This results indicate that the MMs established by the two machine learning methods can better predict the moisture content changes in the paddy samples dried by LPSS.
