Abstract
In this work, three kinds of ANNs are employed to predict the pyrolysis behavior of n-decane. Six-fold cross validation method is adopted to optimize the key parameter of ANNs and evaluate the performance of ANNs. The results indicate that RBFN has best performance in predicting the species distribution and all ANNs have good performance in predicting the bulk temperature. Meanwhile, these results reveal that all ANNs are more suitable for predicting products with high mass fraction than those with less mass fraction, whose errors are also acceptable.
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