The invention relates to the technical field of
chemical production process control, and particularly discloses a production process export quality prediction method based on performance-driven knowledge
distillation, which comprises the following steps: step 1, acquiring
observable process data of an industrial production process and
test data of export quality indexes, and forming a
data set; 2, cleaning and standardizing the
data set, and randomly dividing the
data set into a
training set, a
verification set and a
test set according to a certain proportion; 3, building a basic quality predictor, and embedding an LSTM module into the basic quality predictor through a twinning neural network framework; 4, training a plurality of basic twin LSTM quality predictors to obtain a teacher model; step 5, training student models with the same structure by taking performance as drive and combining knowledge
distillation and
supervised learning; and step 6, inputting process data of the
test sample, and obtaining an output predicted value, so as to improve the accuracy of the soft measurement of the
production quality index in the industrial process on the basis of ensuring the stability of the model.