The application discloses a mechanism-guided
cascade reactor dynamic modeling and transfer learning method and device, and belongs to the technical field of
chemical engineering and
artificial intelligence, and comprises the following steps:
residence time and input variables are taken as inputs of a reactor inlet, are processed through a
reaction rate learning module, and finally the remaining reactant concentrations of different reaction microelements after corresponding
residence time are obtained; flow and
residence time are taken as input parameters and are input into a
residence time distribution learning module; the module processes the parameters, and outputs the
residence time probability distribution under the conditions of corresponding flow and
residence time; the remaining reactant concentrations output by the
reaction rate learning module and the corresponding probability distribution output by the
residence time distribution learning module are subjected to weighted summation through a reactor output module, and finally the reactant concentration at the reactor outlet is obtained.