This application provides a method for predicting the conversion rate and self-correcting parameters of
polymerization reactions in a coaxial dual-stirred reactor, relating to the fields of intelligent manufacturing and
chemical process control. The method includes: synchronously acquiring real-time torque and current components of the inner and outer shaft drive motors of the reactor, real-time speed of the stirrer, and real-time material temperature; preprocessing the acquired signals; performing a cross-product operation on the preprocessed current and speed, obtaining a dimensionless
characteristic ratio after geometric normalization, and collecting its average value during the
induction period as a benchmark value; calculating the cumulative deviation of the dimensionless
characteristic ratio relative to the benchmark value, taking the moment when the inner shaft current first shows a sustained upward trend as the starting point of the
reaction timing; calculating the
diffusion limitation coefficient based on the cumulative deviation and the
diffusion sensitivity coefficient, wherein the
diffusion sensitivity coefficient is adaptively calibrated by the average cumulative deviation within the initial linear interval and the target initial diffusion limitation coefficient; calculating the predicted conversion rate of the
polymerization reaction based on the real-time temperature and the diffusion limitation coefficient; and controlling the termination time of the
polymerization reaction according to the predicted conversion rate. This invention utilizes existing motor signals from the reactor to achieve online prediction of the polymerization reaction conversion rate, offering advantages such as strong anti-interference capability and adaptability to batch differences.