The invention discloses a waste tire continuous thermal
cracking feeding control method and
system based on
machine learning, and relates to the technical field of
plastic waste recovery. The method comprises the following steps: acquiring a historical rear end socket temperature sequence and a historical
internal pressure sequence of a cracker in a historical time window, and a rotation
angular velocity and feeding rate sequence in a future preset time window; on the basis of the long and short-
term memory network, predicting a predicted end socket temperature sequence and a predicted
internal pressure sequence in a future window; with improvement of temperature, pressure and feeding rate stability as multiple targets, an adaptive feeding rate sequence is searched and output in a feeding rate adjustment space through digital twinborn
simulation and an intelligent optimization
algorithm in combination with the prediction sequence; and finally, carrying out feeding control on the cracker according to the sequence in a preset time window. According to the method, prospective optimization control over the
cracking working condition is achieved, the
hysteresis quality of traditional control is effectively overcome, and the stability, safety and
automation level of the continuous thermal
cracking process are improved.