This invention belongs to the field of waste plastic
pyrolysis resource utilization, specifically a
machine learning-based
process optimization method for preparing light aromatics from waste plastic
pyrolysis. This method constructs a dataset based on
raw material characteristics,
pyrolysis conditions, and
product data from published literature, performs preprocessing, trains and optimizes various
machine learning regression models, and selects the optimal model for predicting light aromatics yield based on evaluation indicators. Then,
interpretability analysis is used to identify
key factors affecting the yield, and
raw material structural characteristics such as saturated C-H bonds, methyl branches, and carbonyl groups are used as inputs to enhance the characterization of
raw material differences. Under industrial constraints, with the light aromatics yield as the optimization objective, a
particle swarm optimization algorithm is used to globally optimize the reaction condition parameters to obtain the
optimal combination of operating parameters. This invention can achieve light aromatics yield prediction and operating parameter recommendation under fluctuating raw material composition, improving prediction stability and generalization ability, reducing experimental iterations, and lowering optimization costs.