The invention belongs to the field of
energy consumption optimization, and particularly relates to a two-stage electrolytic
copper foil electrodeposition
energy consumption optimization method based on federal learning. The method comprises the steps that each
client side collects multi-dimensional technological parameters in the electrolytic
copper foil electro-
deposition process, and technological parameter vectors are constructed; constructing a federated optimization framework, training a local
energy consumption predictor by each
client in a local autonomous optimization stage, and calling an intelligent optimization
algorithm for optimization; in the federated collaborative optimization stage, each
client updates a local energy consumption predictor based on the structure parameters of the
global energy consumption predictor generated by the
server, and calls an intelligent optimization
algorithm to perform collaborative optimization; and based on the latest local energy consumption predictor, carrying out
backtracking evaluation on the optimal process parameter vector set, and screening out the process parameter vector which has the lowest predicted energy consumption and is finally applied to
production control. According to the method, flexible support for various intelligent optimizers is realized, and generalization, stability and reliability of the model in a real industrial scene are improved.