Sulfur flotation process is a typical industry process with complex dynamics. For a sulfur flotation cell, the structure of the system model could be derived using first-principles and reaction kinetics. However, the model parameters cannot be obtained under certain working conditions. In this paper, by using adaptive dynamic programming (ADP), we establish a data-driven optimal control approach for the operation of a sulfur flotation cell without knowing the model parameters. By learning from the online production data, an initial admissible control policy iteratively converges to an approximated optimal control law, and the dependence of optimal control design on the full model knowledge is eliminated. A simulation environment of sulfur flotation process is constructed based on phenomenological model and industrial data. Some practical problems in the implementation of ADP, i.e., selection of basis functions, how to use the model structural information in the ADP-based control design, are investigated. The feasibility and performance of the proposed data-driven optimal control are tested in the simulation environment. The results indicate the potential of applying bioinspired control methods in flotation process.
Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design
