Dynamic optimal algorithm for batch processes based on nonlinear programming

The log barrier method and external point method are used to deal with the constraints of actual batch process and transform the constrained optimization problem into the unconstrained optimization problem. Using control vector parameterization of nonlinear programming algorithm, the optimal profiles of manipulated variables are approximated by a set of algebraic equations, and then the optimal control problem with infinite dimensions is converted into an optimal estimation problem with finite dimensions using a trial function with unspecified coefficients. A dynamic iterative optimization algorithm is designed based on Lyapunov theory and the effect of sampling time on the convergence of optimization algorithm is discussed emphatically. At last, the dynamic optimal algorithm is used to solve the optimal distribution of process parameters in alkali fusion batch reaction process, which can increase the ultimate yield of target products and reduce production costs. Simulation experiment was carried out, and the results show the effectiveness and reliability of the proposed algorithm.